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Redirect to opensearch.org/docs for all requests

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Signed-off-by: Miki <mehranb@amazon.com>
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Miki 2021-07-12 14:59:12 -07:00
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.DS_Store
Gemfile.lock
.idea
.jekyll-cache

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<!DOCTYPE html>
<html lang="en-US">
<meta charset="utf-8">
<title>Redirecting&hellip;</title>
<script>location="https://opensearch.org/docs"+location.pathname</script>
<meta name="robots" content="noindex">
<h1>Redirecting&hellip;</h1>
<a href="https://opensearch.org/docs">Click here if you are not redirected.</a>
</html>

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---
permalink: /404.html
---

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CODE_OF_CONDUCT.md
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## Code of Conduct
This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).
For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact
opensource-codeofconduct@amazon.com with any additional questions or comments.

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CONTRIBUTING.md
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# Contributing Guidelines
Thank you for your interest in contributing to our project. Whether it's a bug report, new feature, correction, or additional
documentation, we greatly value feedback and contributions from our community.
Please read through this document before submitting any issues or pull requests to ensure we have all the necessary
information to effectively respond to your bug report or contribution.
## Reporting Bugs/Feature Requests
We welcome you to use the GitHub issue tracker to report bugs or suggest features.
When filing an issue, please check existing open, or recently closed, issues to make sure somebody else hasn't already
reported the issue. Please try to include as much information as you can. Details like these are incredibly useful:
* A reproducible test case or series of steps
* The version of our code being used
* Any modifications you've made relevant to the bug
* Anything unusual about your environment or deployment
## Contributing via Pull Requests
Contributions via pull requests are much appreciated. Before sending us a pull request, please ensure that:
1. You are working against the latest source on the *main* branch.
2. You check existing open, and recently merged, pull requests to make sure someone else hasn't addressed the problem already.
3. You open an issue to discuss any significant work - we would hate for your time to be wasted.
To send us a pull request, please:
1. Fork the repository.
2. Modify the source; please focus on the specific change you are contributing. If you also reformat all the code, it will be hard for us to focus on your change.
3. Ensure local tests pass.
4. Commit to your fork using clear commit messages.
5. Send us a pull request, answering any default questions in the pull request interface.
6. Pay attention to any automated CI failures reported in the pull request, and stay involved in the conversation.
GitHub provides additional document on [forking a repository](https://help.github.com/articles/fork-a-repo/) and
[creating a pull request](https://help.github.com/articles/creating-a-pull-request/).
## Finding contributions to work on
Looking at the existing issues is a great way to find something to contribute on. As our projects, by default, use the default GitHub issue labels (enhancement/bug/duplicate/help wanted/invalid/question/wontfix), looking at any 'help wanted' issues is a great place to start.
## Code of Conduct
This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).
For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact
opensource-codeofconduct@amazon.com with any additional questions or comments.
## Security issue notifications
If you discover a potential security issue in this project we ask that you notify AWS/Amazon Security via our [vulnerability reporting page](http://aws.amazon.com/security/vulnerability-reporting/). Please do **not** create a public github issue.
## Licensing
See the [LICENSE](LICENSE) file for our project's licensing. We will ask you to confirm the licensing of your contribution.

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Gemfile
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source "https://rubygems.org"
# Hello! This is where you manage which Jekyll version is used to run.
# When you want to use a different version, change it below, save the
# file and run `bundle install`. Run Jekyll with `bundle exec`, like so:
#
# bundle exec jekyll serve
#
# This will help ensure the proper Jekyll version is running.
# Happy Jekylling!
# gem "jekyll", "~> 3.9.0"
# This is the default theme for new Jekyll sites. You may change this to anything you like.
gem "just-the-docs", "~> 0.3.3"
# If you want to use GitHub Pages, remove the "gem "jekyll"" above and
# uncomment the line below. To upgrade, run `bundle update github-pages`.
gem 'github-pages', group: :jekyll_plugins
# If you have any plugins, put them here!
# group :jekyll_plugins do
# # gem "jekyll-feed", "~> 0.6"
# gem "jekyll-remote-theme"
# gem "jekyll-redirect-from"
# end
# Windows does not include zoneinfo files, so bundle the tzinfo-data gem
gem "tzinfo-data", platforms: [:mingw, :mswin, :x64_mingw, :jruby]
# Performance-booster for watching directories on Windows
gem "wdm", "~> 0.1.0" if Gem.win_platform?

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_clients/agents-and-ingestion-tools/index.md
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---
layout: default
title: Agents and ingestion tools
nav_order: 100
has_children: false
has_toc: false
redirect_from:
- /clients/agents-and-ingestion-tools/
---
# Agents and ingestion tools
Historically, many multiple popular agents and ingestion tools have worked with Elasticsearch OSS, such as Beats, Logstash, Fluentd, FluentBit, and OpenTelemetry. OpenSearch aims to continue to support a broad set of agents and ingestion tools, but not all have been tested or have explicitly added OpenSearch compatibility.
As an intermediate compatibility solution, OpenSearch has a setting that instructs the cluster to return version 7.10.2 rather than its actual version.
If you use clients that include a version check, such as recent versions of Logstash OSS or Filebeat OSS, enable the setting:
```json
PUT _cluster/settings
{
"persistent": {
"compatibility": {
"override_main_response_version": true
}
}
}
```
For a longer term solution, we plan to create an OpenSearch output plugin for Logstash. This plugin *does not exist yet*, but we've included it in the compatibility matrices below based on its expected behavior.
## Downloads
These versions of Logstash and Beats offer the best compatibility with OpenSearch. For more information, see the [compatibility matrices](#compatibility-matrices).
- [Logstash OSS 7.12.1](https://www.elastic.co/downloads/past-releases/logstash-oss-7-12-1)
- [Filebeat OSS 7.12.1](https://www.elastic.co/downloads/past-releases/filebeat-oss-7-12-1)
- [Metricbeat OSS 7.12.1](https://www.elastic.co/downloads/past-releases/metricbeat-oss-7-12-1)
- [Packetbeat OSS 7.12.1](https://www.elastic.co/downloads/past-releases/packetbeat-oss-7-12-1)
- [Heartbeat OSS 7.12.1](https://elastic.co/downloads/past-releases/heartbeat-oss-7-12-1)
- [Winlogbeat OSS 7.12.1](https://www.elastic.co/downloads/past-releases/winlogbeat-oss-7-12-1)
- [Auditbeat OSS 7.12.1](https://elastic.co/downloads/past-releases/auditbeat-oss-7-12-1)
## Compatibility Matrices
*Italicized* cells are untested, but indicate what a value theoretically should be based on existing information.
### Compatibility Matrix for Logstash
| | Logstash OSS 7.x to 7.11.x | Logstash OSS 7.12.x\* | Logstash 7.13.x without OpenSearch output plugin | Logstash 7.13.x with OpenSearch output plugin\*\* |
| :---| :--- | :--- | :--- | :--- |
| Elasticsearch OSS v7.x to v7.9.x | *Yes* | *Yes* | *No* | *Yes* |
| Elasticsearch OSS v7.10.2 | *Yes* | *Yes* | *No* | *Yes* |
| ODFE OSS v1.x to 1.12 | *Yes* | *Yes* | *No* | *Yes* |
| ODFE 1.13 | *Yes* | *Yes* | *No* | *Yes* |
| OpenSearch 1.0 | [Yes via version setting](https://github.com/opensearch-project/OpenSearch/issues/693) | [Yes via version setting](https://github.com/opensearch-project/OpenSearch/issues/693) | *No* | *Yes* |
\* Most current compatible version with Elasticsearch OSS.
\*\* Planning to build.
### Compatibility Matrix for Beats
| | Beats OSS 7.x to 7.11.x\*\* | Beats OSS 7.12.x\* | Beats 7.13.x |
| :--- | :--- | :--- | :--- |
| Elasticsearch OSS v7.x to v7.9.x | *Yes* | *Yes* | No |
| Elasticsearch OSS v7.10.2 | *Yes* | *Yes* | No |
| ODFE OSS v1.x to 1.12 | *Yes* | *Yes* | No |
| ODFE 1.13 | *Yes* | *Yes* | No |
| OpenSearch 1.0 | [Yes via version setting](https://github.com/opensearch-project/OpenSearch/issues/693) | [Yes via version setting](https://github.com/opensearch-project/OpenSearch/issues/693) | No |
| Logstash OSS 7.x to 7.11.x | *Yes* | *Yes* | *Yes* |
| Logstash OSS 7.12.x\* | *Yes* | *Yes* | *Yes* |
| Logstash 7.13.x with OpenSearch output plugin | *Yes* | *Yes* | *Yes* |
\* Most current compatible version with Elasticsearch OSS.
\*\* Beats OSS includes all Apache 2.0 Beats agents (i.e. Filebeat, Metricbeat, Auditbeat, Heartbeat, Winlogbeat, Packetbeat).

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---
layout: default
title: OpenSearch CLI
nav_order: 52
has_children: false
redirect_from:
- /docs/odfe-cli/
- /docs/cli/
---
# OpenSearch CLI
The OpenSearch CLI command line interface (opensearch-cli) lets you manage your OpenSearch cluster from the command line and automate tasks.
Currently, opensearch-cli supports the [Anomaly Detection]({{site.url}}{{site.baseurl}}/monitoring-plugins/ad/) and [k-NN]({{site.url}}{{site.baseurl}}/search-plugins/knn/) plugins, along with arbitrary REST API paths. Among other things, you can use opensearch-cli to create and delete detectors, start and stop them, and check k-NN statistics.
Profiles let you easily access different clusters or sign requests with different credentials. opensearch-cli supports unauthenticated requests, HTTP basic signing, and IAM signing for Amazon Web Services.
This example moves a detector (`ecommerce-count-quantity`) from a staging cluster to a production cluster:
```bash
opensearch-cli ad get ecommerce-count-quantity --profile staging > ecommerce-count-quantity.json
opensearch-cli ad create ecommerce-count-quantity.json --profile production
opensearch-cli ad start ecommerce-count-quantity.json --profile production
opensearch-cli ad stop ecommerce-count-quantity --profile staging
opensearch-cli ad delete ecommerce-count-quantity --profile staging
```
## Install
1. [Download](https://opensearch.org/downloads.html){:target='\_blank'} and extract the appropriate installation package for your computer.
1. Make the `opensearch-cli` file executable:
```bash
chmod +x ./opensearch-cli
```
1. Add the command to your path:
```bash
export PATH=$PATH:$(pwd)
```
1. Confirm the CLI is working properly:
```bash
opensearch-cli --version
```
## Profiles
Profiles let you easily switch between different clusters and user credentials. To get started, run `opensearch-cli profile create` with the `--auth-type`, `--endpoint`, and `--name` options:
```bash
opensearch-cli profile create --auth-type basic --endpoint https://localhost:9200 --name docker-local
```
Alternatively, save a configuration file to `~/.opensearch-cli/config.yaml`:
```yaml
profiles:
- name: docker-local
endpoint: https://localhost:9200
user: admin
password: foobar
- name: aws
endpoint: https://some-cluster.us-east-1.es.amazonaws.com
aws_iam:
profile: ""
service: es
```
## Usage
opensearch-cli commands use the following syntax:
```bash
opensearch-cli <command> <subcommand> <flags>
```
For example, the following command retrieves information about a detector:
```bash
opensearch-cli ad get my-detector --profile docker-local
```
For a request to the OpenSearch CAT API, try the following command:
```bash
opensearch-cli curl get --path _cat/plugins --profile aws
```
Use the `-h` or `--help` flag to see all supported commands, subcommands, or usage for a specific command:
```bash
opensearch-cli -h
opensearch-cli ad -h
opensearch-cli ad get -h
```

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---
layout: default
title: Java high-level REST client
nav_order: 97
---
# Java high-level REST client
The Elasticsearch OSS Java high-level REST client allows you to interact with your OpenSearch clusters and indices through Java methods and data structures rather than HTTP methods and JSON.
You submit requests to your cluster using request objects, which allows you to create indices, add data to documents, or complete other operations with your cluster. In return, you get back response objects that have all of the available information, such as the associated index or ID, from your cluster.
## Setup
To start using the Elasticsearch OSS Java high-level REST client, ensure that you have the following dependency in your project's `pom.xml` file:
```
<dependency>
<groupId>org.elasticsearch.client</groupId>
<artifactId>elasticsearch-rest-high-level-client</artifactId>
<version>7.10.2</version>
</dependency>
```
You can now start your OpenSearch cluster. The 7.10.2 high-level REST client works with the 1.x versions of OpenSearch.
## Sample code
```java
import org.apache.http.HttpHost;
import org.apache.http.auth.AuthScope;
import org.apache.http.auth.UsernamePasswordCredentials;
import org.apache.http.client.CredentialsProvider;
import org.apache.http.impl.client.BasicCredentialsProvider;
import org.apache.http.impl.nio.client.HttpAsyncClientBuilder;
import org.elasticsearch.action.admin.indices.delete.DeleteIndexRequest;
import org.elasticsearch.action.delete.DeleteRequest;
import org.elasticsearch.action.delete.DeleteResponse;
import org.elasticsearch.action.get.GetRequest;
import org.elasticsearch.action.get.GetResponse;
import org.elasticsearch.action.index.IndexRequest;
import org.elasticsearch.action.index.IndexResponse;
import org.elasticsearch.action.support.master.AcknowledgedResponse;
import org.elasticsearch.client.RequestOptions;
import org.elasticsearch.client.RestClient;
import org.elasticsearch.client.RestClientBuilder;
import org.elasticsearch.client.RestHighLevelClient;
import org.elasticsearch.client.indices.CreateIndexRequest;
import org.elasticsearch.client.indices.CreateIndexResponse;
import org.elasticsearch.common.settings.Settings;
import org.elasticsearch.common.xcontent.XContentType;
import java.io.IOException;
import java.util.HashMap;
public class RESTClientSample {
public static void main(String[] args) throws IOException {
//Point to keystore with appropriate certificates for security.
System.setProperty("javax.net.ssl.trustStore", "/full/path/to/keystore");
System.setProperty("javax.net.ssl.trustStorePassword", password-to-keystore);
//Establish credentials to use basic authentication.
//Only for demo purposes. Do not specify your credentials in code.
final CredentialsProvider credentialsProvider = new BasicCredentialsProvider();
credentialsProvider.setCredentials(AuthScope.ANY,
new UsernamePasswordCredentials("admin", "admin"));
//Create a client.
RestClientBuilder builder = RestClient.builder(new HttpHost("localhost", 9200, "https"))
.setHttpClientConfigCallback(new RestClientBuilder.HttpClientConfigCallback() {
@Override
public HttpAsyncClientBuilder customizeHttpClient(HttpAsyncClientBuilder httpClientBuilder) {
return httpClientBuilder.setDefaultCredentialsProvider(credentialsProvider);
}
});
RestHighLevelClient client = new RestHighLevelClient(builder);
//Create a non-default index with custom settings and mappings.
CreateIndexRequest createIndexRequest = new CreateIndexRequest("test-index");
createIndexRequest.settings(Settings.builder() //Specify in the settings how many shards you want in the index.
.put("index.number_of_shards", 4)
.put("index.number_of_replicas", 3)
);
//Create a set of maps for the index's mappings.
HashMap<String, String> typeMapping = new HashMap<String,String>();
typeMapping.put("type", "integer");
HashMap<String, Object> ageMapping = new HashMap<String, Object>();
ageMapping.put("age", typeMapping);
HashMap<String, Object> mapping = new HashMap<String, Object>();
mapping.put("properties", ageMapping);
createIndexRequest.mapping(mapping);
CreateIndexResponse createIndexResponse = client.indices().create(createIndexRequest, RequestOptions.DEFAULT
//Adding data to the index.
IndexRequest request = new IndexRequest("custom-index"); //Add a document to the custom-index we created.
request.id("1"); //Assign an ID to the document.
HashMap<String, String> stringMapping = new HashMap<String, String>();
stringMapping.put("message:", "Testing Java REST client");
request.source(stringMapping); //Place your content into the index's source.
IndexResponse indexResponse = client.index(request, RequestOptions.DEFAULT);
//Getting back the document
GetRequest getRequest = new GetRequest("custom-index", "1");
GetResponse response = client.get(getRequest, RequestOptions.DEFAULT);
System.out.println(response.getSourceAsString());
//Delete the document
DeleteRequest deleteDocumentRequest = new DeleteRequest("custom-index", "1"); //Index name followed by the ID.
DeleteResponse deleteResponse = client.delete(deleteDocumentRequest, RequestOptions.DEFAULT);
//Delete the index
DeleteIndexRequest deleteIndexRequest = new DeleteIndexRequest("custom-index"); //Index name.
AcknowledgedResponse deleteIndexResponse = client.indices().delete(deleteIndexRequest, RequestOptions.DEFAULT);
client.close();
}
}
```

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title: OpenSearch documentation
description: >- # this means to ignore newlines until "baseurl:"
Documentation for OpenSearch, the Apache 2.0 search, analytics, and visualization suite with advanced security, alerting, SQL support, automated index management, deep performance analysis, and more.
baseurl: "" # the subpath of your site, e.g. /blog
url: "https://docs-beta.opensearch.org" # the base hostname & protocol for your site, e.g. http://example.com
permalink: /:path/
opensearch_version: 1.0.0
opensearch_major_minor_version: 1.0
# Build settings
markdown: kramdown
remote_theme: pmarsceill/just-the-docs@v0.3.3
# Kramdown settings
kramdown:
toc_levels: 2..3
logo: "/assets/images/logo.svg"
# Aux links for the upper right navigation
aux_links:
"Back to OpenSearch.org":
- "https://opensearch.org"
color_scheme: opensearch
# Define Jekyll collections
collections:
# Define a collection named "tests", its documents reside in the "_tests" directory
upgrade-to:
permalink: /:collection/:path/
output: true
opensearch:
permalink: /:collection/:path/
output: true
dashboards:
permalink: /:collection/:path/
output: true
security-plugin:
permalink: /:collection/:path/
output: true
search-plugins:
permalink: /:collection/:path/
output: true
im-plugin:
permalink: /:collection/:path/
output: true
monitoring-plugins:
permalink: /:collection/:path/
output: true
clients:
permalink: /:collection/:path/
output: true
troubleshoot:
permalink: /:collection/:path/
output: true
external_links:
permalink: /:collection/:path/
output: true
just_the_docs:
# Define the collections used in the theme
collections:
upgrade-to:
name: Upgrade to OpenSearch
# nav_exclude: true
nav_fold: true
# search_exclude: true
opensearch:
name: OpenSearch
nav_fold: true
dashboards:
name: OpenSearch Dashboards
nav_fold: true
security-plugin:
name: Security plugin
nav_fold: true
search-plugins:
name: Search plugins
nav_fold: true
im-plugin:
name: Index management plugin
nav_fold: true
monitoring-plugins:
name: Monitoring plugins
nav_fold: true
clients:
name: Clients and tools
nav_fold: true
troubleshoot:
name: Troubleshooting
nav_fold: true
external_links:
name: External links
# Enable or disable the site search
# Supports true (default) or false
search_enabled: true
search:
# Split pages into sections that can be searched individually
# Supports 1 - 6, default: 2
heading_level: 2
# Maximum amount of previews per search result
# Default: 3
previews: 3
# Maximum amount of words to display before a matched word in the preview
# Default: 5
preview_words_before: 5
# Maximum amount of words to display after a matched word in the preview
# Default: 10
preview_words_after: 10
# Set the search token separator
# Default: /[\s\-/]+/
# Example: enable support for hyphenated search words
tokenizer_separator: /[\s/]+/
# Display the relative url in search results
# Supports true (default) or false
rel_url: true
# Enable or disable the search button that appears in the bottom right corner of every page
# Supports true or false (default)
button: false
# Google Analytics Tracking (optional)
# e.g, UA-1234567-89
ga_tracking: G-BQV14XK08F
# Disable the just-the-docs theme anchor links in favor of our custom ones
# See _includes/head_custom.html
heading_anchors: false
# Adds on-hover anchor links to h2-h6
anchor_links: true
footer_content:
plugins:
- jekyll-remote-theme
- jekyll-redirect-from
# Exclude from processing.
# The following items will not be processed, by default. Create a custom list
# to override the default setting.
exclude:
- Gemfile
- Gemfile.lock
- node_modules
- vendor/bundle/
- vendor/cache/
- vendor/gems/
- vendor/ruby/
- README.md

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---
layout: default
title: Browser compatibility
parent: OpenSearch Dashboards
nav_order: 3
---
# Browser compatibility
OpenSearch Dashboards supports the following web browsers:
- Chrome
- Firefox
- Safari
- Edge (Chromium)
Other Chromium-based browsers might work, as well. Internet Explorer and Microsoft Edge Legacy are **not** supported.

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---
layout: default
title: Gantt charts
nav_order: 10
---
# Gantt charts
OpenSearch Dashboards includes a Gantt chart visualization. Gantt charts show the start, end, and duration of unique events in a sequence. Gantt charts are useful in trace analytics, telemetry, and anomaly detection use cases, where you want to understand interactions and dependencies between various events in a schedule.
For example, consider an index of log data. The fields in a typical set of log data, especially audit logs, contain a specific operation or event with a start time and duration.
To create a Gantt chart, perform the following steps:
1. In the visualizations menu, choose **Create visualization** and **Gantt Chart**.
1. Choose a source for the chart (e.g. some log data).
1. Under **Metrics**, choose **Event**. For log data, each log is an event.
1. Select the **Start Time** and **Duration** fields from your data set. The start time is the timestamp for the begining of an event. The duration is the amount of time to add to the start time.
1. Under **Results**, choose the number of events to display on the chart. Gantt charts sequence events from earliest to latest based on start time.
1. Choose **Panel settings** to adjust axis labels, time format, and colors.
1. Choose **Update**.
![Gantt Chart]({{site.url}}{{site.baseurl}}/images/gantt-chart.png)
This Gantt chart displays the ID of each log on the y-axis. Each bar is a unique event that spans some amount of time. Hover over a bar to see the duration of that event.

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---
layout: default
title: About Dashboards
nav_order: 1
has_children: false
has_toc: false
redirect_from:
- /docs/opensearch-dashboards/
- /opensearch-dashboards/
- /dashboards/
---
# OpenSearch Dashboards
OpenSearch Dashboards is the default visualization tool for data in OpenSearch. It also serves as a user interface for many of the OpenSearch plugins, including security, alerting, Index State Management, SQL, and more.
## Get started with OpenSearch Dashboards
1. After starting OpenSearch Dashboards, you can access it at port 5601. For example, http://localhost:5601.
1. Log in with the default username `admin` and password `admin`.
1. Choose **Try our sample data** and add the sample flight data.
1. Choose **Discover** and search for a few flights.
1. Choose **Dashboard**, **[Flights] Global Flight Dashboard**, and wait for the dashboard to load.

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---
layout: default
title: Docker
parent: Install OpenSearch Dashboards
nav_order: 1
---
# Run OpenSearch Dashboards using Docker
You *can* start OpenSearch Dashboards using `docker run` after [creating a Docker network](https://docs.docker.com/engine/reference/commandline/network_create/) and starting OpenSearch, but the process of connecting OpenSearch Dashboards to OpenSearch is significantly easier with a Docker Compose file.
1. Run `docker pull opensearchproject/opensearch-dashboards:{{site.opensearch_version}}`.
1. Create a [`docker-compose.yml`](https://docs.docker.com/compose/compose-file/) file appropriate for your environment. A sample file that includes OpenSearch Dashboards is available on the OpenSearch [Docker installation page]({{site.url}}{{site.baseurl}}/opensearch/install/docker#sample-docker-compose-file).
Just like `opensearch.yml`, you can pass a custom `opensearch_dashboards.yml` to the container in the Docker Compose file.
{: .tip }
1. Run `docker-compose up`.
Wait for the containers to start. Then see the [OpenSearch Dashboards documentation]({{site.url}}{{site.baseurl}}/).
1. When finished, run `docker-compose down`.

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---
layout: default
title: Install OpenSearch Dashboards
nav_order: 1
has_children: true
redirect_from:
- /dashboards/install/
---
# Install and configure OpenSearch Dashboards
OpenSearch Dashboards has two installation options at this time: Docker images and tarballs.

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---
layout: default
title: OpenSearch Dashboards plugins
parent: Install OpenSearch Dashboards
nav_order: 50
---
# Standalone plugin install
If you don't want to use the all-in-one installation options, you can install the various plugins for OpenSearch Dashboards individually.
---
#### Table of contents
1. TOC
{:toc}
---
## Plugin compatibility
<table>
<thead style="text-align: left">
<tr>
<th>OpenSearch Dashboards version</th>
<th>Plugin versions</th>
</tr>
</thead>
<tbody>
<tr>
<td>1.0.0</td>
<td>
<pre>alertingDashboards 1.0.0.0
anomalyDetectionDashboards 1.0.0.0
ganttChartDashboards 1.0.0.0
indexManagementDashboards 1.0.0.0
notebooksDashboards 1.0.0.0
queryWorkbenchDashboards 1.0.0.0
reportsDashboards 1.0.0.0
securityDashboards 1.0.0.0
traceAnalyticsDashboards 1.0.0.0
</pre>
</td>
</tr>
<tr>
<td>1.0.0-rc1</td>
<td>
<pre>alertingDashboards 1.0.0.0-rc1
anomalyDetectionDashboards 1.0.0.0-rc1
ganttChartDashboards 1.0.0.0-rc1
indexManagementDashboards 1.0.0.0-rc1
notebooksDashboards 1.0.0.0-rc1
queryWorkbenchDashboards 1.0.0.0-rc1
reportsDashboards 1.0.0.0-rc1
securityDashboards 1.0.0.0-rc1
traceAnalyticsDashboards 1.0.0.0-rc1
</pre>
</td>
</tr>
<tr>
<td>1.0.0-beta1</td>
<td>
<pre>alertingDashboards 1.0.0.0-beta1
anomalyDetectionDashboards 1.0.0.0-beta1
ganttChartDashboards 1.0.0.0-beta1
indexManagementDashboards 1.0.0.0-beta1
notebooksDashboards 1.0.0.0-beta1
queryWorkbenchDashboards 1.0.0.0-beta1
reportsDashboards 1.0.0.0-beta1
securityDashboards 1.0.0.0-beta1
traceAnalyticsDashboards 1.0.0.0-beta1
</pre>
</td>
</tr>
</tbody>
</table>
## Prerequisites
- A compatible OpenSearch cluster
- The corresponding OpenSearch plugins [installed on that cluster]({{site.url}}{{site.baseurl}}/opensearch/install/plugins/)
- The corresponding version of [OpenSearch Dashboards]({{site.url}}{{site.baseurl}}/) (e.g. OpenSearch Dashboards 1.0.0 works with OpenSearch 1.0.0)
## Install
Navigate to the OpenSearch Dashboards home directory (likely `/usr/share/opensearch-dashboards`) and run the install command for each plugin.
{% comment %}
#### Security OpenSearch Dashboards
```bash
sudo bin/opensearch-dashboards-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-dashboards-plugins/opensearch-security/opensearchSecurityOpenSearch Dashboards-{{site.opensearch_major_minor_version}}.0.1.zip
```
This plugin provides a user interface for managing users, roles, mappings, action groups, and tenants.
#### Alerting OpenSearch Dashboards
```bash
sudo bin/opensearch-dashboards-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-dashboards-plugins/opensearch-alerting/opensearchAlertingOpenSearch Dashboards-{{site.opensearch_major_minor_version}}.0.0.zip
```
This plugin provides a user interface for creating monitors and managing alerts.
#### Index State Management OpenSearch Dashboards
```bash
sudo bin/opensearch-dashboards-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-dashboards-plugins/opensearch-index-management/opensearchIndexManagementOpenSearch Dashboards-{{site.opensearch_major_minor_version}}.0.1.zip
```
This plugin provides a user interface for managing policies.
#### Anomaly Detection OpenSearch Dashboards
```bash
sudo bin/opensearch-dashboards-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-dashboards-plugins/opensearch-anomaly-detection/opensearchAnomalyDetectionOpenSearch Dashboards-{{site.opensearch_major_minor_version}}.0.0.zip
```
This plugin provides a user interface for adding detectors.
#### Query Workbench OpenSearch Dashboards
```bash
sudo bin/opensearch-dashboards-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-dashboards-plugins/opensearch-query-workbench/opensearchQueryWorkbenchOpenSearch Dashboards-{{site.opensearch_major_minor_version}}.0.0.zip
```
This plugin provides a user interface for using SQL queries to explore your data.
#### Trace Analytics
```bash
sudo bin/opensearch-dashboards-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-dashboards-plugins/opensearch-trace-analytics/opensearchTraceAnalyticsOpenSearch Dashboards-{{site.opensearch_major_minor_version}}.2.0.zip
```
This plugin uses distributed trace data (indexed in OpenSearch using Data Prepper) to display latency trends, error rates, and more.
#### Notebooks OpenSearch Dashboards
```bash
sudo bin/opensearch-dashboards-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-dashboards-plugins/opensearch-notebooks/opensearchNotebooksOpenSearch Dashboards-{{site.opensearch_major_minor_version}}.2.0.zip
```
This plugin lets you combine OpenSearch Dashboards visualizations and narrative text in a single interface.
#### Reports OpenSearch Dashboards
```bash
# x86 Linux
sudo bin/opensearch-dashboards-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-dashboards-plugins/opensearch-reports/linux/x64/opensearchReportsOpenSearch Dashboards-{{site.opensearch_major_minor_version}}.2.0-linux-x64.zip
# ARM64 Linux
sudo bin/opensearch-dashboards-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-dashboards-plugins/opensearch-reports/linux/arm64/opensearchReportsOpenSearch Dashboards-{{site.opensearch_major_minor_version}}.2.0-linux-arm64.zip
# x86 Windows
sudo bin/opensearch-dashboards-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-dashboards-plugins/opensearch-reports/windows/x64/opensearchReportsOpenSearch Dashboards-{{site.opensearch_major_minor_version}}.2.0-windows-x64.zip
```
This plugin lets you export and share reports from OpenSearch Dashboards dashboards, visualizations, and saved searches.
#### Gantt Chart OpenSearch Dashboards
```bash
sudo bin/opensearch-dashboards-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-dashboards-plugins/opensearch-gantt-chart/opensearchGanttChartOpenSearch Dashboards-{{site.opensearch_major_minor_version}}.0.0.zip
```
This plugin adds a new Gantt chart visualization.
{% endcomment %}
## List installed plugins
To check your installed plugins:
```bash
sudo bin/opensearch-dashboards-plugin list
```
## Remove plugins
To remove a plugin:
```bash
sudo bin/opensearch-dashboards-plugin remove <plugin-name>
```
Then remove all associated entries from `opensearch_dashboards.yml`.
For certain plugins, you must also remove the "optimze" bundle. This is a sample command for the Anomaly Detection plugin:
```bash
sudo rm /usr/share/opensearch-dashboards/optimize/bundles/opensearch-anomaly-detection-opensearch-dashboards.*
```
Then restart OpenSearch Dashboards. After you remove any plugin, OpenSearch Dashboards performs an optimize operation the next time you start it. This operation takes several minutes even on fast machines, so be patient.
## Update plugins
OpenSearch Dashboards doesn’t update plugins. Instead, you have to remove the old version and its optimized bundle, reinstall them, and restart OpenSearch Dashboards:
1. Remove the old version:
```bash
sudo bin/opensearch-dashboards-plugin remove <plugin-name>
```
1. Remove the optimized bundle:
```bash
sudo rm /usr/share/opensearch-dashboards/optimize/bundles/<bundle-name>
```
1. Reinstall the new version:
```bash
sudo bin/opensearch-dashboards-plugin install <plugin-name>
```
1. Restart OpenSearch Dashboards.
For example, to remove and reinstall the anomaly detection plugin:
```bash
sudo bin/opensearch-plugin remove opensearch-anomaly-detection
sudo rm /usr/share/opensearch-dashboards/optimize/bundles/opensearch-anomaly-detection-opensearch-dashboards.*
sudo bin/opensearch-dashboards-plugin install <AD OpenSearch Dashboards plugin artifact URL>
```

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@ -1,30 +0,0 @@
---
layout: default
title: Tarball
parent: Install OpenSearch Dashboards
nav_order: 30
---
# Run OpenSearch Dashboards using the tarball
1. Download the tarball from the [OpenSearch downloads page](https://opensearch.org/downloads.html){:target='\_blank'}.
1. Extract the TAR file to a directory and change to that directory:
```bash
# x64
tar -zxf opensearch-dashboards-{{site.opensearch_version}}-linux-x64.tar.gz
cd opensearch-dashboards{% comment %}# ARM64
tar -zxf opensearch-dashboards-{{site.opensearch_version}}-linux-arm64.tar.gz
cd opensearch-dashboards{% endcomment %}
```
1. If desired, modify `config/opensearch_dashboards.yml`.
1. Run OpenSearch Dashboards:
```bash
./bin/opensearch-dashboards
```
1. See the [OpenSearch Dashboards documentation]({{site.url}}{{site.baseurl}}/opensearch-dashboards/).

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@ -1,41 +0,0 @@
---
layout: default
title: Configure TLS
parent: Install OpenSearch Dashboards
nav_order: 40
---
# Configure TLS for OpenSearch Dashboards
By default, for ease of testing and getting started, OpenSearch Dashboards runs over HTTP. To enable TLS for HTTPS, update the following settings in `opensearch_dashboards.yml`.
Setting | Description
:--- | :---
opensearch.ssl.verificationMode | This setting is for communications between OpenSearch and OpenSearch Dashboards. Valid values are `full`, `certificate`, or `none`. We recommend `full` if you enable TLS, which enables hostname verification. `certificate` just checks the certificate, not the hostname, and `none` performs no checks (suitable for HTTP). Default is `full`.
opensearch.ssl.certificateAuthorities | If `opensearch.ssl.verificationMode` is `full` or `certificate`, specify the full path (e.g. `[ "/usr/share/opensearch-dashboards-1.0.0/config/root-ca.pem" ]` to the certificate authority for your OpenSearch cluster.
server.ssl.enabled | This setting is for communications between OpenSearch Dashboards and the web browser. Set to true for HTTPS, false for HTTP.
server.ssl.certificate | If `server.ssl.enabled` is true, specify the full path (e.g. `/usr/share/opensearch-dashboards-1.0.0/config/my-client-cert.pem` to a valid client certificate for your OpenSearch cluster. You can [generate your own]({{site.url}}{{site.baseurl}}/security-plugin/configuration/generate-certificates/) or get one from a certificate authority.
server.ssl.key | If `server.ssl.enabled` is true, specify the full path (e.g. `/usr/share/opensearch-dashboards-1.0.0/config/my-client-cert-key.pem` to the key for your client certificate. You can [generate your own]({{site.url}}{{site.baseurl}}/security-plugin/configuration/generate-certificates/) or get one from a certificate authority.
opensearch_security.cookie.secure | If you enable TLS for OpenSearch Dashboards, change this setting to `true`. For HTTP, set it to `false`.
This `opensearch_dashboards.yml` configuration shows OpenSearch and OpenSearch Dashboards running on the same machine with the demo configuration:
```yml
opensearch.hosts: ["https://localhost:9200"]
opensearch.ssl.verificationMode: full
opensearch.username: "kibanaserver"
opensearch.password: "kibanaserver"
opensearch.requestHeadersWhitelist: [ authorization,securitytenant ]
server.ssl.enabled: true
server.ssl.certificate: /usr/share/opensearch-1.0.0/config/client-cert.pem
server.ssl.key: /usr/share/opensearch-1.0.0/config/client-cert-key.pem
opensearch.ssl.certificateAuthorities: [ "/usr/share/opensearch-1.0.0/config/root-ca.pem" ]
opensearch_security.multitenancy.enabled: true
opensearch_security.multitenancy.tenants.preferred: ["Private", "Global"]
opensearch_security.readonly_mode.roles: ["kibana_read_only"]
opensearch_security.cookie.secure: true
```
If you use the Docker install, you can pass a custom `opensearch_dashboards.yml` to the container. To learn more, see the [Docker installation page]({{site.url}}{{site.baseurl}}/opensearch/install/docker/).
After enabling these settings and starting OpenSearch Dashboards, you can connect to it at `https://localhost:5601`. You might have to acknowledge a browser warning if your certificates are self-signed. To avoid this sort of warning (or outright browser incompatibility), best practice is to use certificates from trusted certificate authority.

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---
layout: default
title: WMS map server
nav_order: 5
---
# Configure WMS map server
OpenSearch Dashboards includes default map tiles, but if you need more specialized maps, you can configure OpenSearch Dashboards to use a WMS map server:
1. Open OpenSearch Dashboards at `https://<host>:<port>`. For example, [https://localhost:5601](https://localhost:5601).
1. If necessary, log in.
1. Choose **Management** and **Advanced Settings**.
1. Locate `visualization:tileMap:WMSdefaults`.
1. Change `enabled` to true and add the URL of a valid WMS map server:
```json
{
"enabled": true,
"url": "<wms-map-server-url>",
"options": {
"format": "image/png",
"transparent": true
}
}
```
Map services often have licensing fees or restrictions. You're responsible for all such considerations on any map server that you specify.
{: .note }

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@ -1,118 +0,0 @@
---
layout: default
title: Notebooks
nav_order: 50
redirect_from: /docs/notebooks/
has_children: false
---
# Notebooks
An OpenSearch Dashboards notebook is an interface that lets you easily combine code snippets, live visualizations, and narrative text in a single notebook interface.
Notebooks let you interactively explore data by running different visualizations that you can share with team members to collaborate on a project.
A notebook is a document composed of two elements: code blocks (Markdown/SQL/PPL) and visualizations. Choose multiple timelines to compare and contrast visualizations.
You can also generate [reports]({{site.url}}{{site.baseurl}}/dashboards/reporting/) directly from your notebooks.
Common use cases include creating postmortem reports, designing runbooks, building live infrastructure reports, and writing documentation.
Tenants in OpenSearch Dashboards are spaces for saving notebooks and other OpenSearch Dashboards objects. For more information, see [OpenSearch Dashboards multi-tenancy]({{site.url}}{{site.baseurl}}/security-plugin/access-control/multi-tenancy/).
{: .note }
## Get started with notebooks
To get started, choose **Notebooks** within OpenSearch Dashboards.
### Step 1: Create a notebook
A notebook is an interface for creating reports.
1. Choose **Create notebook** and enter a descriptive name.
1. Choose **Create**.
Choose **Actions** to rename, duplicate, or delete a notebook.
### Step 2: Add a paragraph
Paragraphs combine code blocks and visualizations for describing data.
#### Add a code block
Code blocks support markdown, SQL, and PPL languages.
Specify the input language on the first line using `%[language type]` syntax.
For example, type `%md` for markdown, `%sql` for SQL, and `%ppl` for PPL.
##### Sample markdown block
```
%md
Add in text formatted in markdown.
```
![Markdown paragraph]({{site.url}}{{site.baseurl}}/images/markdown-notebook.png)
##### Sample SQL block
```sql
%sql
Select * from opensearch_dashboards_sample_data_flights limit 20;
```
![SQL paragraph]({{site.url}}{{site.baseurl}}/images/sql-notebook.png)
##### Sample PPL block
```
%ppl
source=opensearch_dashboards_sample_data_logs | head 20
```
![PPL paragraph]({{site.url}}{{site.baseurl}}/images/ppl-notebook.png)
#### Add a visualization
1. To add a visualization, choose **Add paragraph** and select **Visualization**.
1. In **Title**, select your visualization and choose a date range. You can choose multiple timelines to compare and contrast visualizations.
1. To run and save a paragraph, choose **Run**.
![Visualization paragraph]({{site.url}}{{site.baseurl}}/images/visualization-notebook.png)
## Paragraph actions
You can perform the following actions on paragraphs:
- Add a new paragraph to the top of a report.
- Add a new paragraph to the bottom of a report.
- Run all the paragraphs at the same time.
- Clear the outputs of all paragraphs.
- Delete all the paragraphs.
- Move paragraphs up and down.
## Sample notebooks
We prepared the following sample notebooks that showcase a variety of use cases:
- Using SQL to query the OpenSearch Dashboards sample flight data.
- Using PPL to query the OpenSearch Dashboards sample web logs data.
- Using PPL and visualizations to perform sample root cause event analysis on the OpenSearch Dashboards sample web logs data.
To add a sample notebook, choose **Actions** and select **Add sample notebooks**.
## Create a report
You can use notebooks to create PNG and PDF reports:
1. From the top menu bar, choose **Reporting actions**.
1. You can choose to **Download PDF** or **Download PNG**.
Reports generate asynchronously in the background and might take a few minutes, depending on the size of the report. A notification appears when your report is ready to download.
1. To create a schedule-based report, choose **Create report definition**. For steps to create a report definition, see [Create reports using a definition]({{site.url}}{{site.baseurl}}/dashboards/reporting#create-reports-using-a-definition).
1. To see all your reports, choose **View all reports**.

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---
layout: default
title: Reporting
nav_order: 20
---
# Reporting
You can use OpenSearch Dashboards to create PNG, PDF, and CSV reports. To create reports, you must have the correct permissions. For a summary of the predefined roles and the permissions they grant, see the [security plugin]({{site.url}}{{site.baseurl}}/security-plugin/access-control/users-roles#predefined-roles).
CSV reports have a non-configurable 10,000 row limit. They have no explicit size limit (e.g. in MB), but extremely large documents could cause report generation to fail with an out of memory error from the V8 JavaScript engine.
{: .tip }
## Create reports from Discovery, Visualize, or Dashboard
Quickly generate an on-demand report from the current view.
1. From the top menu bar, choose **Reporting**.
1. For dashboards or visualizations, choose **Download PDF** or **Download PNG**. From the Discover page, choose **Download CSV**.
Reports generate asynchronously in the background and might take a few minutes, depending on the size of the report. A notification appears when your report is ready to download.
1. To create a schedule-based report, choose **Create report definition**. Then proceed to [Create reports using a definition](#create-reports-using-a-definition). This option pre-fills many of the fields for you based on the visualization, dashboard, or data you were viewing.
## Create reports using a definition
Definitions let you generate reports on a periodic schedule.
1. From the navigation panel, choose **Reporting**.
1. Choose **Create**.
1. Under **Report settings**, enter a name and optional description for your report.
1. Choose the **Report Source** (i.e. the page from which the report is generated). You can generate reports from the **Dashboard**, **Visualize**, or **Discover** pages.
1. Select your dashboard, visualization, or saved search. Then choose a time range for the report.
1. Choose an appropriate file format for the report.
1. (Optional) Add a header or footer to the report. Headers and footers are only available for dashboard or visualization reports.
1. Under **Report trigger**, choose either **On-demand** or **Schedule**.
For scheduled reports, select either **Recurring** or **Cron based**. You can receive reports daily or at some other time interval. Cron expressions give you even more flexiblity. See [Cron expression reference]({{site.url}}{{site.baseurl}}/monitoring-plugins/alerting/cron/) for more information.
1. Choose **Create**.
## Troubleshooting
### Chromium fails to launch with OpenSearch Dashboards
While creating a report for dashboards or visualizations, you might see a the following error:
![OpenSearch Dashboards reporting pop-up error message]({{site.url}}{{site.baseurl}}/images/reporting-error.png)
This problem can occur for two reasons:
- You don't have the correct version of `headless-chrome` to match the operating system on which OpenSearch Dashboards is running. Download the correct version [here](https://github.com/opensearch-project/dashboards-reports/releases/tag/chromium-1.12.0.0).
- You're missing additional dependencies. Install the required dependencies for your operating system from the [additional libraries](https://github.com/opensearch-project/dashboards-reports/blob/main/dashboards-reports/rendering-engine/headless-chrome/README.md#additional-libaries) section.

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---
layout: default
title: Javadoc
nav_order: 1
permalink: /javadoc/
redirect_to: https://opensearch.org/docs/javadocs/
---

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---
layout: default
title: Index rollups
nav_order: 35
has_children: true
redirect_from: /docs/ism/index-rollups/
has_toc: false
---
# Index rollups
Time series data increases storage costs, strains cluster health, and slows down aggregations over time. Index rollup lets you periodically reduce data granularity by rolling up old data into summarized indices.
You pick the fields that interest you and use index rollup to create a new index with only those fields aggregated into coarser time buckets. You can store months or years of historical data at a fraction of the cost with the same query performance.
For example, say you collect CPU consumption data every five seconds and store it on a hot node. Instead of moving older data to a read-only warm node, you can roll up or compress this data with only the average CPU consumption per day or with a 10% decrease in its interval every week.
You can use index rollup in three ways:
1. Use the index rollup API for an on-demand index rollup job that operates on an index that's not being actively ingested such as a rolled-over index. For example, you can perform an index rollup operation to reduce data collected at a five minute interval to a weekly average for trend analysis.
2. Use the OpenSearch Dashboards UI to create an index rollup job that runs on a defined schedule. You can also set it up to roll up your indices as it’s being actively ingested. For example, you can continuously roll up Logstash indices from a five second interval to a one hour interval.
3. Specify the index rollup job as an ISM action for complete index management. This allows you to roll up an index after a certain event such as a rollover, index age reaching a certain point, index becoming read-only, and so on. You can also have rollover and index rollup jobs running in sequence, where the rollover first moves the current index to a warm node and then the index rollup job creates a new index with the minimized data on the hot node.
## Create an Index Rollup Job
To get started, choose **Index Management** in OpenSearch Dashboards.
Select **Rollup Jobs** and choose **Create rollup job**.
### Step 1: Set up indices
1. In the **Job name and description** section, specify a unique name and an optional description for the index rollup job.
2. In the **Indices** section, select the source and target index. The source index is the one that you want to roll up. The source index remains as is, the index rollup job creates a new index referred to as a target index. The target index is where the index rollup results are saved. For target index, you can either type in a name for a new index or you select an existing index.
5. Choose **Next**
After you create an index rollup job, you can't change your index selections.
### Step 2: Define aggregations and metrics
Select the attributes with the aggregations (terms and histograms) and metrics (avg, sum, max, min, and value count) that you want to roll up. Make sure you don’t add a lot of highly granular attributes, because you won’t save much space.
For example, consider a dataset of cities and demographics within those cities. You can aggregate based on cities and specify demographics within a city as metrics.
The order in which you select attributes is critical. A city followed by a demographic is different from a demographic followed by a city.
1. In the **Time aggregation** section, select a timestamp field. Choose between a **Fixed** or **Calendar** interval type and specify the interval and timezone. The index rollup job uses this information to create a date histogram for the timestamp field.
2. (Optional) Add additional aggregations for each field. You can choose terms aggregation for all field types and histogram aggregation only for numeric fields.
3. (Optional) Add additional metrics for each field. You can choose between **All**, **Min**, **Max**, **Sum**, **Avg**, or **Value Count**.
4. Choose **Next**.
### Step 3: Specify schedule
Specify a schedule to roll up your indices as it’s being ingested. The index rollup job is enabled by default.
1. Specify if the data is continuous or not.
3. For roll up execution frequency, select **Define by fixed interval** and specify the **Rollup interval** and the time unit or **Define by cron expression** and add in a cron expression to select the interval. To learn how to define a cron expression, see [Alerting]({{site.url}}{{site.baseurl}}/monitoring-plugins/alerting/cron/).
4. Specify the number of pages per execution process. A larger number means faster execution and more cost for memory.
5. (Optional) Add a delay to the roll up executions. This is the amount of time the job waits for data ingestion to accommodate any processing time. For example, if you set this value to 10 minutes, an index rollup that executes at 2 PM to roll up 1 PM to 2 PM of data starts at 2:10 PM.
6. Choose **Next**.
### Step 4: Review and create
Review your configuration and select **Create**.
### Step 5: Search the target index
You can use the standard `_search` API to search the target index. Make sure that the query matches the constraints of the target index. For example, if you don’t set up terms aggregations on a field, you don’t receive results for terms aggregations. If you don’t set up the maximum aggregations, you don’t receive results for maximum aggregations.
You can’t access the internal structure of the data in the target index because the plugin automatically rewrites the query in the background to suit the target index. This is to make sure you can use the same query for the source and target index.
To query the target index, set `size` to 0:
```json
GET target_index/_search
{
"size": 0,
"query": {
"match_all": {}
},
"aggs": {
"avg_cpu": {
"avg": {
"field": "cpu_usage"
}
}
}
}
```
Consider a scenario where you collect rolled up data from 1 PM to 9 PM in hourly intervals and live data from 7 PM to 11 PM in minutely intervals. If you execute an aggregation over these in the same query, for 7 PM to 9 PM, you see an overlap of both rolled up data and live data because they get counted twice in the aggregations.
## Sample Walkthrough
This walkthrough uses the OpenSearch Dashboards sample e-commerce data. To add that sample data, log in to OpenSearch Dashboards, choose **Home** and **Try our sample data**. For **Sample eCommerce orders**, choose **Add data**.
Then run a search:
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
```
#### Sample response
```json
{
"took": 23,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 4675,
"relation": "eq"
},
"max_score": 1,
"hits": [
{
"_index": "opensearch_dashboards_sample_data_ecommerce",
"_type": "_doc",
"_id": "jlMlwXcBQVLeQPrkC_kQ",
"_score": 1,
"_source": {
"category": [
"Women's Clothing",
"Women's Accessories"
],
"currency": "EUR",
"customer_first_name": "Selena",
"customer_full_name": "Selena Mullins",
"customer_gender": "FEMALE",
"customer_id": 42,
"customer_last_name": "Mullins",
"customer_phone": "",
"day_of_week": "Saturday",
"day_of_week_i": 5,
"email": "selena@mullins-family.zzz",
"manufacturer": [
"Tigress Enterprises"
],
"order_date": "2021-02-27T03:56:10+00:00",
"order_id": 581553,
"products": [
{
"base_price": 24.99,
"discount_percentage": 0,
"quantity": 1,
"manufacturer": "Tigress Enterprises",
"tax_amount": 0,
"product_id": 19240,
"category": "Women's Clothing",
"sku": "ZO0064500645",
"taxless_price": 24.99,
"unit_discount_amount": 0,
"min_price": 12.99,
"_id": "sold_product_581553_19240",
"discount_amount": 0,
"created_on": "2016-12-24T03:56:10+00:00",
"product_name": "Blouse - port royal",
"price": 24.99,
"taxful_price": 24.99,
"base_unit_price": 24.99
},
{
"base_price": 10.99,
"discount_percentage": 0,
"quantity": 1,
"manufacturer": "Tigress Enterprises",
"tax_amount": 0,
"product_id": 17221,
"category": "Women's Accessories",
"sku": "ZO0085200852",
"taxless_price": 10.99,
"unit_discount_amount": 0,
"min_price": 5.06,
"_id": "sold_product_581553_17221",
"discount_amount": 0,
"created_on": "2016-12-24T03:56:10+00:00",
"product_name": "Snood - rose",
"price": 10.99,
"taxful_price": 10.99,
"base_unit_price": 10.99
}
],
"sku": [
"ZO0064500645",
"ZO0085200852"
],
"taxful_total_price": 35.98,
"taxless_total_price": 35.98,
"total_quantity": 2,
"total_unique_products": 2,
"type": "order",
"user": "selena",
"geoip": {
"country_iso_code": "MA",
"location": {
"lon": -8,
"lat": 31.6
},
"region_name": "Marrakech-Tensift-Al Haouz",
"continent_name": "Africa",
"city_name": "Marrakesh"
},
"event": {
"dataset": "sample_ecommerce"
}
}
}
]
}
}
...
```
Create an index rollup job.
This example picks the `order_date`, `customer_gender`, `geoip.city_name`, `geoip.region_name`, and `day_of_week` fields and rolls them into an `example_rollup` target index:
```json
PUT _plugins/_rollup/jobs/example
{
"rollup": {
"enabled": true,
"schedule": {
"interval": {
"period": 1,
"unit": "Minutes",
"start_time": 1602100553
}
},
"last_updated_time": 1602100553,
"description": "An example policy that rolls up the sample ecommerce data",
"source_index": "opensearch_dashboards_sample_data_ecommerce",
"target_index": "example_rollup",
"page_size": 1000,
"delay": 0,
"continuous": false,
"dimensions": [
{
"date_histogram": {
"source_field": "order_date",
"fixed_interval": "60m",
"timezone": "America/Los_Angeles"
}
},
{
"terms": {
"source_field": "customer_gender"
}
},
{
"terms": {
"source_field": "geoip.city_name"
}
},
{
"terms": {
"source_field": "geoip.region_name"
}
},
{
"terms": {
"source_field": "day_of_week"
}
}
],
"metrics": [
{
"source_field": "taxless_total_price",
"metrics": [
{
"avg": {}
},
{
"sum": {}
},
{
"max": {}
},
{
"min": {}
},
{
"value_count": {}
}
]
},
{
"source_field": "total_quantity",
"metrics": [
{
"avg": {}
},
{
"max": {}
}
]
}
]
}
}
```
You can query the `example_rollup` index for the terms aggregations on the fields set up in the rollup job.
You get back the same response that you would on the original `opensearch_dashboards_sample_data_ecommerce` source index.
```json
POST example_rollup/_search
{
"size": 0,
"query": {
"bool": {
"must": {"term": { "geoip.region_name": "California" } }
}
},
"aggregations": {
"daily_numbers": {
"terms": {
"field": "day_of_week"
},
"aggs": {
"per_city": {
"terms": {
"field": "geoip.city_name"
},
"aggregations": {
"average quantity": {
"avg": {
"field": "total_quantity"
}
}
}
},
"total_revenue": {
"sum": {
"field": "taxless_total_price"
}
}
}
}
}
}
```
#### Sample Response
```json
{
"took": 476,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 281,
"relation": "eq"
},
"max_score": null,
"hits": []
},
"aggregations": {
"daily_numbers": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Friday",
"doc_count": 53,
"total_revenue": {
"value": 4858.84375
},
"per_city": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Los Angeles",
"doc_count": 53,
"average quantity": {
"value": 2.305084745762712
}
}
]
}
},
{
"key": "Saturday",
"doc_count": 43,
"total_revenue": {
"value": 3547.203125
},
"per_city": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Los Angeles",
"doc_count": 43,
"average quantity": {
"value": 2.260869565217391
}
}
]
}
},
{
"key": "Tuesday",
"doc_count": 42,
"total_revenue": {
"value": 3983.28125
},
"per_city": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Los Angeles",
"doc_count": 42,
"average quantity": {
"value": 2.2888888888888888
}
}
]
}
},
{
"key": "Sunday",
"doc_count": 40,
"total_revenue": {
"value": 3308.1640625
},
"per_city": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Los Angeles",
"doc_count": 40,
"average quantity": {
"value": 2.090909090909091
}
}
]
}
}
...
]
}
}
}
```

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@ -1,234 +0,0 @@
---
layout: default
title: Index rollups API
parent: Index rollups
redirect_from: /docs/ism/rollup-api/
nav_order: 9
---
# Index rollups API
Use the index rollup operations to programmatically work with index rollup jobs.
---
#### Table of contents
- TOC
{:toc}
---
## Create or update an index rollup job
Creates or updates an index rollup job.
You must provide the `seq_no` and `primary_term` parameters.
#### Request
```json
PUT _plugins/_rollup/jobs/<rollup_id> // Create
PUT _plugins/_rollup/jobs/<rollup_id>?if_seq_no=1&if_primary_term=1 // Update
{
"rollup": {
"source_index": "nyc-taxi-data",
"target_index": "rollup-nyc-taxi-data",
"schedule": {
"interval": {
"period": 1,
"unit": "Days"
}
},
"description": "Example rollup job",
"enabled": true,
"page_size": 200,
"delay": 0,
"roles": [
"rollup_all",
"nyc_taxi_all",
"example_rollup_index_all"
],
"continuous": false,
"dimensions": {
"date_histogram": {
"source_field": "tpep_pickup_datetime",
"fixed_interval": "1h",
"timezone": "America/Los_Angeles"
},
"terms": {
"source_field": "PULocationID"
},
"metrics": [
{
"source_field": "passenger_count",
"metrics": [
{
"avg": {}
},
{
"sum": {}
},
{
"max": {}
},
{
"min": {}
},
{
"value_count": {}
}
]
}
]
}
}
}
```
You can specify the following options.
Options | Description | Type | Required
:--- | :--- |:--- |:--- |
`source_index` | The name of the detector. | `string` | Yes
`target_index` | Specify the target index that the rolled up data is ingested into. You could either create a new target index or use an existing index. The target index cannot be a combination of raw and rolled up data. | `string` | Yes
`schedule` | Schedule of the index rollup job which can be an interval or a cron expression. | `object` | Yes
`schedule.interval` | Specify the frequency of execution of the rollup job. | `object` | No
`schedule.interval.start_time` | Start time of the interval. | `timestamp` | Yes
`schedule.interval.period` | Define the interval period. | `string` | Yes
`schedule.interval.unit` | Specify the time unit of the interval. | `string` | Yes
`schedule.interval.cron` | Optionally, specify a cron expression to define therollup frequency. | `list` | No
`schedule.interval.cron.expression` | Specify a Unix cron expression. | `string` | Yes
`schedule.interval.cron.timezone` | Specify timezones as defined by the IANA Time Zone Database. Defaults to UTC. | `string` | No
`description` | Optionally, describe the rollup job. | `string` | No
`enabled` | When true, the index rollup job is scheduled. Default is true. | `boolean` | Yes
`continuous` | Specify whether or not the index rollup job continuously rolls up data forever or just executes over the current data set once and stops. Default is false. | `boolean` | Yes
`error_notification` | Set up a Mustache message template sent for error notifications. For example, if an index rollup job fails, the system sends a message to a Slack channel. | `object` | No
`page_size` | Specify the number of buckets to paginate through at a time while rolling up. | `number` | Yes
`delay` | Specify time value to delay execution of the index rollup job. | `time_unit` | No
`dimensions` | Specify aggregations to create dimensions for the roll up time window. | `object` | Yes
`dimensions.date_histogram` | Specify either fixed_interval or calendar_interval, but not both. Either one limits what you can query in the target index. | `object` | No
`dimensions.date_histogram.fixed_interval` | Specify the fixed interval for aggregations in milliseconds, seconds, minutes, hours, or days. | `string` | No
`dimensions.date_histogram.calendar_interval` | Specify the calendar interval for aggregations in minutes, hours, days, weeks, months, quarters, or years. | `string` | No
`dimensions.date_histogram.field` | Specify the date field used in date histogram aggregation. | `string` | No
`dimensions.date_histogram.timezone` | Specify the timezones as defined by the IANA Time Zone Database. The default is UTC. | `string` | No
`dimensions.terms` | Specify the term aggregations that you want to roll up. | `object` | No
`dimensions.terms.fields` | Specify terms aggregation for compatible fields. | `object` | No
`dimensions.histogram` | Specify the histogram aggregations that you want to roll up. | `object` | No
`dimensions.histogram.field` | Add a field for histogram aggregations. | `string` | Yes
`dimensions.histogram.interval` | Specify the histogram aggregation interval for the field. | `long` | Yes
`dimensions.metrics` | Specify a list of objects that represent the fields and metrics that you want to calculate. | `nested object` | No
`dimensions.metrics.field` | Specify the field that you want to perform metric aggregations on. | `string` | No
`dimensions.metrics.field.metrics` | Specify the metric aggregations you want to calculate for the field. | `multiple strings` | No
#### Sample response
```json
{
"_id": "rollup_id",
"_seqNo": 1,
"_primaryTerm": 1,
"rollup": { ... }
}
```
## Get an index rollup job
Returns all information about an index rollup job based on the `rollup_id`.
#### Request
```json
GET _plugins/_rollup/jobs/<rollup_id>
```
#### Sample response
```json
{
"_id": "my_rollup",
"_seqNo": 1,
"_primaryTerm": 1,
"rollup": { ... }
}
```
---
## Delete an index rollup job
Deletes an index rollup job based on the `rollup_id`.
#### Request
```json
DELETE _plugins/_rollup/jobs/<rollup_id>
```
#### Sample response
```json
200 OK
```
---
## Start or stop an index rollup job
Start or stop an index rollup job.
#### Request
```json
POST _plugins/_rollup/jobs/<rollup_id>/_start
POST _plugins/_rollup/jobs/<rollup_id>/_stop
```
#### Sample response
```json
200 OK
```
---
## Explain an index rollup job
Returns detailed metadata information about the index rollup job and its current progress.
#### Request
```json
GET _plugins/_rollup/jobs/<rollup_id>/_explain
```
#### Sample response
```json
{
"example_rollup": {
"rollup_id": "example_rollup",
"last_updated_time": 1602014281,
"continuous": {
"next_window_start_time": 1602055591,
"next_window_end_time": 1602075591
},
"status": "running",
"failure_reason": null,
"stats": {
"pages_processed": 342,
"documents_processed": 489359,
"rollups_indexed": 3420,
"index_time_in_ms": 30495,
"search_time_in_ms": 584922
}
}
}
```

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@ -1,156 +0,0 @@
---
layout: default
title: Index transforms
nav_order: 20
has_children: true
redirect_from: /docs/im/index-transforms/
has_toc: false
---
# Index transforms
Whereas index rollup jobs let you reduce data granularity by rolling up old data into condensed indices, transform jobs let you create a different, summarized view of your data centered around certain fields, so you can visualize or analyze the data in different ways.
For example, suppose that you have airline data that’s scattered across multiple fields and categories, and you want to view a summary of the data that’s organized by airline, quarter, and then price. You can use a transform job to create a new, summarized index that’s organized by those specific categories.
You can use transform jobs in two ways:
1. Use the OpenSearch Dashboards UI to specify the index you want to transform and any optional data filters you want to use to filter the original index. Then select the fields you want to transform and the aggregations to use in the transformation. Finally, define a schedule for your job to follow.
2. Use the transforms API to specify all the details about your job: the index you want to transform, target groups you want the transformed index to have, any aggregations you want to use to group columns, and a schedule for your job to follow.
OpenSearch Dashboards provides a detailed summary of the jobs you created and their relevant information, such as associated indices and job statuses. You can review and edit your job’s details and selections before creation, and even preview a transformed index’s data as you’re choosing which fields to transform. However, you can also use the REST API to create transform jobs and preview transform job results, but you must know all of the necessary settings and parameters to submit them as part of the HTTP request body. Submitting your transform job configurations as JSON scripts offers you more portability, allowing you to share and replicate your transform jobs, which is harder to do using OpenSearch Dashboards.
Your use cases will help you decide which method to use to create transform jobs.
## Create a transform job
If you don't have any data in your cluster, you can use the sample flight data within OpenSearch Dashboards to try out transform jobs. Otherwise, after launching OpenSearch Dashboards, choose **Index Management**. Select **Transform Jobs**, and choose **Create Transform Job**.
### Step 1: Choose indices
1. In the **Job name and description** section, specify a name and an optional description for your job.
2. In the **Indices** section, select the source and target index. You can either select an existing target index or create a new one by entering a name for your new index. If you want to transform just a subset of your source index, choose **Add Data Filter**, and use the OpenSearch query DSL to specify a subset of your source index. For more information about the OpenSearch query DSL, see [query DSL]({{site.url}}{{site.baseurl}}/opensearch/query-dsl/).
3. Choose **Next**.
### Step 2: Select fields to transform
After specifying the indices, you can select the fields you want to use in your transform job, as well as whether to use groupings or aggregations.
You can use groupings to place your data into separate buckets in your transformed index. For example, if you want to group all of the airport destinations within the sample flight data, you can group the `DestAirportID` field into a target field of `DestAirportID_terms` field, and you can find the grouped airport IDs in your transformed index after the transform job finishes.
On the other hand, aggregations let you perform simple calculations. For example, you can include an aggregation in your transform job to define a new field of `sum_of_total_ticket_price` that calculates the sum of all airplane tickets, and then analyze the newly summer data within your transformed index.
1. In the data table, select the fields you want to transform and expand the drop-down menu within the column header to choose the grouping or aggregation you want to use.
Currently, transform jobs support histogram, date_histogram, and terms groupings. For more information about groupings, see [Bucket Aggregations]({{site.url}}{{site.baseurl}}/opensearch/bucket-agg/). In terms of aggregations, you can select from `sum`, `avg`, `max`, `min`, `value_count`, `percentiles`, and `scripted_metric`. For more information about aggregations, see [Metric Aggregations]({{site.url}}{{site.baseurl}}/opensearch/metric-agg/).
2. Repeat step 1 for any other fields that you want to transform.
3. After selecting the fields that you want to transform and verifying the transformation, choose **Next**.
### Step 3: Specify a schedule
You can configure transform jobs to run once or multiple times on a schedule. Transform jobs are enabled by default.
1. For **transformation execution frequency**, select **Define by fixed interval** and specify a **transform interval**.
2. Under **Advanced**, specify an optional amount for **Pages per execution**. A larger number means more data is processed in each search request, but also uses more memory and causes higher latency. Exceeding allowed memory limits can cause exceptions and errors to occur.
3. Choose **Next**.
### Step 4: Review and confirm details
After confirming your transform job’s details are correct, choose **Create Transform Job**. If you want to edit any part of the job, choose **Edit** of the section you want to change, and make the necessary changes. You can’t change aggregations or groupings after creating a job.
### Step 5: Search through the transformed index.
Once the transform job finishes, you can use the `_search` API operation to search the target index.
```json
GET <target_index>/_search
```
For example, after running a transform job that transforms the flight data based on a `DestAirportID` field, you can run the following request that returns all of the fields that have a value of `SFO`.
**Sample Request**
```json
GET finished_flight_job/_search
{
"query": {
"match": {
"DestAirportID_terms" : "SFO"
}
}
}
```
**Sample Response**
```json
{
"took" : 3,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 4,
"relation" : "eq"
},
"max_score" : 3.845883,
"hits" : [
{
"_index" : "finished_flight_job",
"_type" : "_doc",
"_id" : "dSNKGb8U3OJOmC4RqVCi1Q",
"_score" : 3.845883,
"_source" : {
"transform._id" : "sample_flight_job",
"transform._doc_count" : 14,
"Carrier_terms" : "Dashboards Airlines",
"DestAirportID_terms" : "SFO"
}
},
{
"_index" : "finished_flight_job",
"_type" : "_doc",
"_id" : "_D7oqOy7drx9E-MG96U5RA",
"_score" : 3.845883,
"_source" : {
"transform._id" : "sample_flight_job",
"transform._doc_count" : 14,
"Carrier_terms" : "Logstash Airways",
"DestAirportID_terms" : "SFO"
}
},
{
"_index" : "finished_flight_job",
"_type" : "_doc",
"_id" : "YuZ8tOt1OsBA54e84WuAEw",
"_score" : 3.6988301,
"_source" : {
"transform._id" : "sample_flight_job",
"transform._doc_count" : 11,
"Carrier_terms" : "ES-Air",
"DestAirportID_terms" : "SFO"
}
},
{
"_index" : "finished_flight_job",
"_type" : "_doc",
"_id" : "W_-e7bVmH6eu8veJeK8ZxQ",
"_score" : 3.6988301,
"_source" : {
"transform._id" : "sample_flight_job",
"transform._doc_count" : 10,
"Carrier_terms" : "JetBeats",
"DestAirportID_terms" : "SFO"
}
}
]
}
}
```

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@ -1,713 +0,0 @@
---
layout: default
title: Transforms APIs
nav_order: 45
parent: Index transforms
has_toc: true
---
# Transforms APIs
Aside from using OpenSearch Dashboards, you can also use the REST API to create, start, stop, and complete other operations relative to transform jobs.
#### Table of contents
- TOC
{:toc}
## Create a transform job
Creates a transform job.
**Sample Request**
```json
PUT _plugins/_transform/<transform_id>
{
"transform": {
"enabled": true,
"schedule": {
"interval": {
"period": 1,
"unit": "Minutes",
"start_time": 1602100553
}
},
"description": "Sample transform job",
"source_index": "sample_index",
"target_index": "sample_target",
"data_selection_query": {
"match_all": {}
},
"page_size": 1,
"groups": [
{
"terms": {
"source_field": "customer_gender",
"target_field": "gender"
}
},
{
"terms": {
"source_field": "day_of_week",
"target_field": "day"
}
}
],
"aggregations": {
"quantity": {
"sum": {
"field": "total_quantity"
}
}
}
}
}
```
**Sample Response**
```json
{
"_id": "sample",
"_version": 7,
"_seq_no": 13,
"_primary_term": 1,
"transform": {
"transform_id": "sample",
"schema_version": 7,
"schedule": {
"interval": {
"start_time": 1621467964243,
"period": 1,
"unit": "Minutes"
}
},
"metadata_id": null,
"updated_at": 1621467964243,
"enabled": true,
"enabled_at": 1621467964243,
"description": "Sample transform job",
"source_index": "sample_index",
"data_selection_query": {
"match_all": {
"boost": 1.0
}
},
"target_index": "sample_target",
"roles": [],
"page_size": 1,
"groups": [
{
"terms": {
"source_field": "customer_gender",
"target_field": "gender"
}
},
{
"terms": {
"source_field": "day_of_week",
"target_field": "day"
}
}
],
"aggregations": {
"quantity": {
"sum": {
"field": "total_quantity"
}
}
}
}
}
```
You can specify the following options in the HTTP request body:
Option | Data Type | Description | Required
:--- | :--- | :--- | :---
enabled | Boolean | If true, the transform job is enabled at creation. | No
schedule | JSON | The schedule the transform job runs on. | Yes
start_time | Integer | The Unix epoch time of the transform job's start time. | Yes
description | String | Describes the transform job. | No
metadata_id | String | Any metadata to be associated with the transform job. | No
source_index | String | The source index whose data to transform. | Yes
target_index | String | The target index the newly transformed data is added into. You can create a new index or update an existing one. | Yes
data_selection_query | JSON | The query DSL to use to filter a subset of the source index for the transform job. See [query DSL]({{site.url}}{{site.baseurl}}/opensearch/query-dsl) for more information. | Yes
page_size | Integer | The number of fields to transform at a time. Higher number means higher performance but requires more memory and can cause higher latency. (Default: 1) | Yes
groups | Array | Specifies the grouping(s) to use in the transform job. Supported groups are `terms`, `histogram`, and `date_histogram`. For more information, see [Bucket Aggregations]({{site.url}}{{site.baseurl}}/opensearch/bucket-agg). | Yes if not using aggregations
source_field | String | The field(s) to transform | Yes
aggregations | JSON | The aggregations to use in the transform job. Supported aggregations are: `sum`, `max`, `min`, `value_count`, `avg`, `scripted_metric`, and `percentiles`. For more information, see [Metric Aggregations]({{site.url}}{{site.baseurl}}/opensearch/metric-agg). | Yes if not using groups
## Update a transform job
Updates a transform job if `transform_id` already exists.
**Sample Request**
```json
PUT _plugins/_transform/<transform_id>
{
"transform": {
"enabled": true,
"schedule": {
"interval": {
"period": 1,
"unit": "Minutes",
"start_time": 1602100553
}
},
"description": "Sample transform job",
"source_index": "sample_index",
"target_index": "sample_target",
"data_selection_query": {
"match_all": {}
},
"page_size": 1,
"groups": [
{
"terms": {
"source_field": "customer_gender",
"target_field": "gender"
}
},
{
"terms": {
"source_field": "day_of_week",
"target_field": "day"
}
}
],
"aggregations": {
"quantity": {
"sum": {
"field": "total_quantity"
}
}
}
}
}
```
**Sample Response**
```json
{
"_id": "sample",
"_version": 2,
"_seq_no": 14,
"_primary_term": 1,
"transform": {
"transform_id": "sample",
"schema_version": 7,
"schedule": {
"interval": {
"start_time": 1602100553,
"period": 1,
"unit": "Minutes"
}
},
"metadata_id": null,
"updated_at": 1621889843874,
"enabled": true,
"enabled_at": 1621889843874,
"description": "Sample transform job",
"source_index": "sample_index",
"data_selection_query": {
"match_all": {
"boost": 1.0
}
},
"target_index": "sample_target",
"roles": [],
"page_size": 1,
"groups": [
{
"terms": {
"source_field": "customer_gender",
"target_field": "gender"
}
},
{
"terms": {
"source_field": "day_of_week",
"target_field": "day"
}
}
],
"aggregations": {
"quantity": {
"sum": {
"field": "total_quantity"
}
}
}
}
}
```
The `Update` operation supports the following URL parameters:
Parameter | Description | Required
:---| :--- | :---
`if_seq_no` | Only perform the transform operation if the last operation that changed the transform job has the specified sequence number. | No
`if_primary_term` | Only perform the transform operation if the last operation that changed the transform job has the specified sequence term. | No
## Get a transform job's details
Returns a transform job's details.
**Sample Request**
```json
GET _plugins/_transform/<transform_id>
```
**Sample Response**
```json
{
"_id": "sample",
"_version": 7,
"_seq_no": 13,
"_primary_term": 1,
"transform": {
"transform_id": "sample",
"schema_version": 7,
"schedule": {
"interval": {
"start_time": 1621467964243,
"period": 1,
"unit": "Minutes"
}
},
"metadata_id": null,
"updated_at": 1621467964243,
"enabled": true,
"enabled_at": 1621467964243,
"description": "Sample transform job",
"source_index": "sample_index",
"data_selection_query": {
"match_all": {
"boost": 1.0
}
},
"target_index": "sample_target",
"roles": [],
"page_size": 1,
"groups": [
{
"terms": {
"source_field": "customer_gender",
"target_field": "gender"
}
},
{
"terms": {
"source_field": "day_of_week",
"target_field": "day"
}
}
],
"aggregations": {
"quantity": {
"sum": {
"field": "total_quantity"
}
}
}
}
}
```
You can also get details of all transform jobs by omitting `transform_id`.
**Sample Request**
```json
GET _plugins/_transform/
```
**Sample Response**
```json
{
"total_transforms": 1,
"transforms": [
{
"_id": "sample",
"_seq_no": 13,
"_primary_term": 1,
"transform": {
"transform_id": "sample",
"schema_version": 7,
"schedule": {
"interval": {
"start_time": 1621467964243,
"period": 1,
"unit": "Minutes"
}
},
"metadata_id": null,
"updated_at": 1621467964243,
"enabled": true,
"enabled_at": 1621467964243,
"description": "Sample transform job",
"source_index": "sample_index",
"data_selection_query": {
"match_all": {
"boost": 1.0
}
},
"target_index": "sample_target",
"roles": [],
"page_size": 1,
"groups": [
{
"terms": {
"source_field": "customer_gender",
"target_field": "gender"
}
},
{
"terms": {
"source_field": "day_of_week",
"target_field": "day"
}
}
],
"aggregations": {
"quantity": {
"sum": {
"field": "total_quantity"
}
}
}
}
}
]
}
```
You can specify these options as the `GET` API operation’s URL parameters to filter results:
Parameter | Description | Required
:--- | :--- | :---
from | The starting index to search from. (Default: 0) | No
size | Specifies the amount of results to return (Default: 10) | No
search |The search term to use to filter results. | No
sortField | The field to sort results with. | No
sortDirection | Specifies the direction to sort results in. Can be `ASC` or `DESC`. (Default: ASC) | No
For example, this request returns two results starting from the eighth index.
**Sample Request**
```json
GET _plugins/_transform?size=2&from=8
```
**Sample Response**
```json
{
"total_transforms": 18,
"transforms": [
{
"_id": "sample8",
"_seq_no": 93,
"_primary_term": 1,
"transform": {
"transform_id": "sample8",
"schema_version": 7,
"schedule": {
"interval": {
"start_time": 1622063596812,
"period": 1,
"unit": "Minutes"
}
},
"metadata_id": "y4hFAB2ZURQ2dzY7BAMxWA",
"updated_at": 1622063657233,
"enabled": false,
"enabled_at": null,
"description": "Sample transform job",
"source_index": "sample_index3",
"data_selection_query": {
"match_all": {
"boost": 1.0
}
},
"target_index": "sample_target3",
"roles": [],
"page_size": 1,
"groups": [
{
"terms": {
"source_field": "customer_gender",
"target_field": "gender"
}
},
{
"terms": {
"source_field": "day_of_week",
"target_field": "day"
}
}
],
"aggregations": {
"quantity": {
"sum": {
"field": "total_quantity"
}
}
}
}
},
{
"_id": "sample9",
"_seq_no": 98,
"_primary_term": 1,
"transform": {
"transform_id": "sample9",
"schema_version": 7,
"schedule": {
"interval": {
"start_time": 1622063598065,
"period": 1,
"unit": "Minutes"
}
},
"metadata_id": "x8tCIiYMTE3veSbIJkit5A",
"updated_at": 1622063658388,
"enabled": false,
"enabled_at": null,
"description": "Sample transform job",
"source_index": "sample_index4",
"data_selection_query": {
"match_all": {
"boost": 1.0
}
},
"target_index": "sample_target4",
"roles": [],
"page_size": 1,
"groups": [
{
"terms": {
"source_field": "customer_gender",
"target_field": "gender"
}
},
{
"terms": {
"source_field": "day_of_week",
"target_field": "day"
}
}
],
"aggregations": {
"quantity": {
"sum": {
"field": "total_quantity"
}
}
}
}
}
]
}
```
## Start a transform job
Transform jobs created using the API are automatically enabled, but if you ever need to enable a job, you can use the `start` API operation.
**Sample Request**
```json
POST _plugins/_transform/<transform_id>/_start
```
**Sample Response**
```json
{
"acknowledged": true
}
```
## Stop a transform job
Stops/disables a transform job.
**Sample Request**
```json
POST _plugins/_transform/<transform_id>/_stop
```
**Sample Response**
```json
{
"acknowledged": true
}
```
## Get the status of a transform job
Returns the status and metadata of a transform job.
**Sample Request**
```json
GET _plugins/_transform/<transform_id>/_explain
```
**Sample Response**
```json
{
"sample": {
"metadata_id": "PzmjweME5xbgkenl9UpsYw",
"transform_metadata": {
"transform_id": "sample",
"last_updated_at": 1621883525873,
"status": "finished",
"failure_reason": "null",
"stats": {
"pages_processed": 0,
"documents_processed": 0,
"documents_indexed": 0,
"index_time_in_millis": 0,
"search_time_in_millis": 0
}
}
}
}
```
## Preview a transform job's results
Returns a preview of what a transformed index would look like.
**Sample Request**
```json
POST _plugins/_transform/_preview
{
"transform": {
"enabled": false,
"schedule": {
"interval": {
"period": 1,
"unit": "Minutes",
"start_time": 1602100553
}
},
"description": "test transform",
"source_index": "sample_index",
"target_index": "sample_target",
"data_selection_query": {
"match_all": {}
},
"page_size": 10,
"groups": [
{
"terms": {
"source_field": "customer_gender",
"target_field": "gender"
}
},
{
"terms": {
"source_field": "day_of_week",
"target_field": "day"
}
}
],
"aggregations": {
"quantity": {
"sum": {
"field": "total_quantity"
}
}
}
}
}
```
**Sample Response**
```json
{
"documents" : [
{
"quantity" : 862.0,
"gender" : "FEMALE",
"day" : "Friday"
},
{
"quantity" : 682.0,
"gender" : "FEMALE",
"day" : "Monday"
},
{
"quantity" : 772.0,
"gender" : "FEMALE",
"day" : "Saturday"
},
{
"quantity" : 669.0,
"gender" : "FEMALE",
"day" : "Sunday"
},
{
"quantity" : 887.0,
"gender" : "FEMALE",
"day" : "Thursday"
}
]
}
```
## Delete a transform job
Deletes a transform job. This operation does not delete the source or target indices.
**Sample Request**
```json
DELETE _plugins/_transform/<transform_id>
```
**Sample Response**
```json
{
"took": 205,
"errors": false,
"items": [
{
"delete": {
"_index": ".opensearch-ism-config",
"_type": "_doc",
"_id": "sample",
"_version": 4,
"result": "deleted",
"forced_refresh": true,
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"_seq_no": 6,
"_primary_term": 1,
"status": 200
}
}
]
}
```

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@ -1,14 +0,0 @@
---
layout: default
title: About Index Management
nav_order: 1
has_children: false
redirect_from:
- /im-plugin/
---
# About Index Management
OpenSearch Dashboards
{: .label .label-yellow :}
The Index Management (IM) plugin lets you automate recurring index management activities and reduce storage costs.

492
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@ -1,492 +0,0 @@
---
layout: default
title: ISM API
parent: Index State Management
redirect_from: /docs/ism/api/
nav_order: 5
---
# ISM API
Use the index state management operations to programmatically work with policies and managed indices.
---
#### Table of contents
- TOC
{:toc}
---
## Create policy
Creates a policy.
#### Request
```json
PUT _plugins/_ism/policies/policy_1
{
"policy": {
"description": "ingesting logs",
"default_state": "ingest",
"states": [
{
"name": "ingest",
"actions": [
{
"rollover": {
"min_doc_count": 5
}
}
],
"transitions": [
{
"state_name": "search"
}
]
},
{
"name": "search",
"actions": [],
"transitions": [
{
"state_name": "delete",
"conditions": {
"min_index_age": "5m"
}
}
]
},
{
"name": "delete",
"actions": [
{
"delete": {}
}
],
"transitions": []
}
]
}
}
```
#### Sample response
```json
{
"_id": "policy_1",
"_version": 1,
"_primary_term": 1,
"_seq_no": 7,
"policy": {
"policy": {
"policy_id": "policy_1",
"description": "ingesting logs",
"last_updated_time": 1577990761311,
"schema_version": 1,
"error_notification": null,
"default_state": "ingest",
"states": [
{
"name": "ingest",
"actions": [
{
"rollover": {
"min_doc_count": 5
}
}
],
"transitions": [
{
"state_name": "search"
}
]
},
{
"name": "search",
"actions": [],
"transitions": [
{
"state_name": "delete",
"conditions": {
"min_index_age": "5m"
}
}
]
},
{
"name": "delete",
"actions": [
{
"delete": {}
}
],
"transitions": []
}
]
}
}
}
```
---
## Add policy
Adds a policy to an index. This operation does not change the policy if the index already has one.
#### Request
```json
POST _plugins/_ism/add/index_1
{
"policy_id": "policy_1"
}
```
#### Sample response
```json
{
"updated_indices": 1,
"failures": false,
"failed_indices": []
}
```
---
## Update policy
Updates a policy. Use the `seq_no` and `primary_term` parameters to update an existing policy. If these numbers don't match the existing policy or the policy doesn't exist, ISM throws an error.
#### Request
```json
PUT _plugins/_ism/policies/policy_1?if_seq_no=7&if_primary_term=1
{
"policy": {
"description": "ingesting logs",
"default_state": "ingest",
"states": [
{
"name": "ingest",
"actions": [
{
"rollover": {
"min_doc_count": 5
}
}
],
"transitions": [
{
"state_name": "search"
}
]
},
{
"name": "search",
"actions": [],
"transitions": [
{
"state_name": "delete",
"conditions": {
"min_index_age": "5m"
}
}
]
},
{
"name": "delete",
"actions": [
{
"delete": {}
}
],
"transitions": []
}
]
}
}
```
#### Sample response
```json
{
"_id": "policy_1",
"_version": 2,
"_primary_term": 1,
"_seq_no": 10,
"policy": {
"policy": {
"policy_id": "policy_1",
"description": "ingesting logs",
"last_updated_time": 1577990934044,
"schema_version": 1,
"error_notification": null,
"default_state": "ingest",
"states": [
{
"name": "ingest",
"actions": [
{
"rollover": {
"min_doc_count": 5
}
}
],
"transitions": [
{
"state_name": "search"
}
]
},
{
"name": "search",
"actions": [],
"transitions": [
{
"state_name": "delete",
"conditions": {
"min_index_age": "5m"
}
}
]
},
{
"name": "delete",
"actions": [
{
"delete": {}
}
],
"transitions": []
}
]
}
}
}
```
---
## Get policy
Gets the policy by `policy_id`.
#### Request
```json
GET _plugins/_ism/policies/policy_1
```
#### Sample response
```json
{
"_id": "policy_1",
"_version": 2,
"_seq_no": 10,
"_primary_term": 1,
"policy": {
"policy_id": "policy_1",
"description": "ingesting logs",
"last_updated_time": 1577990934044,
"schema_version": 1,
"error_notification": null,
"default_state": "ingest",
"states": [
{
"name": "ingest",
"actions": [
{
"rollover": {
"min_doc_count": 5
}
}
],
"transitions": [
{
"state_name": "search"
}
]
},
{
"name": "search",
"actions": [],
"transitions": [
{
"state_name": "delete",
"conditions": {
"min_index_age": "5m"
}
}
]
},
{
"name": "delete",
"actions": [
{
"delete": {}
}
],
"transitions": []
}
]
}
}
```
---
## Remove policy from index
Removes any ISM policy from the index.
#### Request
```json
POST _plugins/_ism/remove/index_1
```
#### Sample response
```json
{
"updated_indices": 1,
"failures": false,
"failed_indices": []
}
```
---
## Update managed index policy
Updates the managed index policy to a new policy (or to a new version of the policy). You can use an index pattern to update multiple indices at once. When updating multiple indices, you might want to include a state filter to only affect certain managed indices. The change policy filters out all the existing managed indices and only applies the change to the ones in the state that you specify. You can also explicitly specify the state that the managed index transitions to after the change policy takes effect.
A policy change is an asynchronous background process. The changes are queued and are not executed immediately by the background process. This delay in execution protects the currently running managed indices from being put into a broken state. If the policy you are changing to has only some small configuration changes, then the change takes place immediately. For example, if the policy changes the `min_index_age` parameter in a rollover condition from `1000d` to `100d`, this change takes place immediately in its next execution. If the change modifies the state, actions, or the order of actions of the current state the index is in, then the change happens at the end of its current state before transitioning to a new state.
In this example, the policy applied on the `index_1` index is changed to `policy_1`, which could either be a completely new policy or an updated version of its existing policy. The process only applies the change if the index is currently in the `searches` state. After this change in policy takes place, `index_1` transitions to the `delete` state.
#### Request
```json
POST _plugins/_ism/change_policy/index_1
{
"policy_id": "policy_1",
"state": "delete",
"include": [
{
"state": "searches"
}
]
}
```
#### Sample response
```json
{
"updated_indices": 0,
"failures": false,
"failed_indices": []
}
```
---
## Retry failed index
Retries the failed action for an index. For the retry call to succeed, ISM must manage the index, and the index must be in a failed state. You can use index patterns (`*`) to retry multiple failed indices.
#### Request
```json
POST _plugins/_ism/retry/index_1
{
"state": "delete"
}
```
#### Sample response
```json
{
"updated_indices": 0,
"failures": false,
"failed_indices": []
}
```
---
## Explain index
Gets the current state of the index. You can use index patterns to get the status of multiple indices.
#### Request
```json
GET _plugins/_ism/explain/index_1
```
#### Sample response
```json
{
"index_1": {
"index.plugins.index_state_management.policy_id": "policy_1"
}
}
```
The `plugins.index_state_management.policy_id` setting is deprecated starting from ODFE version 1.13.0. We retain this field in the response API for consistency.
---
## Delete policy
Deletes the policy by `policy_id`.
#### Request
```json
DELETE _plugins/_ism/policies/policy_1
```
#### Sample response
```json
{
"_index": ".opendistro-ism-config",
"_type": "_doc",
"_id": "policy_1",
"_version": 3,
"result": "deleted",
"forced_refresh": true,
"_shards": {
"total": 2,
"successful": 2,
"failed": 0
},
"_seq_no": 15,
"_primary_term": 1
}
```

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@ -1,104 +0,0 @@
---
layout: default
title: Index State Management
nav_order: 3
has_children: true
redirect_from:
- /docs/im/ism/
- /docs/ism/
has_toc: false
---
# Index State Management
OpenSearch Dashboards
{: .label .label-yellow :}
If you analyze time-series data, you likely prioritize new data over old data. You might periodically perform certain operations on older indices, such as reducing replica count or deleting them.
Index State Management (ISM) is a plugin that lets you automate these periodic, administrative operations by triggering them based on changes in the index age, index size, or number of documents. Using the ISM plugin, you can define *policies* that automatically handle index rollovers or deletions to fit your use case.
For example, you can define a policy that moves your index into a `read_only` state after 30 days and then deletes it after a set period of 90 days. You can also set up the policy to send you a notification message when the index is deleted.
You might want to perform an index rollover after a certain amount of time or run a `force_merge` operation on an index during off-peak hours to improve search performance during peak hours.
To use the ISM plugin, your user role needs to be mapped to the `all_access` role that gives you full access to the cluster. To learn more, see [Users and roles]({{site.url}}{{site.baseurl}}/security-plugin/access-control/users-roles/).
{: .note }
## Get started with ISM
To get started, choose **Index Management** in OpenSearch Dashboards.
### Step 1: Set up policies
A policy is a set of rules that describes how an index should be managed. For information about creating a policy, see [Policies]({{site.url}}{{site.baseurl}}/im-plugin/ism/policies/).
1. Choose the **Index Policies** tab.
2. Choose **Create policy**.
3. In the **Name policy** section, enter a policy ID.
4. In the **Define policy** section, enter your policy.
5. Choose **Create**.
After you create a policy, your next step is to attach this policy to an index or indices.
You can set up an `ism_template` in the policy so when you create an index that matches the ISM template pattern, the index will have this policy attached to it:
```json
PUT _plugins/_ism/policies/policy_id
{
"policy": {
"description": "Example policy.",
"default_state": "...",
"states": [...],
"ism_template": {
"index_patterns": ["index_name-*"],
"priority": 100
}
}
}
```
For an example ISM template policy, see [Sample policy with ISM template]({{site.url}}{{site.baseurl}}/im-plugin/ism/policies#sample-policy-with-ism-template).
Older versions of the plugin include the `policy_id` in an index template, so when an index is created that matches the index template pattern, the index will have the policy attached to it:
```json
PUT _index_template/<template_name>
{
"index_patterns": [
"index_name-*"
],
"template": {
"settings": {
"opendistro.index_state_management.policy_id": "policy_id"
}
}
}
```
The `opendistro.index_state_management.policy_id` setting is deprecated. You can continue to automatically manage newly created indices with the ISM template field.
{: .note }
### Step 2: Attach policies to indices
1. Choose **Indices**.
2. Choose the index or indices that you want to attach your policy to.
3. Choose **Apply policy**.
4. From the **Policy ID** menu, choose the policy that you created.
You can see a preview of your policy.
5. If your policy includes a rollover operation, specify a rollover alias.
Make sure that the alias that you enter already exists. For more information about the rollover operation, see [rollover]({{site.url}}{{site.baseurl}}/im-plugin/ism/policies#rollover).
6. Choose **Apply**.
After you attach a policy to an index, ISM creates a job that runs every 5 minutes by default to perform policy actions, check conditions, and transition the index into different states. To change the default time interval for this job, see [Settings]({{site.url}}{{site.baseurl}}/im-plugin/ism/settings/).
If you want to use an OpenSearch operation to create an index with a policy already attached to it, see [create index]({{site.url}}{{site.baseurl}}/im-plugin/ism/api#create-index).
### Step 3: Manage indices
1. Choose **Managed Indices**.
2. To change your policy, see [Change Policy]({{site.url}}{{site.baseurl}}/im-plugin/ism/managedindices#change-policy).
3. To attach a rollover alias to your index, select your policy and choose **Add rollover alias**.
Make sure that the alias that you enter already exists. For more information about the rollover operation, see [rollover]({{site.url}}{{site.baseurl}}/im-plugin/ism/policies#rollover).
4. To remove a policy, choose your policy, and then choose **Remove policy**.
5. To retry a policy, choose your policy, and then choose **Retry policy**.
For information about managing your policies, see [Managed Indices]({{site.url}}{{site.baseurl}}/im-plugin/ism/managedindices/).

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---
layout: default
title: Managed Indices
nav_order: 3
parent: Index State Management
redirect_from: /docs/ism/managedindices/
has_children: false
---
# Managed indices
You can change or update a policy using the managed index operations.
This table lists the fields of managed index operations.
Parameter | Description | Type | Required | Read Only
:--- | :--- |:--- |:--- |
`name` | The name of the managed index policy. | `string` | Yes | No
`index` | The name of the managed index that this policy is managing. | `string` | Yes | No
`index_uuid` | The uuid of the index. | `string` | Yes | No
`enabled` | When `true`, the managed index is scheduled and run by the scheduler. | `boolean` | Yes | No
`enabled_time` | The time the managed index was last enabled. If the managed index process is disabled, then this is null. | `timestamp` | Yes | Yes
`last_updated_time` | The time the managed index was last updated. | `timestamp` | Yes | Yes
`schedule` | The schedule of the managed index job. | `object` | Yes | No
`policy_id` | The name of the policy used by this managed index. | `string` | Yes | No
`policy_seq_no` | The sequence number of the policy used by this managed index. | `number` | Yes | No
`policy_primary_term` | The primary term of the policy used by this managed index. | `number` | Yes | No
`policy_version` | The version of the policy used by this managed index. | `number` | Yes | Yes
`policy` | The cached JSON of the policy for the `policy_version` that's used during runs. If the policy is null, it means that this is the first execution of the job and the latest policy document is read in/saved. | `object` | No | No
`change_policy` | The information regarding what policy and state to change to. | `object` | No | No
`policy_name` | The name of the policy to update to. To update to the latest version, set this to be the same as the current `policy_name`. | `string` | No | Yes
`state` | The state of the managed index after it finishes updating. If no state is specified, it's assumed that the policy structure did not change. | `string` | No | Yes
The following example shows a managed index policy:
```json
{
"managed_index": {
"name": "my_index",
"index": "my_index",
"index_uuid": "sOKSOfkdsoSKeofjIS",
"enabled": true,
"enabled_time": 1553112384,
"last_updated_time": 1553112384,
"schedule": {
"interval": {
"period": 1,
"unit": "MINUTES",
"start_time": 1553112384
}
},
"policy_id": "log_rotation",
"policy_version": 1,
"policy": {...},
"change_policy": null
}
}
```
## Change policy
You can change any managed index policy, but ISM has a few constraints in place to make sure that policy changes don't break indices.
If an index is stuck in its current state, never proceeding, and you want to update its policy immediately, make sure that the new policy includes the same state---same name, same actions, same order---as the old policy. In this case, even if the policy is in the middle of executing an action, ISM applies the new policy.
If you update the policy without including an identical state, ISM updates the policy only after all actions in the current state finish executing. Alternately, you can choose a specific state in your old policy after which you want the new policy to take effect.
To change a policy using OpenSearch Dashboards, do the following:
- Under **Managed indices**, choose the indices that you want to attach the new policy to.
- To attach the new policy to indices in specific states, choose **Choose state filters**, and then choose those states.
- Under **Choose New Policy**, choose the new policy.
- To start the new policy for indices in the current state, choose **Keep indices in their current state after the policy takes effect**.
- To start the new policy in a specific state, choose **Start from a chosen state after changing policies**, and then choose the default start state in your new policy.

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@ -1,665 +0,0 @@
---
layout: default
title: Policies
nav_order: 1
parent: Index State Management
redirect_from: /docs/ism/policies/
has_children: false
---
# Policies
Policies are JSON documents that define the following:
- The *states* that an index can be in, including the default state for new indices. For example, you might name your states "hot," "warm," "delete," and so on. For more information, see [States](#states).
- Any *actions* that you want the plugin to take when an index enters a state, such as performing a rollover. For more information, see [Actions](#actions).
- The conditions that must be met for an index to move into a new state, known as *transitions*. For example, if an index is more than eight weeks old, you might want to move it to the "delete" state. For more information, see [Transitions](#transitions).
In other words, a policy defines the *states* that an index can be in, the *actions* to perform when in a state, and the conditions that must be met to *transition* between states.
You have complete flexibility in the way you can design your policies. You can create any state, transition to any other state, and specify any number of actions in each state.
This table lists the relevant fields of a policy.
Field | Description | Type | Required | Read Only
:--- | :--- |:--- |:--- |
`policy_id` | The name of the policy. | `string` | Yes | Yes
`description` | A human-readable description of the policy. | `string` | Yes | No
`ism_template` | Specify an ISM template pattern that matches the index to apply the policy. | `nested list of objects` | No | No
`last_updated_time` | The time the policy was last updated. | `timestamp` | Yes | Yes
`error_notification` | The destination and message template for error notifications. The destination could be Amazon Chime, Slack, or a webhook URL. | `object` | No | No
`default_state` | The default starting state for each index that uses this policy. | `string` | Yes | No
`states` | The states that you define in the policy. | `nested list of objects` | Yes | No
---
#### Table of contents
1. TOC
{:toc}
---
## States
A state is the description of the status that the managed index is currently in. A managed index can be in only one state at a time. Each state has associated actions that are executed sequentially on entering a state and transitions that are checked after all the actions have been completed.
This table lists the parameters that you can define for a state.
Field | Description | Type | Required
:--- | :--- |:--- |:--- |
`name` | The name of the state. | `string` | Yes
`actions` | The actions to execute after entering a state. For more information, see [Actions](#actions). | `nested list of objects` | Yes
`transitions` | The next states and the conditions required to transition to those states. If no transitions exist, the policy assumes that it's complete and can now stop managing the index. For more information, see [Transitions](#transitions). | `nested list of objects` | Yes
---
## Actions
Actions are the steps that the policy sequentially executes on entering a specific state.
They are executed in the order in which they are defined.
This table lists the parameters that you can define for an action.
Parameter | Description | Type | Required | Default
:--- | :--- |:--- |:--- |
`timeout` | The timeout period for the action. Accepts time units for minutes, hours, and days. | `time unit` | No | -
`retry` | The retry configuration for the action. | `object` | No | Specific to action
The `retry` operation has the following parameters:
Parameter | Description | Type | Required | Default
:--- | :--- |:--- |:--- |
`count` | The number of retry counts. | `number` | Yes | -
`backoff` | The backoff policy type to use when retrying. | `string` | No | Exponential
`delay` | The time to wait between retries. Accepts time units for minutes, hours, and days. | `time unit` | No | 1 minute
The following example action has a timeout period of one hour. The policy retries this action three times with an exponential backoff policy, with a delay of 10 minutes between each retry:
```json
"actions": {
"timeout": "1h",
"retry": {
"count": 3,
"backoff": "exponential",
"delay": "10m"
}
}
```
For a list of available unit types, see [Supported units]({{site.url}}{{site.baseurl}}/opensearch/units/).
## ISM supported operations
ISM supports the following operations:
- [force_merge](#forcemerge)
- [read_only](#read_only)
- [read_write](#read_write)
- [replica_count](#replica_count)
- [close](#close)
- [open](#open)
- [delete](#delete)
- [rollover](#rollover)
- [notification](#notification)
- [snapshot](#snapshot)
- [index_priority](#index_priority)
- [allocation](#allocation)
### force_merge
Reduces the number of Lucene segments by merging the segments of individual shards. This operation attempts to set the index to a `read-only` state before starting the merging process.
Parameter | Description | Type | Required
:--- | :--- |:--- |:--- |
`max_num_segments` | The number of segments to reduce the shard to. | `number` | Yes
```json
{
"force_merge": {
"max_num_segments": 1
}
}
```
### read_only
Sets a managed index to be read only.
```json
{
"read_only": {}
}
```
### read_write
Sets a managed index to be writeable.
```json
{
"read_write": {}
}
```
### replica_count
Sets the number of replicas to assign to an index.
Parameter | Description | Type | Required
:--- | :--- |:--- |:--- |
`number_of_replicas` | Defines the number of replicas to assign to an index. | `number` | Yes
```json
{
"replica_count": {
"number_of_replicas": 2
}
}
```
For information about setting replicas, see [Primary and replica shards]({{site.url}}{{site.baseurl}}/opensearch#primary-and-replica-shards).
### close
Closes the managed index.
```json
{
"close": {}
}
```
Closed indices remain on disk, but consume no CPU or memory. You can't read from, write to, or search closed indices.
Closing an index is a good option if you need to retain data for longer than you need to actively search it and have sufficient disk space on your data nodes. If you need to search the data again, reopening a closed index is simpler than restoring an index from a snapshot.
### open
Opens a managed index.
```json
{
"open": {}
}
```
### delete
Deletes a managed index.
```json
{
"delete": {}
}
```
### rollover
Rolls an alias over to a new index when the managed index meets one of the rollover conditions.
The index format must match the pattern: `^.*-\d+$`. For example, `(logs-000001)`.
Set `index.plugins.index_state_management.rollover_alias` as the alias to rollover.
Parameter | Description | Type | Example | Required
:--- | :--- |:--- |:--- |
`min_size` | The minimum size of the total primary shard storage (not counting replicas) required to roll over the index. For example, if you set `min_size` to 100 GiB and your index has 5 primary shards and 5 replica shards of 20 GiB each, the total size of the primaries is 100 GiB, so the rollover occurs. ISM doesn't check indices continually, so it doesn't roll over indices at exactly 100 GiB. Instead, if an index is continuously growing, ISM might check it at 99 GiB, not perform the rollover, check again when the shards reach 105 GiB, and then perform the operation. | `string` | `20gb` or `5mb` | No
`min_doc_count` | The minimum number of documents required to roll over the index. | `number` | `2000000` | No
`min_index_age` | The minimum age required to roll over the index. Index age is the time between its creation and the present. | `string` | `5d` or `7h` | No
```json
{
"rollover": {
"min_size": "50gb"
}
}
```
```json
{
"rollover": {
"min_doc_count": 100000000
}
}
```
```json
{
"rollover": {
"min_index_age": "30d"
}
}
```
### notification
Sends you a notification.
Parameter | Description | Type | Required
:--- | :--- |:--- |:--- |
`destination` | The destination URL. | `Slack, Amazon Chime, or webhook URL` | Yes
`message_template` | The text of the message. You can add variables to your messages using [Mustache templates](https://mustache.github.io/mustache.5.html). | `object` | Yes
The destination system **must** return a response otherwise the notification operation throws an error.
#### Example 1: Chime notification
```json
{
"notification": {
"destination": {
"chime": {
"url": "<url>"
}
},
"message_template": {
"source": "the index is {% raw %}{{ctx.index}}{% endraw %}"
}
}
}
```
#### Example 2: Custom webhook notification
```json
{
"notification": {
"destination": {
"custom_webhook": {
"url": "https://<your_webhook>"
}
},
"message_template": {
"source": "the index is {% raw %}{{ctx.index}}{% endraw %}"
}
}
}
```
#### Example 3: Slack notification
```json
{
"notification": {
"destination": {
"slack": {
"url": "https://hooks.slack.com/services/xxx/xxxxxx"
}
},
"message_template": {
"source": "the index is {% raw %}{{ctx.index}}{% endraw %}"
}
}
}
```
You can use `ctx` variables in your message to represent a number of policy parameters based on the past executions of your policy. For example, if your policy has a rollover action, you can use `{% raw %}{{ctx.action.name}}{% endraw %}` in your message to represent the name of the rollover.
The following `ctx` variable options are available for every policy:
#### Guaranteed variables
Parameter | Description | Type
:--- | :--- |:--- |:--- |
`index` | The name of the index. | `string`
`index_uuid` | The uuid of the index. | `string`
`policy_id` | The name of the policy. | `string`
### snapshot
Backup your cluster’s indices and state. For more information about snapshots, see [Take and restore snapshots]({{site.url}}{{site.baseurl}}/opensearch/snapshot-restore/).
The `snapshot` operation has the following parameters:
Parameter | Description | Type | Required | Default
:--- | :--- |:--- |:--- |
`repository` | The repository name that you register through the native snapshot API operations. | `string` | Yes | -
`snapshot` | The name of the snapshot. | `string` | Yes | -
```json
{
"snapshot": {
"repository": "my_backup",
"snapshot": "my_snapshot"
}
}
```
### index_priority
Set the priority for the index in a specific state. Unallocated shards of indices are recovered in the order of their priority, whenever possible. The indices with higher priority values are recovered first followed by the indices with lower priority values.
The `index_priority` operation has the following parameter:
Parameter | Description | Type | Required | Default
:--- | :--- |:--- |:--- |:---
`priority` | The priority for the index as soon as it enters a state. | `number` | Yes | 1
```json
"actions": [
{
"index_priority": {
"priority": 50
}
}
]
```
### allocation
Allocate the index to a node with a specific attribute.
For example, setting `require` to `warm` moves your data only to "warm" nodes.
The `allocation` operation has the following parameters:
Parameter | Description | Type | Required
:--- | :--- |:--- |:---
`require` | Allocate the index to a node with a specified attribute. | `string` | Yes
`include` | Allocate the index to a node with any of the specified attributes. | `string` | Yes
`exclude` | Don’t allocate the index to a node with any of the specified attributes. | `string` | Yes
`wait_for` | Wait for the policy to execute before allocating the index to a node with a specified attribute. | `string` | Yes
```json
"actions": [
{
"allocation": {
"require": { "box_type": "warm" }
}
}
]
```
---
## Transitions
Transitions define the conditions that need to be met for a state to change. After all actions in the current state are completed, the policy starts checking the conditions for transitions.
Transitions are evaluated in the order in which they are defined. For example, if the conditions for the first transition are met, then this transition takes place and the rest of the transitions are dismissed.
If you don't specify any conditions in a transition and leave it empty, then it's assumed to be the equivalent of always true. This means that the policy transitions the index to this state the moment it checks.
This table lists the parameters you can define for transitions.
Parameter | Description | Type | Required
:--- | :--- |:--- |:--- |
`state_name` | The name of the state to transition to if the conditions are met. | `string` | Yes
`conditions` | List the conditions for the transition. | `list` | Yes
The `conditions` object has the following parameters:
Parameter | Description | Type | Required
:--- | :--- |:--- |:--- |
`min_index_age` | The minimum age of the index required to transition. | `string` | No
`min_doc_count` | The minimum document count of the index required to transition. | `number` | No
`min_size` | The minimum size of the index required to transition. | `string` | No
`cron` | The `cron` job that triggers the transition if no other transition happens first. | `object` | No
`cron.cron.expression` | The `cron` expression that triggers the transition. | `string` | Yes
`cron.cron.timezone` | The timezone that triggers the transition. | `string` | Yes
The following example transitions the index to a `cold` state after a period of 30 days:
```json
"transitions": [
{
"state_name": "cold",
"conditions": {
"min_index_age": "30d"
}
}
]
```
ISM checks the conditions on every execution of the policy based on the set interval.
This example uses the `cron` condition to transition indices every Saturday at 5:00 PT:
```json
"transitions": [
{
"state_name": "cold",
"conditions": {
"cron": {
"cron": {
"expression": "* 17 * * SAT",
"timezone": "America/Los_Angeles"
}
}
}
}
]
```
Note that this condition does not execute at exactly 5:00 PM; the job still executes based off the `job_interval` setting. Due to this variance in start time and the amount of time that it can take for actions to complete prior to checking transition conditions, we recommend against overly narrow cron expressions. For example, don't use `15 17 * * SAT` (5:15 PM on Saturday).
A window of an hour, which this example uses, is generally sufficient, but you might increase it to 2--3 hours to avoid missing the window and having to wait a week for the transition to occur. Alternately, you could use a broader expression such as `* * * * SAT,SUN` to have the transition occur at any time during the weekend.
For information on writing cron expressions, see [Cron expression reference]({{site.url}}{{site.baseurl}}/monitoring-plugins/alerting/cron/).
---
## Error notifications
The `error_notification` operation sends you a notification if your managed index fails.
It notifies a single destination with a custom message.
Set up error notifications at the policy level:
```json
{
"policy": {
"description": "hot warm delete workflow",
"default_state": "hot",
"schema_version": 1,
"error_notification": { },
"states": [ ]
}
}
```
Parameter | Description | Type | Required
:--- | :--- |:--- |:--- |
`destination` | The destination URL. | `Slack, Amazon Chime, or webhook URL` | Yes
`message_template` | The text of the message. You can add variables to your messages using [Mustache templates](https://mustache.github.io/mustache.5.html). | `object` | Yes
The destination system **must** return a response otherwise the `error_notification` operation throws an error.
#### Example 1: Chime notification
```json
{
"error_notification": {
"destination": {
"chime": {
"url": "<url>"
}
},
"message_template": {
"source": "The index {% raw %}{{ctx.index}}{% endraw %} failed during policy execution."
}
}
}
```
#### Example 2: Custom webhook notification
```json
{
"error_notification": {
"destination": {
"custom_webhook": {
"url": "https://<your_webhook>"
}
},
"message_template": {
"source": "The index {% raw %}{{ctx.index}}{% endraw %} failed during policy execution."
}
}
}
```
#### Example 3: Slack notification
```json
{
"error_notification": {
"destination": {
"slack": {
"url": "https://hooks.slack.com/services/xxx/xxxxxx"
}
},
"message_template": {
"source": "The index {% raw %}{{ctx.index}}{% endraw %} failed during policy execution."
}
}
}
```
You can use the same options for `ctx` variables as the [notification](#notification) operation.
## Sample policy with ISM template
The following sample template policy is for a rollover use case.
1. Create a policy with an `ism_template` field:
```json
PUT _plugins/_ism/policies/rollover_policy
{
"policy": {
"description": "Example rollover policy.",
"default_state": "rollover",
"states": [
{
"name": "rollover",
"actions": [
{
"rollover": {
"min_doc_count": 1
}
}
],
"transitions": []
}
],
"ism_template": {
"index_patterns": ["log*"],
"priority": 100
}
}
}
```
You need to specify the `index_patterns` field. If you don't specify a value for `priority`, it defaults to 0.
2. Set up a template with the `rollover_alias` as `log` :
```json
PUT _index_template/ism_rollover
{
"index_patterns": ["log*"],
"settings": {
"plugins.index_state_management.rollover_alias": "log"
}
}
```
3. Create an index with the `log` alias:
```json
PUT log-000001
{
"aliases": {
"log": {
"is_write_index": true
}
}
}
```
4. Index a document to trigger the rollover condition:
```json
POST log/_doc
{
"message": "dummy"
}
```
## Example policy
The following example policy implements a `hot`, `warm`, and `delete` workflow. You can use this policy as a template to prioritize resources to your indices based on their levels of activity.
In this case, an index is initially in a `hot` state. After a day, it changes to a `warm` state, where the number of replicas increases to 5 to improve the read performance.
After 30 days, the policy moves this index into a `delete` state. The service sends a notification to a Chime room that the index is being deleted, and then permanently deletes it.
```json
{
"policy": {
"description": "hot warm delete workflow",
"default_state": "hot",
"schema_version": 1,
"states": [
{
"name": "hot",
"actions": [
{
"rollover": {
"min_index_age": "1d"
}
}
],
"transitions": [
{
"state_name": "warm"
}
]
},
{
"name": "warm",
"actions": [
{
"replica_count": {
"number_of_replicas": 5
}
}
],
"transitions": [
{
"state_name": "delete",
"conditions": {
"min_index_age": "30d"
}
}
]
},
{
"name": "delete",
"actions": [
{
"notification": {
"destination": {
"chime": {
"url": "<URL>"
}
},
"message_template": {
"source": "The index {% raw %}{{ctx.index}}{% endraw %} is being deleted"
}
}
},
{
"delete": {}
}
]
}
]
}
}
```
This diagram shows the `states`, `transitions`, and `actions` of the above policy as a finite-state machine. For more information about finite-state machines, see [Wikipedia](https://en.wikipedia.org/wiki/Finite-state_machine).
![Policy State Machine]({{site.baseurl}}/images/ism.png)

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@ -1,49 +0,0 @@
---
layout: default
title: Settings
parent: Index State Management
redirect_from: /docs/ism/settings/
nav_order: 4
---
# ISM settings
We don't recommend changing these settings; the defaults should work well for most use cases.
Index State Management (ISM) stores its configuration in the `.opendistro-ism-config` index. Don't modify this index without using the [ISM API operations]({{site.url}}{{site.baseurl}}/im-plugin/ism/api/).
All settings are available using the OpenSearch `_cluster/settings` operation. None require a restart, and all can be marked `persistent` or `transient`.
Setting | Default | Description
:--- | :--- | :---
`plugins.index_state_management.enabled` | True | Specifies whether ISM is enabled or not.
`plugins.index_state_management.job_interval` | 5 minutes | The interval at which the managed index jobs are run.
`plugins.index_state_management.coordinator.sweep_period` | 10 minutes | How often the routine background sweep is run.
`plugins.index_state_management.coordinator.backoff_millis` | 50 milliseconds | The backoff time between retries for failures in the `ManagedIndexCoordinator` (such as when we update managed indices).
`plugins.index_state_management.coordinator.backoff_count` | 2 | The count of retries for failures in the `ManagedIndexCoordinator`.
`plugins.index_state_management.history.enabled` | True | Specifies whether audit history is enabled or not. The logs from ISM are automatically indexed to a logs document.
`plugins.index_state_management.history.max_docs` | 2,500,000 | The maximum number of documents before rolling over the audit history index.
`plugins.index_state_management.history.max_age` | 24 hours | The maximum age before rolling over the audit history index.
`plugins.index_state_management.history.rollover_check_period` | 8 hours | The time between rollover checks for the audit history index.
`plugins.index_state_management.history.rollover_retention_period` | 30 days | How long audit history indices are kept.
`plugins.index_state_management.allow_list` | All actions | List of actions that you can use.
## Audit history indices
If you don't want to disable ISM audit history or shorten the retention period, you can create an [index template]({{site.url}}{{site.baseurl}}/opensearch/index-templates/) to reduce the shard count of the history indices:
```json
PUT _index_template/ism_history_indices
{
"index_patterns": [
".opendistro-ism-managed-index-history-*"
],
"template": {
"settings": {
"number_of_shards": 1,
"number_of_replicas": 0
}
}
}
```

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---
layout: default
title: Refresh search analyzer
nav_order: 40
has_children: false
redirect_from: /docs/ism/refresh-analyzer/
has_toc: false
---
# Refresh search analyzer
With ISM installed, you can refresh search analyzers in real time with the following API:
```json
POST /_plugins/_refresh_search_analyzers/<index or alias or wildcard>
```
For example, if you change the synonym list in your analyzer, the change takes effect without you needing to close and reopen the index.
To work, the token filter must have an `updateable` flag of `true`:
```json
{
"analyzer": {
"my_synonyms": {
"tokenizer": "whitespace",
"filter": [
"synonym"
]
}
},
"filter": {
"synonym": {
"type": "synonym_graph",
"synonyms_path": "synonyms.txt",
"updateable": true
}
}
}
```

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{% if site.anchor_links != nil %}
<script src="https://cdnjs.cloudflare.com/ajax/libs/anchor-js/4.2.0/anchor.min.js"></script>
{% endif %}
{% if page.has_math == true %}
<script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3.0.1/es5/tex-mml-chtml.js"></script>
{% endif %}

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<ul class="nav-list">
{%- assign titled_pages = include.pages
| where_exp:"item", "item.title != nil" -%}
{%- comment -%}
The values of `title` and `nav_order` can be numbers or strings.
Jekyll gives build failures when sorting on mixtures of different types,
so numbers and strings need to be sorted separately.
Here, numbers are sorted by their values, and come before all strings.
An omitted `nav_order` value is equivalent to the page's `title` value
(except that a numerical `title` value is treated as a string).
The case-sensitivity of string sorting is determined by `site.nav_sort`.
{%- endcomment -%}
{%- assign string_ordered_pages = titled_pages
| where_exp:"item", "item.nav_order == nil" -%}
{%- assign nav_ordered_pages = titled_pages
| where_exp:"item", "item.nav_order != nil" -%}
{%- comment -%}
The nav_ordered_pages have to be added to number_ordered_pages and
string_ordered_pages, depending on the nav_order value.
The first character of the jsonify result is `"` only for strings.
{%- endcomment -%}
{%- assign nav_ordered_groups = nav_ordered_pages
| group_by_exp:"item", "item.nav_order | jsonify | slice: 0" -%}
{%- assign number_ordered_pages = "" | split:"X" -%}
{%- for group in nav_ordered_groups -%}
{%- if group.name == '"' -%}
{%- assign string_ordered_pages = string_ordered_pages | concat: group.items -%}
{%- else -%}
{%- assign number_ordered_pages = number_ordered_pages | concat: group.items -%}
{%- endif -%}
{%- endfor -%}
{%- assign sorted_number_ordered_pages = number_ordered_pages | sort:"nav_order" -%}
{%- comment -%}
The string_ordered_pages have to be sorted by nav_order, and otherwise title
(where appending the empty string to a numeric title converts it to a string).
After grouping them by those values, the groups are sorted, then the items
of each group are concatenated.
{%- endcomment -%}
{%- assign string_ordered_groups = string_ordered_pages
| group_by_exp:"item", "item.nav_order | default: item.title | append:''" -%}
{%- if site.nav_sort == 'case_insensitive' -%}
{%- assign sorted_string_ordered_groups = string_ordered_groups | sort_natural:"name" -%}
{%- else -%}
{%- assign sorted_string_ordered_groups = string_ordered_groups | sort:"name" -%}
{%- endif -%}
{%- assign sorted_string_ordered_pages = "" | split:"X" -%}
{%- for group in sorted_string_ordered_groups -%}
{%- assign sorted_string_ordered_pages = sorted_string_ordered_pages | concat: group.items -%}
{%- endfor -%}
{%- assign pages_list = sorted_number_ordered_pages | concat: sorted_string_ordered_pages -%}
{%- for node in pages_list -%}
{%- if node.parent == nil -%}
{%- unless node.nav_exclude -%}
<li class="nav-list-item{% if page.collection == include.key and page.url == node.url or page.parent == node.title or page.grand_parent == node.title %} active{% endif %}">
{%- if node.has_children -%}
<a href="#" class="nav-list-expander"><svg viewBox="0 0 24 24"><use xlink:href="#svg-arrow-right"></use></svg></a>
{%- endif -%}
<a href="{{ node.url | absolute_url }}" class="nav-list-link{% if page.url == node.url %} active{% endif %}">{{ node.title }}</a>
{%- if node.has_children -%}
{%- assign children_list = pages_list | where: "parent", node.title -%}
<ul class="nav-list ">
{%- for child in children_list -%}
{%- unless child.nav_exclude -%}
<li class="nav-list-item {% if page.url == child.url or page.parent == child.title %} active{% endif %}">
{%- if child.has_children -%}
<a href="#" class="nav-list-expander"><svg viewBox="0 0 24 24"><use xlink:href="#svg-arrow-right"></use></svg></a>
{%- endif -%}
<a href="{{ child.url | absolute_url }}" class="nav-list-link{% if page.url == child.url %} active{% endif %}">{{ child.title }}</a>
{%- if child.has_children -%}
{%- assign grand_children_list = pages_list | where: "parent", child.title | where: "grand_parent", node.title -%}
<ul class="nav-list">
{%- for grand_child in grand_children_list -%}
{%- unless grand_child.nav_exclude -%}
<li class="nav-list-item {% if page.url == grand_child.url %} active{% endif %}">
<a href="{{ grand_child.url | absolute_url }}" class="nav-list-link{% if page.url == grand_child.url %} active{% endif %}">{{ grand_child.title }}</a>
</li>
{%- endunless -%}
{%- endfor -%}
</ul>
{%- endif -%}
</li>
{%- endunless -%}
{%- endfor -%}
</ul>
{%- endif -%}
</li>
{%- endunless -%}
{%- endif -%}
{%- endfor -%}
</ul>
{%- if page.collection == include.key -%}
{%- for node in pages_list -%}
{%- if node.parent == nil -%}
{%- if page.parent == node.title or page.grand_parent == node.title -%}
{%- assign first_level_url = node.url | absolute_url -%}
{%- endif -%}
{%- if node.has_children -%}
{%- assign children_list = pages_list | where: "parent", node.title -%}
{%- for child in children_list -%}
{%- if child.has_children -%}
{%- if page.url == child.url or page.parent == child.title and page.grand_parent == child.parent -%}
{%- assign second_level_url = child.url | absolute_url -%}
{%- endif -%}
{%- endif -%}
{%- endfor -%}
{%- endif -%}
{%- endif -%}
{%- endfor -%}
{% if page.has_children == true and page.has_toc != false %}
{%- assign toc_list = pages_list | where: "parent", page.title | where: "grand_parent", page.parent -%}
{%- endif -%}
{%- endif -%}

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@ -1,182 +0,0 @@
{% capture tocWorkspace %}
{% comment %}
Copyright (c) 2017 Vladimir "allejo" Jimenez
Permission is hereby granted, free of charge, to any person
obtaining a copy of this software and associated documentation
files (the "Software"), to deal in the Software without
restriction, including without limitation the rights to use,
copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the
Software is furnished to do so, subject to the following
conditions:
The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
OTHER DEALINGS IN THE SOFTWARE.
{% endcomment %}
{% comment %}
Version 1.1.0
https://github.com/allejo/jekyll-toc
"...like all things liquid - where there's a will, and ~36 hours to spare, there's usually a/some way" ~jaybe
Usage:
{% include toc.html html=content sanitize=true class="inline_toc" id="my_toc" h_min=2 h_max=3 %}
Parameters:
* html (string) - the HTML of compiled markdown generated by kramdown in Jekyll
Optional Parameters:
* sanitize (bool) : false - when set to true, the headers will be stripped of any HTML in the TOC
* class (string) : '' - a CSS class assigned to the TOC
* id (string) : '' - an ID to assigned to the TOC
* h_min (int) : 1 - the minimum TOC header level to use; any header lower than this value will be ignored
* h_max (int) : 6 - the maximum TOC header level to use; any header greater than this value will be ignored
* ordered (bool) : false - when set to true, an ordered list will be outputted instead of an unordered list
* item_class (string) : '' - add custom class(es) for each list item; has support for '%level%' placeholder, which is the current heading level
* submenu_class (string) : '' - add custom class(es) for each child group of headings; has support for '%level%' placeholder which is the current "submenu" heading level
* base_url (string) : '' - add a base url to the TOC links for when your TOC is on another page than the actual content
* anchor_class (string) : '' - add custom class(es) for each anchor element
* skip_no_ids (bool) : false - skip headers that do not have an `id` attribute
Output:
An ordered or unordered list representing the table of contents of a markdown block. This snippet will only
generate the table of contents and will NOT output the markdown given to it
{% endcomment %}
{% capture newline %}
{% endcapture %}
{% assign newline = newline | rstrip %} <!-- Remove the extra spacing but preserve the newline -->
{% capture deprecation_warnings %}{% endcapture %}
{% if include.baseurl %}
{% capture deprecation_warnings %}{{ deprecation_warnings }}<!-- jekyll-toc :: "baseurl" has been deprecated, use "base_url" instead -->{{ newline }}{% endcapture %}
{% endif %}
{% if include.skipNoIDs %}
{% capture deprecation_warnings %}{{ deprecation_warnings }}<!-- jekyll-toc :: "skipNoIDs" has been deprecated, use "skip_no_ids" instead -->{{ newline }}{% endcapture %}
{% endif %}
{% capture jekyll_toc %}{% endcapture %}
{% assign orderedList = include.ordered | default: false %}
{% assign baseURL = include.base_url | default: include.baseurl | default: '' %}
{% assign skipNoIDs = include.skip_no_ids | default: include.skipNoIDs | default: false %}
{% assign minHeader = include.h_min | default: 1 %}
{% assign maxHeader = include.h_max | default: 6 %}
{% assign nodes = include.html | strip | split: '<h' %}
{% assign firstHeader = true %}
{% assign currLevel = 0 %}
{% assign lastLevel = 0 %}
{% capture listModifier %}{% if orderedList %}ol{% else %}ul{% endif %}{% endcapture %}
{% for node in nodes %}
{% if node == "" %}
{% continue %}
{% endif %}
{% assign currLevel = node | replace: '"', '' | slice: 0, 1 | times: 1 %}
{% if currLevel < minHeader or currLevel > maxHeader %}
{% continue %}
{% endif %}
{% assign _workspace = node | split: '</h' %}
{% assign _idWorkspace = _workspace[0] | split: 'id="' %}
{% assign _idWorkspace = _idWorkspace[1] | split: '"' %}
{% assign htmlID = _idWorkspace[0] %}
{% assign _classWorkspace = _workspace[0] | split: 'class="' %}
{% assign _classWorkspace = _classWorkspace[1] | split: '"' %}
{% assign htmlClass = _classWorkspace[0] %}
{% if htmlClass contains "no_toc" %}
{% continue %}
{% endif %}
{% if firstHeader %}
{% assign minHeader = currLevel %}
{% endif %}
{% capture _hAttrToStrip %}{{ _workspace[0] | split: '>' | first }}>{% endcapture %}
{% assign header = _workspace[0] | replace: _hAttrToStrip, '' %}
{% if include.item_class and include.item_class != blank %}
{% capture listItemClass %} class="{{ include.item_class | replace: '%level%', currLevel | split: '.' | join: ' ' }}"{% endcapture %}
{% endif %}
{% if include.submenu_class and include.submenu_class != blank %}
{% assign subMenuLevel = currLevel | minus: 1 %}
{% capture subMenuClass %} class="{{ include.submenu_class | replace: '%level%', subMenuLevel | split: '.' | join: ' ' }}"{% endcapture %}
{% endif %}
{% capture anchorBody %}{% if include.sanitize %}{{ header | strip_html }}{% else %}{{ header }}{% endif %}{% endcapture %}
{% if htmlID %}
{% capture anchorAttributes %} href="{% if baseURL %}{{ baseURL }}{% endif %}#{{ htmlID }}"{% endcapture %}
{% if include.anchor_class %}
{% capture anchorAttributes %}{{ anchorAttributes }} class="{{ include.anchor_class | split: '.' | join: ' ' }}"{% endcapture %}
{% endif %}
{% capture listItem %}<a{{ anchorAttributes }}>{{ anchorBody }}</a>{% endcapture %}
{% elsif skipNoIDs == true %}
{% continue %}
{% else %}
{% capture listItem %}{{ anchorBody }}{% endcapture %}
{% endif %}
{% if currLevel > lastLevel %}
{% capture jekyll_toc %}{{ jekyll_toc }}<{{ listModifier }}{{ subMenuClass }}>{% endcapture %}
{% elsif currLevel < lastLevel %}
{% assign repeatCount = lastLevel | minus: currLevel %}
{% for i in (1..repeatCount) %}
{% capture jekyll_toc %}{{ jekyll_toc }}</li></{{ listModifier }}>{% endcapture %}
{% endfor %}
{% capture jekyll_toc %}{{ jekyll_toc }}</li>{% endcapture %}
{% else %}
{% capture jekyll_toc %}{{ jekyll_toc }}</li>{% endcapture %}
{% endif %}
{% capture jekyll_toc %}{{ jekyll_toc }}<li{{ listItemClass }}>{{ listItem }}{% endcapture %}
{% assign lastLevel = currLevel %}
{% assign firstHeader = false %}
{% endfor %}
{% assign repeatCount = minHeader | minus: 1 %}
{% assign repeatCount = lastLevel | minus: repeatCount %}
{% for i in (1..repeatCount) %}
{% capture jekyll_toc %}{{ jekyll_toc }}</li></{{ listModifier }}>{% endcapture %}
{% endfor %}
{% if jekyll_toc != '' %}
{% assign rootAttributes = '' %}
{% if include.class and include.class != blank %}
{% capture rootAttributes %} class="{{ include.class | split: '.' | join: ' ' }}"{% endcapture %}
{% endif %}
{% if include.id and include.id != blank %}
{% capture rootAttributes %}{{ rootAttributes }} id="{{ include.id }}"{% endcapture %}
{% endif %}
{% if rootAttributes %}
{% assign nodes = jekyll_toc | split: '>' %}
{% capture jekyll_toc %}<{{ listModifier }}{{ rootAttributes }}>{{ nodes | shift | join: '>' }}>{% endcapture %}
{% endif %}
{% endif %}
{% endcapture %}{% assign tocWorkspace = '' %}{{ deprecation_warnings }}{{ jekyll_toc }}

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---
layout: table_wrappers
---
<!DOCTYPE html>
<html lang="{{ site.lang | default: 'en-US' }}">
{% include head.html %}
<body>
<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
<symbol id="svg-link" viewBox="0 0 24 24">
<title>Link</title>
<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather feather-link">
<path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path>
</svg>
</symbol>
<symbol id="svg-search" viewBox="0 0 24 24">
<title>Search</title>
<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather feather-search">
<circle cx="11" cy="11" r="8"></circle><line x1="21" y1="21" x2="16.65" y2="16.65"></line>
</svg>
</symbol>
<symbol id="svg-menu" viewBox="0 0 24 24">
<title>Menu</title>
<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather feather-menu">
<line x1="3" y1="12" x2="21" y2="12"></line><line x1="3" y1="6" x2="21" y2="6"></line><line x1="3" y1="18" x2="21" y2="18"></line>
</svg>
</symbol>
<symbol id="svg-arrow-right" viewBox="0 0 24 24">
<title>Expand</title>
<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather feather-chevron-right">
<polyline points="9 18 15 12 9 6"></polyline>
</svg>
</symbol>
<symbol id="svg-doc" viewBox="0 0 24 24">
<title>Document</title>
<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather feather-file">
<path d="M13 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V9z"></path><polyline points="13 2 13 9 20 9"></polyline>
</svg>
</symbol>
</svg>
<div class="side-bar">
<div class="site-header">
<a href="{{ '/' | absolute_url }}" class="site-title lh-tight">{% include title.html %}</a>
<a href="#" id="menu-button" class="site-button">
<svg viewBox="0 0 24 24" class="icon"><use xlink:href="#svg-menu"></use></svg>
</a>
</div>
<nav role="navigation" aria-label="Main" id="site-nav" class="site-nav">
{% assign pages_top_size = site.html_pages
| where_exp:"item", "item.title != nil"
| where_exp:"item", "item.parent == nil"
| where_exp:"item", "item.nav_exclude != true"
| size %}
{% if pages_top_size > 0 %}
{% include nav.html pages=site.html_pages key=nil %}
{% endif %}
{% if site.just_the_docs.collections %}
{% assign collections_size = site.just_the_docs.collections | size %}
{% for collection_entry in site.just_the_docs.collections %}
{% assign collection_key = collection_entry[0] %}
{% assign collection_value = collection_entry[1] %}
{% assign collection = site[collection_key] %}
{% if collection_value.nav_exclude != true %}
{% if collections_size > 1 or pages_top_size > 0 %}
{% if collection_value.nav_fold == true %}
<ul class="nav-list nav-category-list">
<li class="nav-list-item{% if page.collection == collection_key %} active{% endif %}">
{%- if collection.size > 0 -%}
<a href="#" class="nav-list-expander"><svg viewBox="0 0 24 24"><use xlink:href="#svg-arrow-right"></use></svg></a>
{%- endif -%}
<div class="nav-category">{{ collection_value.name }}</div>
{% include nav.html pages=collection key=collection_key %}
</li>
</ul>
{% else %}
<div class="nav-category">{{ collection_value.name }}</div>
{% include nav.html pages=collection key=collection_key %}
{% endif %}
{% else %}
{% include nav.html pages=collection key=collection_key %}
{% endif %}
{% endif %}
{% endfor %}
{% endif %}
</nav>
<footer class="site-footer">
<p class="text-small text-grey-dk-100">See a problem? Submit <a href="https://github.com/opensearch-project/documentation-website/issues">issues</a> or <a href="https://github.com/opensearch-project/documentation-website/edit/main/{{ page.path }}">edit this page</a> on <a href="https://github.com/opensearch-project/documentation-website/">GitHub</a>.</p>
<p class="text-small text-grey-dk-100 mb-0">© 2021 OpenSearch contributors. This documentation is under the Apache License 2.0.</p>
</footer>
</div>
<div class="main" id="top">
<div id="main-header" class="main-header">
{% if site.search_enabled != false %}
<div class="search">
<div class="search-input-wrap">
<input type="text" id="search-input" class="search-input" tabindex="0" placeholder="Search..." aria-label="Search {{ site.title }}" autocomplete="off">
<label for="search-input" class="search-label"><svg viewBox="0 0 24 24" class="search-icon"><use xlink:href="#svg-search"></use></svg></label>
</div>
<div id="search-results" class="search-results"></div>
</div>
{% endif %}
{% include header_custom.html %}
{% if site.aux_links %}
<nav aria-label="Auxiliary" class="aux-nav">
<ul class="aux-nav-list">
{% for link in site.aux_links %}
<li class="aux-nav-list-item">
<a href="{{ link.last }}" class="site-button"
{% if site.aux_links_new_tab %}
target="_blank" rel="noopener noreferrer"
{% endif %}
>
{{ link.first }}
</a>
</li>
{% endfor %}
</ul>
</nav>
{% endif %}
</div>
<div id="main-content-wrap" class="main-content-wrap">
{% unless page.url == "/" %}
{% if page.parent %}
<nav aria-label="Breadcrumb" class="breadcrumb-nav">
<ol class="breadcrumb-nav-list">
{% if page.grand_parent %}
<li class="breadcrumb-nav-list-item"><a href="{{ first_level_url }}">{{ page.grand_parent }}</a></li>
<li class="breadcrumb-nav-list-item"><a href="{{ second_level_url }}">{{ page.parent }}</a></li>
{% else %}
<li class="breadcrumb-nav-list-item"><a href="{{ first_level_url }}">{{ page.parent }}</a></li>
{% endif %}
<li class="breadcrumb-nav-list-item"><span>{{ page.title }}</span></li>
</ol>
</nav>
{% endif %}
{% endunless %}
<div id="main-content" class="main-content" role="main">
{% if site.heading_anchors != false %}
{% include vendor/anchor_headings.html html=content beforeHeading="true" anchorBody="<svg viewBox=\"0 0 16 16\" aria-hidden=\"true\"><use xlink:href=\"#svg-link\"></use></svg>" anchorClass="anchor-heading" anchorAttrs="aria-labelledby=\"%html_id%\"" %}
{% else %}
{{ content }}
{% endif %}
{% if page.has_children == true and page.has_toc != false %}
<hr>
<h2 class="text-delta">Table of contents</h2>
<ul>
{% for child in toc_list %}
<li>
<a href="{{ child.url | absolute_url }}">{{ child.title }}</a>{% if child.summary %} - {{ child.summary }}{% endif %}
</li>
{% endfor %}
</ul>
{% endif %}
{% capture footer_custom %}
{%- include footer_custom.html -%}
{% endcapture %}
{% if footer_custom != "" or site.last_edit_timestamp or site.gh_edit_link %}
<hr>
<footer>
{% if site.back_to_top %}
<p><a href="#top" id="back-to-top">{{ site.back_to_top_text }}</a></p>
{% endif %}
{{ footer_custom }}
{% if site.last_edit_timestamp or site.gh_edit_link %}
<div class="d-flex mt-2">
{% if site.last_edit_timestamp and site.last_edit_time_format and page.last_modified_date %}
<p class="text-small text-grey-dk-000 mb-0 mr-2">
Page last modified: <span class="d-inline-block">{{ page.last_modified_date | date: site.last_edit_time_format }}</span>.
</p>
{% endif %}
{% if
site.gh_edit_link and
site.gh_edit_link_text and
site.gh_edit_repository and
site.gh_edit_branch and
site.gh_edit_view_mode
%}
<p class="text-small text-grey-dk-000 mb-0">
<a href="{{ site.gh_edit_repository }}/{{ site.gh_edit_view_mode }}/{{ site.gh_edit_branch }}{% if site.gh_edit_source %}/{{ site.gh_edit_source }}{% endif %}/{{ page.path }}" id="edit-this-page">{{ site.gh_edit_link_text }}</a>
</p>
{% endif %}
</div>
{% endif %}
</footer>
{% endif %}
</div>
</div>
<div class="toc">
{% include toc.html html=content h_min=2 h_max=2 class="toc-list" item_class="toc-item" sanitize=true %}
</div>
{% if site.search_enabled != false %}
{% if site.search.button %}
<a href="#" id="search-button" class="search-button">
<svg viewBox="0 0 24 24" class="icon"><use xlink:href="#svg-search"></use></svg>
</a>
{% endif %}
<div class="search-overlay"></div>
{% endif %}
</div>
{% if site.anchor_links != nil %}
<script>
anchors.add();
</script>
{% endif %}
</body>
</html>

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---
layout: default
title: Anomaly detection
nav_order: 46
has_children: true
redirect_from:
- /monitoring-plugins/ad/
---
# Anomaly detection
An anomaly in OpenSearch is any unusual behavior change in your time-series data. Anomalies can provide valuable insights into your data. For example, for IT infrastructure data, an anomaly in the memory usage metric might help you uncover early signs of a system failure.
It can be challenging to discover anomalies using conventional methods such as creating visualizations and dashboards. You could configure an alert based on a static threshold, but this requires prior domain knowledge and isn't adaptive to data that exhibits organic growth or seasonal behavior.
Anomaly detection automatically detects anomalies in your OpenSearch data in near real-time using the Random Cut Forest (RCF) algorithm. RCF is an unsupervised machine learning algorithm that models a sketch of your incoming data stream to compute an `anomaly grade` and `confidence score` value for each incoming data point. These values are used to differentiate an anomaly from normal variations. For more information about how RCF works, see [Random Cut Forests](https://pdfs.semanticscholar.org/8bba/52e9797f2e2cc9a823dbd12514d02f29c8b9.pdf?_ga=2.56302955.1913766445.1574109076-1059151610.1574109076).
You can pair the anomaly detection plugin with the [alerting plugin]({{site.url}}{{site.baseurl}}/monitoring-plugins/alerting/) to notify you as soon as an anomaly is detected.
To use the anomaly detection plugin, your computer needs to have more than one CPU core.
{: .note }
## Get started with Anomaly Detection
To get started, choose **Anomaly Detection** in OpenSearch Dashboards.
To first test with sample streaming data, choose **Sample Detectors** and try out one of the preconfigured detectors.
### Step 1: Create a detector
A detector is an individual anomaly detection task. You can create multiple detectors, and all the detectors can run simultaneously, with each analyzing data from different sources.
1. Choose **Create Detector**.
1. Enter a name and brief description. Make sure the name is unique and descriptive enough to help you to identify the purpose of the detector.
1. For **Data source**, choose the index you want to use as the data source. You can optionally use index patterns to choose multiple indices.
1. Select the **Timestamp field** in your index.
1. (Optional) For **Data filter**, filter the index you chose as the data source. From the **Filter type** menu, choose **Visual filter**, and then design your filter query by selecting **Fields**, **Operator**, and **Value**, or choose **Custom Expression** and add your own JSON filter query.
1. For **Detector operation settings**, define the **Detector interval**, which is the time interval at which the detector collects data.
- The detector aggregates the data in this interval, then feeds the aggregated result into the anomaly detection model.
The shorter you set this interval, the fewer data points the detector aggregates.
The anomaly detection model uses a shingling process, a technique that uses consecutive data points to create a sample for the model. This process needs a certain number of aggregated data points from contiguous intervals.
- We recommend setting the detector interval based on your actual data. If it's too long it might delay the results, and if it's too short it might miss some data. It also won't have a sufficient number of consecutive data points for the shingle process.
1. (Optional) To add extra processing time for data collection, specify a **Window delay** value. This value tells the detector that the data is not ingested into OpenSearch in real time but with a certain delay.
Set the window delay to shift the detector interval to account for this delay.
- For example, say the detector interval is 10 minutes and data is ingested into your cluster with a general delay of 1 minute.
Assume the detector runs at 2:00. The detector attempts to get the last 10 minutes of data from 1:50 to 2:00, but because of the 1-minute delay, it only gets 9 minutes of data and misses the data from 1:59 to 2:00.
Setting the window delay to 1 minute shifts the interval window to 1:49 - 1:59, so the detector accounts for all 10 minutes of the detector interval time.
1. Choose **Create**.
After you create the detector, the next step is to add features to it.
### Step 2: Add features to your detector
A feature is the field in your index that you want to check for anomalies. A detector can discover anomalies across one or more features. You must choose an aggregation method for each feature: `average()`, `count()`, `sum()`, `min()`, or `max()`. The aggregation method determines what constitutes an anomaly.
For example, if you choose `min()`, the detector focuses on finding anomalies based on the minimum values of your feature. If you choose `average()`, the detector finds anomalies based on the average values of your feature.
A multi-feature model correlates anomalies across all its features. The [curse of dimensionality](https://en.wikipedia.org/wiki/Curse_of_dimensionality) makes it less likely for multi-feature models to identify smaller anomalies as compared to a single-feature model. Adding more features might negatively impact the [precision and recall](https://en.wikipedia.org/wiki/Precision_and_recall) of a model. A higher proportion of noise in your data might further amplify this negative impact. Selecting the optimal feature set is usually an iterative process. We recommend experimenting with a historical detector with different feature sets and checking the precision before moving on to real-time detectors. By default, the maximum number of features for a detector is 5. You can adjust this limit with the `plugins.anomaly_detection.max_anomaly_features` setting.
{: .note }
1. On the **Model configuration** page, enter the **Feature name**.
1. For **Find anomalies based on**, choose the method to find anomalies. For **Field Value** menu, choose the **field** and the **aggregation method**. Or choose **Custom expression**, and add your own JSON aggregation query.
#### (Optional) Set a category field for high cardinality
You can categorize anomalies based on a keyword or IP field type.
The category field categorizes or slices the source time series with a dimension like IP addresses, product IDs, country codes, and so on. This helps to see a granular view of anomalies within each entity of the category field to isolate and debug issues.
To set a category field, choose **Enable a category field** and select a field.
Only a certain number of unique entities are supported in the category field. Use the following equation to calculate the recommended total number of entities supported in a cluster:
```
(data nodes * heap size * anomaly detection maximum memory percentage) / (entity size of a detector)
```
This formula provides a good starting point, but make sure to test with a representative workload.
{: .note }
For example, for a cluster with 3 data nodes, each with 8G of JVM heap size, a maximum memory percentage of 10% (default), and the entity size of the detector as 1MB: the total number of unique entities supported is (8.096 * 10^9 * 0.1 / 1M ) * 3 = 2429.
#### Set a window size
Set the number of aggregation intervals from your data stream to consider in a detection window. It's best to choose this value based on your actual data to see which one leads to the best results for your use case.
Based on experiments performed on a wide variety of one-dimensional data streams, we recommend using a window size between 1 and 16. The default window size is 8. If you set the category field for high cardinality, the default window size is 1.
If you expect missing values in your data or if you want to base the anomalies on the current interval, choose 1. If your data is continuously ingested and you want to base the anomalies on multiple intervals, choose a larger window size.
#### Preview sample anomalies
Preview sample anomalies and adjust the feature settings if needed.
For sample previews, the anomaly detection plugin selects a small number of data samples---for example, one data point every 30 minutes---and uses interpolation to estimate the remaining data points to approximate the actual feature data. It loads this sample dataset into the detector. The detector uses this sample dataset to generate a sample preview of anomaly results.
Examine the sample preview and use it to fine-tune your feature configurations (for example, enable or disable features) to get more accurate results.
1. Choose **Save and start detector**.
1. Choose between automatically starting the detector (recommended) or manually starting the detector at a later time.
### Step 3: Observe the results
Choose the **Anomaly results** tab. You need to wait for some time to see the anomaly results. If the detector interval is 10 minutes, the detector might take more than an hour to start, as it's waiting for sufficient data to generate anomalies.
A shorter interval means the model passes the shingle process more quickly and starts to generate the anomaly results sooner.
Use the [profile detector]({{site.url}}{{site.baseurl}}/monitoring-plugins/ad/api#profile-detector) operation to make sure you have sufficient data points.
If you see the detector pending in "initialization" for longer than a day, aggregate your existing data using the detector interval to check for any missing data points. If you find a lot of missing data points from the aggregated data, consider increasing the detector interval.
![Anomaly detection results]({{site.url}}{{site.baseurl}}/images/ad.png)
Analize anomalies with the following visualizations:
- **Live anomalies** - displays live anomaly results for the last 60 intervals. For example, if the interval is 10, it shows results for the last 600 minutes. The chart refreshes every 30 seconds.
- **Anomaly history** - plots the anomaly grade with the corresponding measure of confidence.
- **Feature breakdown** - plots the features based on the aggregation method. You can vary the date-time range of the detector.
- **Anomaly occurrence** - shows the `Start time`, `End time`, `Data confidence`, and `Anomaly grade` for each detected anomaly.
`Anomaly grade` is a number between 0 and 1 that indicates how anomalous a data point is. An anomaly grade of 0 represents “not an anomaly,” and a non-zero value represents the relative severity of the anomaly.
`Data confidence` is an estimate of the probability that the reported anomaly grade matches the expected anomaly grade. Confidence increases as the model observes more data and learns the data behavior and trends. Note that confidence is distinct from model accuracy.
If you set the category field, you see an additional **Heat map** chart. The heat map correlates results for anomalous entities. This chart is empty until you select an anomalous entity. You also see the anomaly and feature line chart for the time period of the anomaly (`anomaly_grade` > 0).
Choose a filled rectangle to see a more detailed view of the anomaly.
{: .note }
### Step 4: Set up alerts
Choose **Set up alerts** and configure a monitor to notify you when anomalies are detected. For steps to create a monitor and set up notifications based on your anomaly detector, see [Monitors]({{site.url}}{{site.baseurl}}/monitoring-plugins/alerting/monitors/).
If you stop or delete a detector, make sure to delete any monitors associated with it.
### Step 5: Adjust the model
To see all the configuration settings for a detector, choose the **Detector configuration** tab.
1. To make any changes to the detector configuration, or fine tune the time interval to minimize any false positives, go to the **Detector configuration** section and choose **Edit**.
- You need to stop the detector to change its configuration. Confirm that you want to stop the detector and proceed.
1. To enable or disable features, in the **Features** section, choose **Edit** and adjust the feature settings as needed. After you make your changes, choose **Save and start detector**.
- Choose between automatically starting the detector (recommended) or manually starting the detector at a later time.
### Step 6: Analyze historical data
Analyzing historical data helps you get familiar with the anomaly detection plugin. You can also evaluate the performance of a detector with historical data to further fine-tune it.
To use a historical detector, you need to specify a date range that has data present in at least 1,000 detection intervals.
{: .note }
1. Choose **Historical detectors** and **Create historical detector**.
1. Enter the **Name** of the detector and a brief **Description**.
1. For **Data source**, choose the index to use as the data source. You can optionally use index patterns to choose multiple indices.
1. For **Time range**, select a time range for historical analysis.
1. For **Detector settings**, choose to use the settings of an existing detector. Or choose the **Timestamp field** in your index, add individual features to the detector, and set the detector interval.
1. (Optional) Choose to run the historical detector automatically after creating it.
1. Choose **Create**.
- You can stop the historical detector even before it completes.
### Step 7: Manage your detectors
To change or delete a detector, go to the **Detector details** page.
1. To make changes to your detector, choose the detector name.
1. Choose **Actions** and **Edit detector**.
- You need to stop the detector to change its configuration. Confirm that you want to stop the detector and proceed.
1. Make your changes and choose **Save changes**.
To delete your detector, choose **Actions** and **Delete detector**. In the pop-up box, type `delete` to confirm and choose **Delete**.

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---
layout: default
title: Anomaly detection security
nav_order: 10
parent: Anomaly detection
has_children: false
---
# Anomaly detection security
You can use the security plugin with anomaly detection in OpenSearch to limit non-admin users to specific actions. For example, you might want some users to only be able to create, update, or delete detectors, while others to only view detectors.
All anomaly detection indices are protected as system indices. Only a super admin user or an admin user with a TLS certificate can access system indices. For more information, see [System indices]({{site.url}}{{site.baseurl}}/security-plugin/configuration/system-indices/).
Security for anomaly detection works the same as [security for alerting]({{site.url}}{{site.baseurl}}/monitoring-plugins/alerting/security/).
## Basic permissions
As an admin user, you can use the security plugin to assign specific permissions to users based on which APIs they need access to. For a list of supported APIs, see [Anomaly detection API]({{site.url}}{{site.baseurl}}/monitoring-plugins/ad/api/).
The security plugin has two built-in roles that cover most anomaly detection use cases: `anomaly_full_access` and `anomaly_read_access`. For descriptions of each, see [Predefined roles]({{site.url}}{{site.baseurl}}/security-plugin/access-control/users-roles#predefined-roles).
If these roles don't meet your needs, mix and match individual anomaly detection [permissions]({{site.url}}{{site.baseurl}}/security-plugin/access-control/permissions/) to suit your use case. Each action corresponds to an operation in the REST API. For example, the `cluster:admin/opensearch/ad/detector/delete` permission lets you delete detectors.
## (Advanced) Limit access by backend role
Use backend roles to configure fine-grained access to individual detectors based on roles. For example, users of different departments in an organization can view detectors owned by their own department.
First, make sure your users have the appropriate [backend roles]({{site.url}}{{site.baseurl}}/security-plugin/access-control/index/). Backend roles usually come from an [LDAP server]({{site.url}}{{site.baseurl}}/security-plugin/configuration/ldap/) or [SAML provider]({{site.url}}{{site.baseurl}}/security-plugin/configuration/saml/), but if you use the internal user database, you can use the REST API to [add them manually]({{site.url}}{{site.baseurl}}/security-plugin/access-control/api#create-user).
Next, enable the following setting:
```json
PUT _cluster/settings
{
"transient": {
"plugins.anomaly_detection.filter_by_backend_roles": "true"
}
}
```
Now when users view anomaly detection resources in OpenSearch Dashboards (or make REST API calls), they only see detectors created by users who share at least one backend role.
For example, consider two users: `alice` and `bob`.
`alice` has an analyst backend role:
```json
PUT _plugins/_security/api/internalusers/alice
{
"password": "alice",
"backend_roles": [
"analyst"
],
"attributes": {}
}
```
`bob` has a human-resources backend role:
```json
PUT _plugins/_security/api/internalusers/bob
{
"password": "bob",
"backend_roles": [
"human-resources"
],
"attributes": {}
}
```
Both `alice` and `bob` have full access to anomaly detection:
```json
PUT _plugins/_security/api/rolesmapping/anomaly_full_access
{
"backend_roles": [],
"hosts": [],
"users": [
"alice",
"bob"
]
}
```
Because they have different backend roles, `alice` and `bob` cannot view each other's detectors or their results.

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---
layout: default
title: Settings
parent: Anomaly detection
nav_order: 4
---
# Settings
The anomaly detection plugin adds several settings to the standard OpenSearch cluster settings.
The settings are dynamic, so you can change the default behavior of the plugin without restarting your cluster.
You can mark settings as `persistent` or `transient`.
For example, to update the retention period of the result index:
```json
PUT _cluster/settings
{
"transient": {
"plugins.anomaly_detection.ad_result_history_retention_period": "5m"
}
}
```
Setting | Default | Description
:--- | :--- | :---
`plugins.anomaly_detection.enabled` | True | Whether the anomaly detection plugin is enabled or not. If disabled, all detectors immediately stop running.
`plugins.anomaly_detection.max_anomaly_detectors` | 1,000 | The maximum number of non-high cardinality detectors (no category field) users can create.
`plugins.anomaly_detection.max_multi_entity_anomaly_detectors` | 10 | The maximum number of high cardinality detectors (with category field) in a cluster.
`plugins.anomaly_detection.max_anomaly_features` | 5 | The maximum number of features for a detector.
`plugins.anomaly_detection.ad_result_history_rollover_period` | 12h | How often the rollover condition is checked. If `true`, the plugin rolls over the result index to a new index.
`plugins.anomaly_detection.ad_result_history_max_docs` | 250000000 | The maximum number of documents in one result index. The plugin only counts refreshed documents in the primary shards.
`plugins.anomaly_detection.ad_result_history_retention_period` | 30d | The maximum age of the result index. If its age exceeds the threshold, the plugin deletes the rolled over result index. If the cluster has only one result index, the plugin keeps the index even if it's older than its configured retention period.
`plugins.anomaly_detection.max_entities_per_query` | 1,000 | The maximum unique values per detection interval for high cardinality detectors. By default, if the category field has more than 1,000 unique values in a detector interval, the plugin selects the top 1,000 values and orders them by `doc_count`.
`plugins.anomaly_detection.max_entities_for_preview` | 30 | The maximum unique category field values displayed with the preview operation for high cardinality detectors. If the category field has more than 30 unique values, the plugin selects the top 30 values and orders them by `doc_count`.
`plugins.anomaly_detection.max_primary_shards` | 10 | The maximum number of primary shards an anomaly detection index can have.
`plugins.anomaly_detection.filter_by_backend_roles` | False | When you enable the security plugin and set this to `true`, the plugin filters results based on the user's backend role(s).
`plugins.anomaly_detection.max_cache_miss_handling_per_second` | 100 | High cardinality detectors use a cache to store active models. In the event of a cache miss, the cache gets the models from the model checkpoint index. Use this setting to limit the rate of fetching models. Because the thread pool for a GET operation has a queue of 1,000, we recommend setting this value below 1,000.
`plugins.anomaly_detection.max_batch_task_per_node` | 2 | Starting a historical detector triggers a batch task. This setting is the number of batch tasks that you can run per data node. You can tune this setting from 1 to 1000. If the data nodes can't support all batch tasks and you're not sure if the data nodes are capable of running more historical detectors, add more data nodes instead of changing this setting to a higher value.
`plugins.anomaly_detection.max_old_ad_task_docs_per_detector` | 10 | You can run the same historical detector many times. For each run, the anomaly detection plugin creates a new task. This setting is the number of previous tasks the plugin keeps. Set this value to at least 1 to track its last run. You can keep a maximum of 1,000 old tasks to avoid overwhelming the cluster.
`plugins.anomaly_detection.batch_task_piece_size` | 1000 | The date range for a historical task is split into smaller pieces and the anomaly detection plugin runs the task piece by piece. Each piece contains 1,000 detection intervals by default. For example, if detector interval is 1 minute and one piece is 1000 minutes, the feature data is queried every 1,000 minutes. You can change this setting from 1 to 10,000.
`plugins.anomaly_detection.batch_task_piece_interval_seconds` | 5 | Add a time interval between historical detector tasks. This interval prevents the task from consuming too much of the available resources and starving other operations like search and bulk index. You can change this setting from 1 to 600 seconds.

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---
layout: default
title: Cron
nav_order: 20
parent: Alerting
has_children: false
redirect_from:
- /alerting/cron/
- /docs/alerting/cron/
---
# Cron expression reference
Monitors can run at a variety of fixed intervals (e.g. hourly, daily, etc.), but you can also define custom cron expressions for when they should run. Monitors use the Unix cron syntax and support five fields:
Field | Valid values
:--- | :---
Minute | 0-59
Hour | 0-23
Day of month | 1-31
Month | 1-12
Day of week | 0-7 (0 and 7 are both Sunday) or SUN, MON, TUE, WED, THU, FRI, SAT
For example, the following expression translates to "every Monday through Friday at 11:30 AM":
```
30 11 * * 1-5
```
## Features
Feature | Description
:--- | :---
`*` | Wildcard. Specifies all valid values.
`,` | List. Use to specify several values (e.g. `1,15,30`).
`-` | Range. Use to specify a range of values (e.g. `1-15`).
`/` | Step. Use after a wildcard or range to specify the "step" between values. For example, `0-11/2` is equivalent to `0,2,4,6,8,10`.
Note that you can specify the day using two fields: day of month and day of week. For most situations, we recommend that you use just one of these fields and leave the other as `*`.
If you use a non-wildcard value in both fields, the monitor runs when either field matches the time. For example, `15 2 1,15 * 1` causes the monitor to run at 2:15 AM on the 1st of the month, the 15th of the month, and every Monday.
## Sample expressions
Every other day at 1:45 PM:
```
45 13 1-31/2 * *
```
Every 10 minutes on Saturday and Sunday:
```
0/10 * * * 6-7
```
Every three hours on the first day of every other month:
```
0 0-23/3 1 1-12/2 *
```
## API
For an example of how to use a custom cron expression in an API call, see the [create monitor API operation]({{site.url}}{{site.baseurl}}/monitoring-plugins/alerting/api#request-1).

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---
layout: default
title: Alerting
nav_order: 34
has_children: true
redirect_from:
- /monitoring-plugins/alerting/
---
# Alerting
OpenSearch Dashboards
{: .label .label-yellow :}
The alerting feature notifies you when data from one or more OpenSearch indices meets certain conditions. For example, you might want to notify a [Slack](https://slack.com/) channel if your application logs more than five HTTP 503 errors in one hour, or you might want to page a developer if no new documents have been indexed in the past 20 minutes.
To get started, choose **Alerting** in OpenSearch Dashboards.
![OpenSearch Dashboards side bar with link]({{site.url}}{{site.baseurl}}/images/alerting.png)

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---
layout: default
title: Monitors
nav_order: 1
parent: Alerting
has_children: false
---
# Monitors
#### Table of contents
- TOC
{:toc}
---
## Key terms
Term | Definition
:--- | :---
Monitor | A job that runs on a defined schedule and queries OpenSearch. The results of these queries are then used as input for one or more *triggers*.
Trigger | Conditions that, if met, generate *alerts*.
Alert | An event associated with a trigger. When an alert is created, the trigger performs *actions*, which can include sending a notification.
Action | The information that you want the monitor to send out after being triggered. Actions have a *destination*, a message subject, and a message body.
Destination | A reusable location for an action, such as Amazon Chime, Slack, or a webhook URL.
---
## Create destinations
1. Choose **Alerting**, **Destinations**, **Add destination**.
1. Specify a name for the destination so that you can identify it later.
1. For **Type**, choose Slack, Amazon Chime, custom webhook, or [email](#email-as-a-destination).
For Email type, refer to [Email as a destination](#email-as-a-destination) section below. For all other types, specify the webhook URL. For more information about webhooks, see the documentation for [Slack](https://api.slack.com/incoming-webhooks) and [Chime](https://docs.aws.amazon.com/chime/latest/ug/webhooks.html).
For custom webhooks, you must specify more information: parameters and headers. For example, if your endpoint requires basic authentication, you might need to add a header with a key of `Authorization` and a value of `Basic <Base64-encoded-credential-string>`. You might also need to change `Content-Type` to whatever your webhook requires. Popular values are `application/json`, `application/xml`, and `text/plain`.
This information is stored in plain text in the OpenSearch cluster. We will improve this design in the future, but for now, the encoded credentials (which are neither encrypted nor hashed) might be visible to other OpenSearch users.
### Email as a destination
To send or receive an alert notification as an email, choose **Email** as the destination type. Next, add at least one sender and recipient. We recommend adding email groups if you want to notify more than a few people of an alert. You can configure senders and recipients using **Manage senders** and **Manage email groups**.
#### Manage senders
Senders are email accounts from which the alerting plugin sends notifications.
To configure a sender email, do the following:
1. After you choose **Email** as the destination type, choose **Manage senders**.
1. Choose **Add sender**, **New sender** and enter a unique name.
1. Enter the email address, SMTP host (e.g. `smtp.gmail.com` for a Gmail account), and the port.
1. Choose an encryption method, or use the default value of **None**. However, most email providers require SSL or TLS, which requires a username and password in OpenSearch keystore. Refer to [Authenticate sender account](#authenticate-sender-account) to learn more.
1. Choose **Save** to save the configuration and create the sender. You can create a sender even before you add your credentials to the OpenSearch keystore. However, you must [authenticate each sender account](#authenticate-sender-account) before you use the destination to send your alert.
You can reuse senders across many different destinations, but each destination only supports one sender.
#### Manage email groups or recipients
Use email groups to create and manage reusable lists of email addresses. For example, one alert might email the DevOps team, whereas another might email the executive team and the engineering team.
You can enter individual email addresses or an email group in the **Recipients** field.
1. After you choose **Email** as the destination type, choose **Manage email groups**. Then choose **Add email group**, **New email group**.
1. Enter a unique name.
1. For recipient emails, enter any number of email addresses.
1. Choose **Save**.
#### Authenticate sender account
If your email provider requires SSL or TLS, you must authenticate each sender account before you can send an email. Enter these credentials in the OpenSearch keystore using the CLI. Run the following commands (in your OpenSearch directory) to enter your username and password. The `<sender_name>` is the name you entered for **Sender** earlier.
```bash
./bin/opensearch-keystore add plugins.alerting.destination.email.<sender_name>.username
./bin/opensearch-keystore add plugins.alerting.destination.email.<sender_name>.password
```
**Note**: Keystore settings are node-specific. You must run these commands on each node.
{: .note}
To change or update your credentials (after you've added them to the keystore on every node), call the reload API to automatically update those credentials without restarting OpenSearch:
```json
POST _nodes/reload_secure_settings
{
"secure_settings_password": "1234"
}
```
---
## Create monitors
1. Choose **Alerting**, **Monitors**, **Create monitor**.
1. Specify a name for the monitor.
The anomaly detection option is for pairing with the anomaly detection plugin. See [Anomaly Detection]({{site.url}}{{site.baseurl}}/monitoring-plugins/ad/).
For anomaly detector, choose an appropriate schedule for the monitor based on the detector interval. Otherwise, the alerting monitor might miss reading the results.
For example, assume you set the monitor interval and the detector interval as 5 minutes, and you start the detector at 12:00. If an anomaly is detected at 12:05, it might be available at 12:06 because of the delay between writing the anomaly and it being available for queries. The monitor reads the anomaly results between 12:00 and 12:05, so it does not get the anomaly results available at 12:06.
To avoid this issue, make sure the alerting monitor is at least twice the detector interval.
When you create a monitor using OpenSearch Dashboards, the anomaly detector plugin generates a default monitor schedule that's twice the detector interval.
Whenever you update a detector’s interval, make sure to update the associated monitor interval as well, as the anomaly detection plugin does not do this automatically.
1. Choose one or more indices. You can also use `*` as a wildcard to specify an index pattern.
If you use the security plugin, you can only choose indices that you have permission to access. For details, see [Alerting security]({{site.url}}{{site.baseurl}}/security-plugin/).
1. Define the monitor in one of three ways: visually, using a query, or using an anomaly detector.
- Visual definition works well for monitors that you can define as "some value is above or below some threshold for some amount of time."
- Query definition gives you flexibility in terms of what you query for (using [the OpenSearch query DSL]({{site.url}}{{site.baseurl}}/opensearch/query-dsl/full-text)) and how you evaluate the results of that query (Painless scripting).
This example averages the `cpu_usage` field:
```json
{
"size": 0,
"query": {
"match_all": {}
},
"aggs": {
"avg_cpu": {
"avg": {
"field": "cpu_usage"
}
}
}
}
```
You can even filter query results using `{% raw %}{{period_start}}{% endraw %}` and `{% raw %}{{period_end}}{% endraw %}`:
```json
{
"size": 0,
"query": {
"bool": {
"filter": [{
"range": {
"timestamp": {
"from": "{% raw %}{{period_end}}{% endraw %}||-1h",
"to": "{% raw %}{{period_end}}{% endraw %}",
"include_lower": true,
"include_upper": true,
"format": "epoch_millis",
"boost": 1
}
}
}],
"adjust_pure_negative": true,
"boost": 1
}
},
"aggregations": {}
}
```
"Start" and "end" refer to the interval at which the monitor runs. See [Available variables](#available-variables).
1. To define a monitor visually, choose **Define using visual graph**. Then choose an aggregation (for example, `count()` or `average()`), a set of documents, and a timeframe. Visual definition works well for most monitors.
To use a query, choose **Define using extraction query**, add your query (using [the OpenSearch query DSL]({{site.url}}{{site.baseurl}}/opensearch/query-dsl/full-text/)), and test it using the **Run** button.
The monitor makes this query to OpenSearch as often as the schedule dictates; check the **Query Performance** section and make sure you're comfortable with the performance implications.
To use an anomaly detector, choose **Define using Anomaly detector** and select your **Detector**.
1. Choose a frequency and timezone for your monitor. Note that you can only pick a timezone if you choose Daily, Weekly, Monthly, or [custom cron expression]({{site.url}}{{site.baseurl}}/monitoring-plugins/alerting/cron/) for frequency.
1. Choose **Create**.
---
## Create triggers
The next step in creating a monitor is to create a trigger. These steps differ depending on whether you chose **Define using visual graph** or **Define using extraction query** or **Define using Anomaly detector** when you created the monitor.
Either way, you begin by specifying a name and severity level for the trigger. Severity levels help you manage alerts. A trigger with a high severity level (e.g. 1) might page a specific individual, whereas a trigger with a low severity level might message a chat room.
### Visual graph
For **Trigger condition**, specify a threshold for the aggregation and timeframe you chose earlier, such as "is below 1,000" or "is exactly 10."
The line moves up and down as you increase and decrease the threshold. Once this line is crossed, the trigger evaluates to true.
### Extraction query
For **Trigger condition**, specify a Painless script that returns true or false. Painless is the default OpenSearch scripting language and has a syntax similar to Groovy.
Trigger condition scripts revolve around the `ctx.results[0]` variable, which corresponds to the extraction query response. For example, your script might reference `ctx.results[0].hits.total.value` or `ctx.results[0].hits.hits[i]._source.error_code`.
A return value of true means the trigger condition has been met, and the trigger should execute its actions. Test your script using the **Run** button.
The **Info** link next to **Trigger condition** contains a useful summary of the variables and results available to your query.
{: .tip }
### Anomaly detector
For **Trigger type**, choose **Anomaly detector grade and confidence**.
Specify the **Anomaly grade condition** for the aggregation and timeframe you chose earlier, "IS ABOVE 0.7" or "IS EXACTLY 0.5." The *anomaly grade* is a number between 0 and 1 that indicates the level of severity of how anomalous a data point is.
Specify the **Anomaly confidence condition** for the aggregation and timeframe you chose earlier, "IS ABOVE 0.7" or "IS EXACTLY 0.5." The *anomaly confidence* is an estimate of the probability that the reported anomaly grade matches the expected anomaly grade.
The line moves up and down as you increase and decrease the threshold. Once this line is crossed, the trigger evaluates to true.
#### Sample scripts
{::comment}
These scripts are Painless, not Groovy, but calling them Groovy in Jekyll gets us syntax highlighting in the generated HTML.
{:/comment}
```groovy
// Evaluates to true if the query returned any documents
ctx.results[0].hits.total.value > 0
```
```groovy
// Returns true if the avg_cpu aggregation exceeds 90
if (ctx.results[0].aggregations.avg_cpu.value > 90) {
return true;
}
```
```groovy
// Performs some crude custom scoring and returns true if that score exceeds a certain value
int score = 0;
for (int i = 0; i < ctx.results[0].hits.hits.length; i++) {
// Weighs 500 errors 10 times as heavily as 503 errors
if (ctx.results[0].hits.hits[i]._source.http_status_code == "500") {
score += 10;
} else if (ctx.results[0].hits.hits[i]._source.http_status_code == "503") {
score += 1;
}
}
if (score > 99) {
return true;
} else {
return false;
}
```
Below are some variables you can include in your message using Mustache templates to see more information about your monitors.
### Available variables
#### Monitor variables
Variable | Data Type | Description
:--- | :--- | :---
`ctx.monitor` | JSON | Includes `ctx.monitor.name`, `ctx.monitor.type`, `ctx.monitor.enabled`, `ctx.monitor.enabled_time`, `ctx.monitor.schedule`, `ctx.monitor.inputs`, `triggers` and `ctx.monitor.last_update_time`.
`ctx.monitor.user` | JSON | Includes information about the user who created the monitor. Includes `ctx.monitor.user.backend_roles` and `ctx.monitor.user.roles`, which are arrays that contain the backend roles and roles assigned to the user. See [alerting security]({{site.url}}{{site.baseurl}}/monitoring-plugins/alerting/security/) for more information.
`ctx.monitor.enabled` | Boolean | Whether the monitor is enabled.
`ctx.monitor.enabled_time` | Milliseconds | Unix epoch time of when the monitor was last enabled.
`ctx.monitor.schedule` | JSON | Contains a schedule of how often or when the monitor should run.
`ctx.monitor.schedule.period.interval` | Integer | The interval at which the monitor runs.
`ctx.monitor.schedule.period.unit` | String | The interval's unit of time.
`ctx.monitor.inputs` | Array | An array that contains the indices and definition used to create the monitor.
`ctx.monitor.inputs.search.indices` | Array | An array that contains the indices the monitor observes.
`ctx.monitor.inputs.search.query` | N/A | The definition used to define the monitor.
#### Trigger variables
Variable | Data Type | Description
:--- | :--- | : ---
`ctx.trigger.id` | String | The trigger's ID.
`ctx.trigger.name` | String | The trigger's name.
`ctx.trigger.severity` | String | The trigger's severity.
`ctx.trigger.condition`| JSON | Contains the Painless script used when creating the monitor.
`ctx.trigger.condition.script.source` | String | The language used to define the script. Must be painless.
`ctx.trigger.condition.script.lang` | String | The script used to define the trigger.
`ctx.trigger.actions`| Array | An array with one element that contains information about the action the monitor needs to trigger.
#### Action variables
Variable | Data Type | Description
:--- | :--- | : ---
`ctx.trigger.actions.id` | String | The action's ID.
`ctx.trigger.actions.name` | String | The action's name.
`ctx.trigger.actions.destination_id`| String | The alert destination's ID.
`ctx.trigger.actions.message_template.source` | String | The message to send in the alert.
`ctx.trigger.actions.message_template.lang` | String | The scripting language used to define the message. Must be Mustache.
`ctx.trigger.actions.throttle_enabled` | Boolean | Whether throttling is enabled for this trigger. See [adding actions](#add-actions/) for more information about throttling.
`ctx.trigger.actions.subject_template.source` | String | The message's subject in the alert.
`ctx.trigger.actions.subject_template.lang` | String | The scripting language used to define the subject. Must be mustache.
#### Other variables
Variable | Data Type | Description
:--- | :--- : :---
`ctx.results` | Array | An array with one element (i.e. `ctx.results[0]`). Contains the query results. This variable is empty if the trigger was unable to retrieve results. See `ctx.error`.
`ctx.last_update_time` | Milliseconds | Unix epoch time of when the monitor was last updated.
`ctx.periodStart` | String | Unix timestamp for the beginning of the period during which the alert triggered. For example, if a monitor runs every ten minutes, a period might begin at 10:40 and end at 10:50.
`ctx.periodEnd` | String | The end of the period during which the alert triggered.
`ctx.error` | String | The error message if the trigger was unable to retrieve results or unable to evaluate the trigger, typically due to a compile error or null pointer exception. Null otherwise.
`ctx.alert` | JSON | The current, active alert (if it exists). Includes `ctx.alert.id`, `ctx.alert.version`, and `ctx.alert.isAcknowledged`. Null if no alert is active.
---
## Add actions
The final step in creating a monitor is to add one or more actions. Actions send notifications when trigger conditions are met and support [Slack](https://slack.com/), [Amazon Chime](https://aws.amazon.com/chime/), and webhooks.
If you don't want to receive notifications for alerts, you don't have to add actions to your triggers. Instead, you can periodically check OpenSearch Dashboards.
{: .tip }
1. Specify a name for the action.
1. Choose a destination.
1. Add a subject and body for the message.
You can add variables to your messages using [Mustache templates](https://mustache.github.io/mustache.5.html). You have access to `ctx.action.name`, the name of the current action, as well as all [trigger variables](#available-variables).
If your destination is a custom webhook that expects a particular data format, you might need to include JSON (or even XML) directly in the message body:
```json
{% raw %}{ "text": "Monitor {{ctx.monitor.name}} just entered alert status. Please investigate the issue. - Trigger: {{ctx.trigger.name}} - Severity: {{ctx.trigger.severity}} - Period start: {{ctx.periodStart}} - Period end: {{ctx.periodEnd}}" }{% endraw %}
```
In this case, the message content must conform to the `Content-Type` header in the [custom webhook](#create-destinations).
1. (Optional) Use action throttling to limit the number of notifications you receive within a given span of time.
For example, if a monitor checks a trigger condition every minute, you could receive one notification per minute. If you set action throttling to 60 minutes, you receive no more than one notification per hour, even if the trigger condition is met dozens of times in that hour.
1. Choose **Create**.
After an action sends a message, the content of that message has left the purview of the security plugin. Securing access to the message (e.g. access to the Slack channel) is your responsibility.
#### Sample message
```mustache
{% raw %}Monitor {{ctx.monitor.name}} just entered an alert state. Please investigate the issue.
- Trigger: {{ctx.trigger.name}}
- Severity: {{ctx.trigger.severity}}
- Period start: {{ctx.periodStart}}
- Period end: {{ctx.periodEnd}}{% endraw %}
```
If you want to use the `ctx.results` variable in a message, use `{% raw %}{{ctx.results.0}}{% endraw %}` rather than `{% raw %}{{ctx.results[0]}}{% endraw %}`. This difference is due to how Mustache handles bracket notation.
{: .note }
---
## Work with alerts
Alerts persist until you resolve the root cause and have the following states:
State | Description
:--- | :---
Active | The alert is ongoing and unacknowledged. Alerts remain in this state until you acknowledge them, delete the trigger associated with the alert, or delete the monitor entirely.
Acknowledged | Someone has acknowledged the alert, but not fixed the root cause.
Completed | The alert is no longer ongoing. Alerts enter this state after the corresponding trigger evaluates to false.
Error | An error occurred while executing the trigger---usually the result of a a bad trigger or destination.
Deleted | Someone deleted the monitor or trigger associated with this alert while the alert was ongoing.

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---
layout: default
title: Alerting security
nav_order: 10
parent: Alerting
has_children: false
---
# Alerting security
If you use the security plugin alongside alerting, you might want to limit certain users to certain actions. For example, you might want some users to only be able to view and acknowledge alerts, while others can modify monitors and destinations.
## Basic permissions
The security plugin has three built-in roles that cover most alerting use cases: `alerting_read_access`, `alerting_ack_alerts`, and `alerting_full_access`. For descriptions of each, see [Predefined roles]({{site.url}}{{site.baseurl}}/security-plugin/access-control/users-roles#predefined-roles).
If these roles don't meet your needs, mix and match individual alerting [permissions]({{site.url}}{{site.baseurl}}/security-plugin/access-control/permissions/) to suit your use case. Each action corresponds to an operation in the REST API. For example, the `cluster:admin/opensearch/alerting/destination/delete` permission lets you delete destinations.
## How monitors access data
Monitors run with the permissions of the user who created or last modified them. For example, consider the user `jdoe`, who works at a chain of retail stores. `jdoe` has two roles. Together, these two roles allow read access to three indices: `store1-returns`, `store2-returns`, and `store3-returns`.
`jdoe` creates a monitor that sends an email to management whenever the number of returns across all three indices exceeds 40 per hour.
Later, the user `psantos` wants to edit the monitor to run every two hours, but `psantos` only has access to `store1-returns`. To make the change, `psantos` has two options:
- Update the monitor so that it only checks `store1-returns`.
- Ask an administrator for read access to the other two indices.
After making the change, the monitor now runs with the same permissions as `psantos`, including any [document-level security]({{site.url}}{{site.baseurl}}/security-plugin/access-control/document-level-security/) queries, [excluded fields]({{site.url}}{{site.baseurl}}/security-plugin/access-control/field-level-security/), and [masked fields]({{site.url}}{{site.baseurl}}/security-plugin/access-control/field-masking/). If you use an extraction query to define your monitor, use the **Run** button to ensure that the response includes the fields you need.
## (Advanced) Limit access by backend role
Out of the box, the alerting plugin has no concept of ownership. For example, if you have the `cluster:admin/opensearch/alerting/monitor/write` permission, you can edit *all* monitors, regardless of whether you created them. If a small number of trusted users manage your monitors and destinations, this lack of ownership generally isn't a problem. A larger organization might need to segment access by backend role.
First, make sure that your users have the appropriate [backend roles]({{site.url}}{{site.baseurl}}/security-plugin/access-control/index/). Backend roles usually come from an [LDAP server]({{site.url}}{{site.baseurl}}/security-plugin/configuration/ldap/) or [SAML provider]({{site.url}}{{site.baseurl}}/security-plugin/configuration/saml/). However, if you use the internal user database, you can use the REST API to [add them manually]({{site.url}}{{site.baseurl}}/security-plugin/access-control/api#create-user).
Next, enable the following setting:
```json
PUT _cluster/settings
{
"transient": {
"plugins.alerting.filter_by_backend_roles": "true"
}
}
```
Now when users view alerting resources in OpenSearch Dashboards (or make REST API calls), they only see monitors and destinations that are created by users who share *at least one* backend role. For example, consider three users who all have full access to alerting: `jdoe`, `jroe`, and `psantos`.
`jdoe` and `jroe` are on the same team at work and both have the `analyst` backend role. `psantos` has the `human-resources` backend role.
If `jdoe` creates a monitor, `jroe` can see and modify it, but `psantos` can't. If that monitor generates an alert, the situation is the same: `jroe` can see and acknowledge it, but `psantos` can't. If `psantos` creates a destination, `jdoe` and `jroe` can't see or modify it.
<!-- ## (Advanced) Limit access by individual
If you only want users to be able to see and modify their own monitors and destinations, duplicate the `alerting_full_access` role and add the following [DLS query]({{site.url}}{{site.baseurl}}/security-plugin/access-control/document-level-security/) to it:
```json
{
"bool": {
"should": [{
"match": {
"monitor.created_by": "${user.name}"
}
}, {
"match": {
"destination.created_by": "${user.name}"
}
}]
}
}
```
Then, use this new role for all alerting users. -->

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---
layout: default
title: Management
parent: Alerting
nav_order: 5
---
# Management
## Alerting indices
The alerting feature creates several indices and one alias. The security plugin demo script configures them as [system indices]({{site.url}}{{site.baseurl}}/security-plugin/configuration/system-indices/) for an extra layer of protection. Don't delete these indices or modify their contents without using the alerting APIs.
Index | Purpose
:--- | :---
`.opendistro-alerting-alerts` | Stores ongoing alerts.
`.opendistro-alerting-alert-history-<date>` | Stores a history of completed alerts.
`.opendistro-alerting-config` | Stores monitors, triggers, and destinations. [Take a snapshot]({{site.url}}{{site.baseurl}}/opensearch/snapshot-restore) of this index to back up your alerting configuration.
`.opendistro-alerting-alert-history-write` (alias) | Provides a consistent URI for the `.opendistro-alerting-alert-history-<date>` index.
All alerting indices are hidden by default. For a summary, make the following request:
```
GET _cat/indices?expand_wildcards=open,hidden
```
## Alerting settings
We don't recommend changing these settings; the defaults should work well for most use cases.
All settings are available using the OpenSearch `_cluster/settings` API. None require a restart, and all can be marked `persistent` or `transient`.
Setting | Default | Description
:--- | :--- | :---
`plugins.scheduled_jobs.enabled` | true | Whether the alerting plugin is enabled or not. If disabled, all monitors immediately stop running.
`plugins.alerting.index_timeout` | 60s | The timeout for creating monitors and destinations using the REST APIs.
`plugins.alerting.request_timeout` | 10s | The timeout for miscellaneous requests from the plugin.
`plugins.alerting.action_throttle_max_value` | 24h | The maximum amount of time you can set for action throttling. By default, this value displays as 1440 minutes in OpenSearch Dashboards.
`plugins.alerting.input_timeout` | 30s | How long the monitor can take to issue the search request.
`plugins.alerting.bulk_timeout` | 120s | How long the monitor can write alerts to the alert index.
`plugins.alerting.alert_backoff_count` | 3 | The number of retries for writing alerts before the operation fails.
`plugins.alerting.alert_backoff_millis` | 50ms | The amount of time to wait between retries---increases exponentially after each failed retry.
`plugins.alerting.alert_history_rollover_period` | 12h | How frequently to check whether the `.opendistro-alerting-alert-history-write` alias should roll over to a new history index and whether the Alerting plugin should delete any history indices.
`plugins.alerting.move_alerts_backoff_millis` | 250 | The amount of time to wait between retries---increases exponentially after each failed retry.
`plugins.alerting.move_alerts_backoff_count` | 3 | The number of retries for moving alerts to a deleted state after their monitor or trigger has been deleted.
`plugins.alerting.monitor.max_monitors` | 1000 | The maximum number of monitors users can create.
`plugins.alerting.alert_history_max_age` | 30d | The oldest document to store in the `.opendistro-alert-history-<date>` index before creating a new index. If the number of alerts in this time period does not exceed `alert_history_max_docs`, alerting creates one history index per period (e.g. one index every 30 days).
`plugins.alerting.alert_history_max_docs` | 1000 | The maximum number of alerts to store in the `.opendistro-alert-history-<date>` index before creating a new index.
`plugins.alerting.alert_history_enabled` | true | Whether to create `.opendistro-alerting-alert-history-<date>` indices.
`plugins.alerting.alert_history_retention_period` | 60d | The amount of time to keep history indices before automatically deleting them.
`plugins.alerting.destination.allow_list` | ["chime", "slack", "custom_webhook", "email", "test_action"] | The list of allowed destinations. If you don't want to allow users to a certain type of destination, you can remove it from this list, but we recommend leaving this setting as-is.
`plugins.alerting.filter_by_backend_roles` | "false" | Restricts access to monitors by backend role. See [Alerting security]({{site.url}}{{site.baseurl}}/monitoring-plugins/alerting/security/).
`plugins.scheduled_jobs.sweeper.period` | 5m | The alerting feature uses its "job sweeper" component to periodically check for new or updated jobs. This setting is the rate at which the sweeper checks to see if any jobs (monitors) have changed and need to be rescheduled.
`plugins.scheduled_jobs.sweeper.page_size` | 100 | The page size for the sweeper. You shouldn't need to change this value.
`plugins.scheduled_jobs.sweeper.backoff_millis` | 50ms | The amount of time the sweeper waits between retries---increases exponentially after each failed retry.
`plugins.scheduled_jobs.sweeper.retry_count` | 3 | The total number of times the sweeper should retry before throwing an error.
`plugins.scheduled_jobs.request_timeout` | 10s | The timeout for the request that sweeps shards for jobs.

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---
layout: default
title: API
parent: Performance Analyzer
nav_order: 1
---
# Performance Analyzer API
Performance Analyzer uses a single HTTP method and URI for most requests:
```
GET <endpoint>:9600/_plugins/_performanceanalyzer/metrics
```
Note the use of port 9600. Provide parameters for metrics, aggregations, dimensions, and nodes (optional):
```
?metrics=<metrics>&agg=<aggregations>&dim=<dimensions>&nodes=all"
```
For a full list of metrics, see [Metrics reference]({{site.url}}{{site.baseurl}}/monitoring-plugins/pa/reference/). Performance Analyzer updates its data every five seconds. If you create a custom client, we recommend using that same interval for calls to the API.
#### Sample request
```
GET localhost:9600/_plugins/_performanceanalyzer/metrics?metrics=Latency,CPU_Utilization&agg=avg,max&dim=ShardID&nodes=all
```
#### Sample response
```json
{
"keHlhQbbTpm1BYicficEQg": {
"timestamp": 1554940530000,
"data": {
"fields": [{
"name": "ShardID",
"type": "VARCHAR"
},
{
"name": "Latency",
"type": "DOUBLE"
},
{
"name": "CPU_Utilization",
"type": "DOUBLE"
}
],
"records": [
[
null,
null,
0.012552206029147535
],
[
"1",
4.8,
0.0009780939762972104
]
]
}
},
"bHdpbMJZTs-TKtZro2SmYA": {
"timestamp": 1554940530000,
"data": {
"fields": [{
"name": "ShardID",
"type": "VARCHAR"
},
{
"name": "Latency",
"type": "DOUBLE"
},
{
"name": "CPU_Utilization",
"type": "DOUBLE"
}
],
"records": [
[
null,
18.2,
0.011966493817311527
],
[
"1",
14.8,
0.0007670829370071493
]
]
}
}
}
```
In this case, each top-level object represents a node. The API returns names and data types for the metrics and dimensions that you specified, along with values from five seconds ago and current values (if different). Null values represent inactivity during that time period.
Performance Analyzer has one additional URI that returns the unit for each metric.
#### Sample request
```
GET localhost:9600/_plugins/_performanceanalyzer/metrics/units
```
#### Sample response
```json
{
"Disk_Utilization": "%",
"Cache_Request_Hit": "count",
"TermVectors_Memory": "B",
"Segments_Memory": "B",
"HTTP_RequestDocs": "count",
"Net_TCP_Lost": "segments/flow",
"Refresh_Time": "ms",
"GC_Collection_Event": "count",
"Merge_Time": "ms",
"Sched_CtxRate": "count/s",
"Cache_Request_Size": "B",
"ThreadPool_QueueSize": "count",
"Sched_Runtime": "s/ctxswitch",
"Disk_ServiceRate": "MB/s",
"Heap_AllocRate": "B/s",
"Heap_Max": "B",
"Sched_Waittime": "s/ctxswitch",
"ShardBulkDocs": "count",
"Thread_Blocked_Time": "s/event",
"VersionMap_Memory": "B",
"Master_Task_Queue_Time": "ms",
"Merge_CurrentEvent": "count",
"Indexing_Buffer": "B",
"Bitset_Memory": "B",
"Norms_Memory": "B",
"Net_PacketDropRate4": "packets/s",
"Heap_Committed": "B",
"Net_PacketDropRate6": "packets/s",
"Thread_Blocked_Event": "count",
"GC_Collection_Time": "ms",
"Cache_Query_Miss": "count",
"IO_TotThroughput": "B/s",
"Latency": "ms",
"Net_PacketRate6": "packets/s",
"Cache_Query_Hit": "count",
"IO_ReadSyscallRate": "count/s",
"Net_PacketRate4": "packets/s",
"Cache_Request_Miss": "count",
"CB_ConfiguredSize": "B",
"CB_TrippedEvents": "count",
"ThreadPool_RejectedReqs": "count",
"Disk_WaitTime": "ms",
"Net_TCP_TxQ": "segments/flow",
"Master_Task_Run_Time": "ms",
"IO_WriteSyscallRate": "count/s",
"IO_WriteThroughput": "B/s",
"Flush_Event": "count",
"Net_TCP_RxQ": "segments/flow",
"Refresh_Event": "count",
"Points_Memory": "B",
"Flush_Time": "ms",
"Heap_Init": "B",
"CPU_Utilization": "cores",
"HTTP_TotalRequests": "count",
"ThreadPool_ActiveThreads": "count",
"Cache_Query_Size": "B",
"Paging_MinfltRate": "count/s",
"Merge_Event": "count",
"Net_TCP_SendCWND": "B/flow",
"Cache_Request_Eviction": "count",
"Segments_Total": "count",
"Terms_Memory": "B",
"DocValues_Memory": "B",
"Heap_Used": "B",
"Cache_FieldData_Eviction": "count",
"IO_TotalSyscallRate": "count/s",
"CB_EstimatedSize": "B",
"Net_Throughput": "B/s",
"Paging_RSS": "pages",
"Indexing_ThrottleTime": "ms",
"StoredFields_Memory": "B",
"IndexWriter_Memory": "B",
"Master_PendingQueueSize": "count",
"Net_TCP_SSThresh": "B/flow",
"Cache_FieldData_Size": "B",
"Paging_MajfltRate": "count/s",
"ThreadPool_TotalThreads": "count",
"IO_ReadThroughput": "B/s",
"ShardEvents": "count",
"Net_TCP_NumFlows": "count"
}
```

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---
layout: default
title: Create Dashboards
parent: Performance Analyzer
nav_order: 2
---
# PerfTop dashboards
Dashboards are defined in JSON and composed of three main elements: tables, line graphs, and bar graphs. You define a grid of rows and columns and then place elements within that grid, with each element spanning as many rows and columns as you specify.
The best way to get started with building custom dashboards is to duplicate and modify one of the existing JSON files in the `dashboards` directory.
{: .tip }
---
#### Table of contents
1. TOC
{:toc}
---
## Summary of elements
- Tables show metrics per dimension. For example, if your metric is `CPU_Utilization` and your dimension `ShardID`, a PerfTop table shows a row for each shard on each node.
- Bar graphs are aggregated for the cluster, unless you add `nodeName` to the dashboard. See the [options for all elements](#all-elements).
- Line graphs are aggregated for each node. Each line represents a node.
## Position elements
PerfTop positions elements within a grid. For example, consider this 12 * 12 grid.
![Dashboard grid]({{site.url}}{{site.baseurl}}/images/perftop-grid.png)
The upper-left of the grid represents row 0, column 0, so the starting positions for the three boxes are:
- Orange: row 0, column 0
- Purple: row 2, column 2
- Green: row 1, column 6
These boxes span a number of rows and columns. In this case:
- Orange: 2 rows, 4 columns
- Purple: 1 row, 4 columns
- Green: 3 rows, 2 columns
In JSON form, we have the following:
```json
{
"gridOptions": {
"rows": 12,
"cols": 12
},
"graphs": {
"tables": [{
"options": {
"gridPosition": {
"row": 0,
"col": 0,
"rowSpan": 2,
"colSpan": 4
}
}
},
{
"options": {
"gridPosition": {
"row": 2,
"col": 2,
"rowSpan": 1,
"colSpan": 4
}
}
},
{
"options": {
"gridPosition": {
"row": 1,
"col": 6,
"rowSpan": 3,
"colSpan": 2
}
}
}
]
}
}
```
At this point, however, all the JSON does is define the size and position of three tables. To fill elements with data, you specify a query.
## Add queries
Queries use the same elements as the [REST API]({{site.url}}{{site.baseurl}}/monitoring-plugins/pa/api/), just in JSON form:
```json
{
"queryParams": {
"metrics": "estimated,limitConfigured",
"aggregates": "avg,avg",
"dimensions": "type",
"sortBy": "estimated"
}
}
```
For details on available metrics, see [Metrics reference]({{site.url}}{{site.baseurl}}/monitoring-plugins/pa/reference/).
## Add options
Options include labels, colors, and a refresh interval. Different elements types have different options.
Dashboards support the 16 ANSI colors: black, red, green, yellow, blue, magenta, cyan, and white. For the "bright" variants of these colors, use the numbers 8--15. If your terminal supports 256 colors, you can also use hex codes (e.g. `#6D40ED`).
{: .note }
### All elements
Option | Type | Description
:--- | :--- | :---
`label` | String or integer | The text in the upper-left corner of the box.
`labelColor` | String or integer | The color of the label.
`refreshInterval` | Integer | The number of milliseconds between calls to the Performance Analyzer API for new data. Minimum value is 5000.
`dimensionFilters` | String array | The dimension value to diplay for the graph. For example, if you query for `metric=Net_Throughput&agg=sum&dim=Direction` and the possible dimension values are `in` and `out`, you can define `dimensionFilters: ["in"]` to only display the metric data for `in` dimension
`nodeName` | String | If non-null, lets you restrict elements to individual nodes. You can specify the node name directly in the dashboard file, but the better approach is to use `"nodeName": "#nodeName"` in the dashboard and include the `--nodename <node_name>` argument when starting PerfTop.
### Tables
Option | Type | Description
:--- | :--- | :---
`bg` | String or integer | The background color.
`fg` | String or integer | The text color.
`selectedFg` | String or integer | The text color for focused text.
`selectedBg` | String or integer | The background color for focused text.
`columnSpacing` | Integer | The amount of space (measured in characters) between columns.
`keys` | Boolean | Has no impact at this time.
### Bars
Option | Type | Description
:--- | :--- | :---
`barWidth` | Integer | The width of each bar (measured in characters) in the graph.
`xOffset` | Integer | The amount of space (measured in characters) between the y-axis and the first bar in the graph.
`maxHeight` | Integer | The maximum height of each bar (measured in characters) in the graph.
### Lines
Option | Type | Description
:--- | :--- | :---
`showNthLabel` | Integer | Which of the `xAxis` labels to show. For example, `"showNthLabel": 2` shows every other label.
`showLegend` | Boolean | Whether or not to display a legend for the line graph.
`legend.width` | Integer | The width of the legend (measured in characters) in the graph.
`xAxis` | String array | Array of labels for the x-axis. For example, `["0:00", "0:10", "0:20", "0:30", "0:40", "0:50"]`.
`colors` | String array | Array of line colors to choose from. For example, `["magenta", "cyan"]`. If you don't provide this value, PerfTop chooses random colors for each line.

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---
layout: default
title: Performance Analyzer
nav_order: 58
has_children: true
redirect_from:
- /monitoring-plugins/pa/
---
# Performance Analyzer
Performance Analyzer is an agent and REST API that allows you to query numerous performance metrics for your cluster, including aggregations of those metrics, independent of the Java Virtual Machine (JVM). PerfTop is the default command line interface (CLI) for displaying those metrics.
To download PerfTop, see [Download](https://opensearch.org/downloads.html) on the OpenSearch website.
You can also install it using [npm](https://www.npmjs.com/):
```bash
npm install -g @aws/opensearch-perftop
```
![PerfTop screenshot]({{site.url}}{{site.baseurl}}/images/perftop.png)
## Get started with PerfTop
The basic syntax is:
```bash
./opensearch-perf-top-<operating_system> --dashboard <dashboard>.json --endpoint <endpoint>
```
If you're using npm, the syntax is similar:
```bash
opensearch-perf-top --dashboard <dashboard> --endpoint <endpoint>
```
If you're running PerfTop from a node (i.e. locally), specify port 9600:
```bash
./opensearch-perf-top-linux --dashboard dashboards/<dashboard>.json --endpoint localhost:9600
```
Otherwise, just specify the OpenSearch endpoint:
```bash
./opensearch-perf-top-macos --dashboard dashboards/<dashboard>.json --endpoint my-cluster.my-domain.com
```
PerfTop has four pre-built dashboards in the `dashboards` directory, but you can also [create your own]({{site.url}}{{site.baseurl}}/dashboards/).
You can also load the pre-built dashboards (ClusterOverview, ClusterNetworkMemoryAnalysis, ClusterThreadAnalysis, or NodeAnalysis) without the JSON files, such as `--dashboard ClusterThreadAnalysis`.
PerfTop has no interactivity. Start the application, monitor the dashboard, and press esc, q, or Ctrl + C to quit.
{: .note }
### Other options
- For NodeAnalysis and similar custom dashboards, you can add the `--nodename <node_name>` argument if you want your dashboard to display metrics for only a single node.
- For troubleshooting, add the `--logfile <log-file>.txt` argument.
## Performance Analyzer configuration
### Storage
Performance Analyzer uses `/dev/shm` for temporary storage. During heavy workloads on a cluster, Performance Analyzer can use up to 1 GB of space.
Docker, however, has a default `/dev/shm` size of 64 MB. To change this value, you can use the `docker run --shm-size 1gb` flag or [a similar setting in Docker Compose](https://docs.docker.com/compose/compose-file#shm_size).
If you're not using Docker, check the size of `/dev/shm` using `df -h`. The default value is probably plenty, but if you need to change its size, add the following line to `/etc/fstab`:
```bash
tmpfs /dev/shm tmpfs defaults,noexec,nosuid,size=1G 0 0
```
Then remount the file system:
```bash
mount -o remount /dev/shm
```
### Security
Performance Analyzer supports encryption in transit for requests. It currently does *not* support client or server authentication for requests. To enable encryption in transit, edit `performance-analyzer.properties` in your `$OPENSEARCH_HOME` directory:
```bash
vi $OPENSEARCH_HOME/plugins/opensearch-performance-analyzer/pa_config/performance-analyzer.properties
```
Change the following lines to configure encryption in transit. Note that `certificate-file-path` must be a certificate for the server, not a root CA:
```
https-enabled = true
#Setup the correct path for certificates
certificate-file-path = specify_path
private-key-file-path = specify_path
```

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---
layout: default
title: API
parent: Root Cause Analysis
grand_parent: Performance Analyzer
nav_order: 1
---
# RCA API
## Sample request
```
# Request all available RCAs
GET localhost:9600/_plugins/_performanceanalyzer/rca
# Request a specific RCA
GET localhost:9600/_plugins/_performanceanalyzer/rca?name=HighHeapUsageClusterRca
```
## Sample response
```json
{
"HighHeapUsageClusterRca": [{
"rca_name": "HighHeapUsageClusterRca",
"state": "unhealthy",
"timestamp": 1587426650942,
"HotClusterSummary": [{
"number_of_nodes": 2,
"number_of_unhealthy_nodes": 1,
"HotNodeSummary": [{
"host_address": "192.168.144.2",
"node_id": "JtlEoRowSI6iNpzpjlbp_Q",
"HotResourceSummary": [{
"resource_type": "old gen",
"threshold": 0.65,
"value": 0.81827232588145373,
"avg": NaN,
"max": NaN,
"min": NaN,
"unit_type": "heap usage in percentage",
"time_period_seconds": 600,
"TopConsumerSummary": [{
"name": "CACHE_FIELDDATA_SIZE",
"value": 590702564
},
{
"name": "CACHE_REQUEST_SIZE",
"value": 28375
},
{
"name": "CACHE_QUERY_SIZE",
"value": 12687
}
],
}]
}]
}]
}]
}
```

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---
layout: default
title: Root Cause Analysis
nav_order: 50
parent: Performance Analyzer
has_children: true
---
# Root Cause Analysis
The OpenSearch Performance Analyzer plugin (PA) captures OpenSearch and JVM activity, plus their lower-level resource usage (e.g. disk, network, CPU, and memory). Based on this instrumentation, Performance Analyzer computes and exposes diagnostic metrics so that administrators can measure and understand the bottlenecks in their OpenSearch clusters.
The Root Cause Analysis framework (RCA) uses the information from PA to alert administrators about the root cause of performance and availability issues that their clusters might be experiencing.
In broad strokes, the framework helps you access data streams from OpenSearch nodes running Performance Analyzer. You write snippets of Java to choose the streams that matter to you and evaluate the streams' PA metrics against certain thresholds. As RCA runs, you can access the state of each analysis using the REST API.
To learn more about Root Cause Analysis, see [its repository on GitHub](https://github.com/opensearch-project/performance-analyzer-rca).

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---
layout: default
title: RCA Reference
parent: Root Cause Analysis
grand_parent: Performance Analyzer
nav_order: 3
---
# RCA reference
You can find a reference of available RCAs and their purposes on [GitHub](https://github.com/opensearch-project/performance-analyzer-rca/tree/main/docs).

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---
layout: default
title: Metrics Reference
parent: Performance Analyzer
nav_order: 3
---
# Metrics reference
This page contains all Performance Analyzer metrics. All metrics support the `avg`, `sum`, `min`, and `max` aggregations, although certain metrics measure only one thing, making the choice of aggregation irrelevant.
For information on dimensions, see the [dimensions reference](#dimensions-reference).
This list is extensive. We recommend using Ctrl/Cmd + F to find what you're looking for.
{: .tip }
<table>
<thead style="text-align: left">
<tr>
<th>Metric</th>
<th>Dimensions</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>CPU_Utilization
</td>
<td rowspan="18">ShardID, IndexName, Operation, ShardRole
</td>
<td>CPU usage ratio. CPU time (in milliseconds) used by the associated thread(s) in the past five seconds, divided by 5000 milliseconds.
</td>
</tr>
<tr>
<td>Paging_MajfltRate
</td>
<td>The number of major faults per second in the past five seconds. A major fault requires the process to load a memory page from disk.
</td>
</tr>
<tr>
<td>Paging_MinfltRate
</td>
<td>The number of minor faults per second in the past five seconds. A minor fault does not requires the process to load a memory page from disk.
</td>
</tr>
<tr>
<td>Paging_RSS
</td>
<td>The number of pages the process has in real memory---the pages that count towards text, data, or stack space. This number does not include pages that have not been demand-loaded in or swapped out.
</td>
</tr>
<tr>
<td>Sched_Runtime
</td>
<td>Time (seconds) spent executing on the CPU per context switch.
</td>
</tr>
<tr>
<td>Sched_Waittime
</td>
<td>Time (seconds) spent waiting on a run queue per context switch.
</td>
</tr>
<tr>
<td>Sched_CtxRate
</td>
<td>Number of times run on the CPU per second in the past five seconds.
</td>
</tr>
<tr>
<td>Heap_AllocRate
</td>
<td>An approximation of the heap memory allocated, in bytes, per second in the past five seconds
</td>
</tr>
<tr>
<td>IO_ReadThroughput
</td>
<td>Number of bytes read per second in the last five seconds.
</td>
</tr>
<tr>
<td>IO_WriteThroughput
</td>
<td>Number of bytes written per second in the last five seconds.
</td>
</tr>
<tr>
<td>IO_TotThroughput
</td>
<td>Number of bytes read or written per second in the last five seconds.
</td>
</tr>
<tr>
<td>IO_ReadSyscallRate
</td>
<td>Read system calls per second in the last five seconds.
</td>
</tr>
<tr>
<td>IO_WriteSyscallRate
</td>
<td>Write system calls per second in the last five seconds.
</td>
</tr>
<tr>
<td>IO_TotalSyscallRate
</td>
<td>Read and write system calls per second in the last five seconds.
</td>
</tr>
<tr>
<td>Thread_Blocked_Time
</td>
<td>Average time (seconds) that the associated thread(s) blocked to enter or reenter a monitor.
</td>
</tr>
<tr>
<td>Thread_Blocked_Event
</td>
<td>The total number of times that the associated thread(s) blocked to enter or reenter a monitor (i.e. the number of times a thread has been in the blocked state).
</td>
</tr>
<tr>
<td>ShardEvents
</td>
<td>The total number of events executed on a shard in the past five seconds.
</td>
</tr>
<tr>
<td>ShardBulkDocs
</td>
<td>The total number of documents indexed in the past five seconds.
</td>
</tr>
<tr>
<td>Indexing_ThrottleTime
</td>
<td rowspan="30">ShardID, IndexName
</td>
<td>Time (milliseconds) that the index has been under merge throttling control in the past five seconds.
</td>
</tr>
<tr>
<td>Cache_Query_Hit
</td>
<td>The number of successful lookups in the query cache in the past five seconds.
</td>
</tr>
<tr>
<td>Cache_Query_Miss
</td>
<td>The number of lookups in the query cache that failed to retrieve a `DocIdSet` in the past five seconds. `DocIdSet` is a set of document IDs in Lucene.
</td>
</tr>
<tr>
<td>Cache_Query_Size
</td>
<td>Query cache memory size in bytes.
</td>
</tr>
<tr>
<td>Cache_FieldData_Eviction
</td>
<td>The number of times OpenSearch has evicted data from the fielddata heap space (occurs when the heap space is full) in the past five seconds.
</td>
</tr>
<tr>
<td>Cache_FieldData_Size
</td>
<td>Fielddata memory size in bytes.
</td>
</tr>
<tr>
<td>Cache_Request_Hit
</td>
<td>The number of successful lookups in the shard request cache in the past five seconds.
</td>
</tr>
<tr>
<td>Cache_Request_Miss
</td>
<td>The number of lookups in the request cache that failed to retrieve the results of search requests in the past five seconds.
</td>
</tr>
<tr>
<td>Cache_Request_Eviction
</td>
<td>The number of times OpenSearch evicts data from shard request cache (occurs when the request cache is full) in the past five seconds.
</td>
</tr>
<tr>
<td>Cache_Request_Size
</td>
<td>Shard request cache memory size in bytes.
</td>
</tr>
<tr>
<td>Refresh_Event
</td>
<td>The total number of refreshes executed in the past five seconds.
</td>
</tr>
<tr>
<td>Refresh_Time
</td>
<td>The total time (milliseconds) spent executing refreshes in the past five seconds
</td>
</tr>
<tr>
<td>Flush_Event
</td>
<td>The total number of flushes executed in the past five seconds.
</td>
</tr>
<tr>
<td>Flush_Time
</td>
<td>The total time (milliseconds) spent executing flushes in the past five seconds.
</td>
</tr>
<tr>
<td>Merge_Event
</td>
<td>The total number of merges executed in the past five seconds.
</td>
</tr>
<tr>
<td>Merge_Time
</td>
<td>The total time (milliseconds) spent executing merges in the past five seconds.
</td>
</tr>
<tr>
<td>Merge_CurrentEvent
</td>
<td>The current number of merges executing.
</td>
</tr>
<tr>
<td>Indexing_Buffer
</td>
<td>Index buffer memory size in bytes.
</td>
</tr>
<tr>
<td>Segments_Total
</td>
<td>The number of segments.
</td>
</tr>
<tr>
<td>Segments_Memory
</td>
<td>Estimated memory usage of segments in bytes.
</td>
</tr>
<tr>
<td>Terms_Memory
</td>
<td>Estimated memory usage of terms dictionaries in bytes.
</td>
</tr>
<tr>
<td>StoredFields_Memory
</td>
<td>Estimated memory usage of stored fields in bytes.
</td>
</tr>
<tr>
<td>TermVectors_Memory
</td>
<td>Estimated memory usage of term vectors in bytes.
</td>
</tr>
<tr>
<td>Norms_Memory
</td>
<td>Estimated memory usage of norms (normalization factors) in bytes.
</td>
</tr>
<tr>
<td>Points_Memory
</td>
<td>Estimated memory usage of points in bytes.
</td>
</tr>
<tr>
<td>DocValues_Memory
</td>
<td>Estimated memory usage of doc values in bytes.
</td>
</tr>
<tr>
<td>IndexWriter_Memory
</td>
<td>Estimated memory usage by the index writer in bytes.
</td>
</tr>
<tr>
<td>Bitset_Memory
</td>
<td>Estimated memory usage for the cached bit sets in bytes.
</td>
</tr>
<tr>
<td>VersionMap_Memory
</td>
<td>Estimated memory usage of the version map in bytes.
</td>
</tr>
<tr>
<td>Shard_Size_In_Bytes
</td>
<td>Estimated disk usage of the shard in bytes.
</td>
</tr>
<tr>
<td>Latency
</td>
<td>Operation, Exception, Indices, HTTPRespCode, ShardID, IndexName, ShardRole
</td>
<td>Latency (milliseconds) of a request.
</td>
</tr>
<tr>
<td>GC_Collection_Event
</td>
<td rowspan="6">MemType
</td>
<td>The number of garbage collections that have occurred in the past five seconds.
</td>
</tr>
<tr>
<td>GC_Collection_Time
</td>
<td>The approximate accumulated time (milliseconds) of all garbage collections that have occurred in the past five seconds.
</td>
</tr>
<tr>
<td>Heap_Committed
</td>
<td>The amount of memory (bytes) that is committed for the JVM to use.
</td>
</tr>
<tr>
<td>Heap_Init
</td>
<td>The amount of memory (bytes) that the JVM initially requests from the operating system for memory management.
</td>
</tr>
<tr>
<td>Heap_Max
</td>
<td>The maximum amount of memory (bytes) that can be used for memory management.
</td>
</tr>
<tr>
<td>Heap_Used
</td>
<td>The amount of used memory in bytes.
</td>
</tr>
<tr>
<td>Disk_Utilization
</td>
<td rowspan="3">DiskName
</td>
<td>Disk utilization rate: percentage of disk time spent reading and writing by the OpenSearch process in the past five seconds.
</td>
</tr>
<tr>
<td>Disk_WaitTime
</td>
<td>Average duration (milliseconds) of read and write operations in the past five seconds.
</td>
</tr>
<tr>
<td>Disk_ServiceRate
</td>
<td>Service rate: MB read or written per second in the past five seconds. This metric assumes that each disk sector stores 512 bytes.
</td>
</tr>
<tr>
<td>Net_TCP_NumFlows
</td>
<td rowspan="6">DestAddr
</td>
<td>Number of samples collected. Performance Analyzer collects one sample every five seconds.
</td>
</tr>
<tr>
<td>Net_TCP_TxQ
</td>
<td>Average number of TCP packets in the send buffer.
</td>
</tr>
<tr>
<td>Net_TCP_RxQ
</td>
<td>Average number of TCP packets in the receive buffer.
</td>
</tr>
<tr>
<td>Net_TCP_Lost
</td>
<td>Average number of unrecovered recurring timeouts. This number is reset when the recovery finishes or `SND.UNA` is advanced. `SND.UNA` is the sequence number of the first byte of data that has been sent, but not yet acknowledged.
</td>
</tr>
<tr>
<td>Net_TCP_SendCWND
</td>
<td>Average size (bytes) of the sending congestion window.
</td>
</tr>
<tr>
<td>Net_TCP_SSThresh
</td>
<td>Average size (bytes) of the slow start size threshold.
</td>
</tr>
<tr>
<td>Net_PacketRate4
</td>
<td rowspan="5">Direction
</td>
<td>The total number of IPv4 datagrams transmitted/received from/by interfaces per second, including those transmitted or received in error
</td>
</tr>
<tr>
<td>Net_PacketDropRate4
</td>
<td>The total number of IPv4 datagrams transmitted or received in error per second.
</td>
</tr>
<tr>
<td>Net_PacketRate6
</td>
<td>The total number of IPv6 datagrams transmitted or received from or by interfaces per second, including those transmitted or received in error.
</td>
</tr>
<tr>
<td>Net_PacketDropRate6
</td>
<td>The total number of IPv6 datagrams transmitted or received in error per second.
</td>
</tr>
<tr>
<td>Net_Throughput
</td>
<td>The number of bits transmitted or received per second by all network interfaces.
</td>
</tr>
<tr>
<td>ThreadPool_QueueSize
</td>
<td rowspan="4">ThreadPoolType
</td>
<td>The size of the task queue.
</td>
</tr>
<tr>
<td>ThreadPool_RejectedReqs
</td>
<td>The number of rejected executions.
</td>
</tr>
<tr>
<td>ThreadPool_TotalThreads
</td>
<td>The current number of threads in the pool.
</td>
</tr>
<tr>
<td>ThreadPool_ActiveThreads
</td>
<td>The approximate number of threads that are actively executing tasks.
</td>
</tr>
<tr>
<td>Master_PendingQueueSize
</td>
<td>N/A
</td>
<td>The current number of pending tasks in the cluster state update thread. Each node has a cluster state update thread that submits cluster state update tasks (create index, update mapping, allocate shard, fail shard, etc.).
</td>
</tr>
<tr>
<td>HTTP_RequestDocs
</td>
<td rowspan="2">Operation, Exception, Indices, HTTPRespCode
</td>
<td>The number of items in the request (only for `_bulk` request type).
</td>
</tr>
<tr>
<td>HTTP_TotalRequests
</td>
<td>The number of finished requests in the past five seconds.
</td>
</tr>
<tr>
<td>CB_EstimatedSize
</td>
<td rowspan="3">CBType
</td>
<td>The current number of estimated bytes.
</td>
</tr>
<tr>
<td>CB_TrippedEvents
</td>
<td>The number of times the circuit breaker has tripped.
</td>
</tr>
<tr>
<td>CB_ConfiguredSize
</td>
<td>The limit (bytes) for how much memory operations can use.
</td>
</tr>
<tr>
<td>Master_Task_Queue_Time
</td>
<td rowspan="2">MasterTaskInsertOrder, MasterTaskPriority, MasterTaskType, MasterTaskMetadata
</td>
<td>The time (milliseconds) that a master task spent in the queue.
</td>
</tr>
<tr>
<td>Master_Task_Run_Time
</td>
<td>The time (milliseconds) that a master task has been executed.
</td>
</tr>
</tbody>
</table>
## Dimensions reference
Dimension | Return values
:--- | :---
ShardID | ID for the shard (e.g. `1`).
IndexName | Name of the index (e.g. `my-index`).
Operation | Type of operation (e.g. `shardbulk`).
ShardRole | `primary`, `replica`
Exception | OpenSearch exceptions (e.g. `org.opensearch.index_not_found_exception`).
Indices | The list of indices in the request URI.
HTTPRespCode | Response code from OpenSearch (e.g. `200`).
MemType | `totYoungGC`, `totFullGC`, `Survivor`, `PermGen`, `OldGen`, `Eden`, `NonHeap`, `Heap`
DiskName | Name of the disk (e.g. `sda1`).
DestAddr | Destination address (e.g. `010015AC`).
Direction | `in`, `out`
ThreadPoolType | The OpenSearch thread pools (e.g. `index`, `search`,`snapshot`).
CBType | `accounting`, `fielddata`, `in_flight_requests`, `parent`, `request`
MasterTaskInsertOrder | The order in which the task was inserted (e.g. `3691`).
MasterTaskPriority | Priority of the task (e.g. `URGENT`). OpenSearch executes higher priority tasks before lower priority ones, regardless of `insert_order`.
MasterTaskType | `shard-started`, `create-index`, `delete-index`, `refresh-mapping`, `put-mapping`, `CleanupSnapshotRestoreState`, `Update snapshot state`
MasterTaskMetadata | Metadata for the task (if any).

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---
layout: default
title: Configuration reference
parent: Trace analytics
nav_order: 25
---
# Data Prepper configuration reference
This page lists all supported Data Prepper sources, buffers, preppers, and sinks, along with their associated options. For example configuration files, see [Data Prepper]({{site.url}}{{site.baseurl}}/monitoring-plugins/trace/data-prepper/).
## Data Prepper server options
Option | Required | Description
:--- | :--- | :---
ssl | No | Boolean, indicating whether TLS should be used for server APIs. Defaults to true.
keyStoreFilePath | No | String, path to a .jks or .p12 keystore file. Required if ssl is true.
keyStorePassword | No | String, password for keystore. Optional, defaults to empty string.
privateKeyPassword | No | String, password for private key within keystore. Optional, defaults to empty string.
serverPort | No | Integer, port number to use for server APIs. Defaults to 4900
## General pipeline options
Option | Required | Description
:--- | :--- | :---
workers | No | Integer, default 1. Essentially the number of application threads. As a starting point for your use case, try setting this value to the number of CPU cores on the machine.
delay | No | Integer (milliseconds), default 3,000. How long workers wait between buffer read attempts.
## Sources
Sources define where your data comes from.
### otel_trace_source
Source for the OpenTelemetry Collector.
Option | Required | Description
:--- | :--- | :---
ssl | No | Boolean, whether to connect to the OpenTelemetry Collector over SSL.
sslKeyCertChainFile | No | String, path to the security certificate (e.g. `"config/demo-data-prepper.crt"`.
sslKeyFile | No | String, path to the security certificate key (e.g. `"config/demo-data-prepper.key"`).
### file
Source for flat file input.
Option | Required | Description
:--- | :--- | :---
path | Yes | String, path to the input file (e.g. `logs/my-log.log`).
### pipeline
Source for reading from another pipeline.
Option | Required | Description
:--- | :--- | :---
name | Yes | String, name of the pipeline to read from.
### stdin
Source for console input. Can be useful for testing. No options.
## Buffers
Buffers store data as it passes through the pipeline. If you implement a custom buffer, it can be memory-based (better performance) or disk-based (larger).
### bounded_blocking
The default buffer. Memory-based.
Option | Required | Description
:--- | :--- | :---
buffer_size | No | Integer, default 512. The maximum number of records the buffer accepts.
batch_size | No | Integer, default 8. The maximum number of records the buffer drains after each read.
## Preppers
Preppers perform some action on your data: filter, transform, enrich, etc.
### otel_trace_raw_prepper
Converts OpenTelemetry data to OpenSearch-compatible JSON documents.
Option | Required | Description
:--- | :--- | :---
root_span_flush_delay | No | Integer, representing the time interval in seconds to flush all the root spans in the prepper together with their descendants. Defaults to 30.
trace_flush_interval | No | Integer, representing the time interval in seconds to flush all the descendant spans without any root span. Defaults to 180.
### service_map_stateful
Uses OpenTelemetry data to create a distributed service map for visualization in OpenSearch Dashboards.
Option | Required | Description
:--- | :--- | :---
window_duration | No | Integer, representing the fixed time window in seconds to evaluate service-map relationships. Defaults to 180.
### peer_forwarder
Forwards ExportTraceServiceRequests via gRPC to other Data Prepper instances. Required for operating Data Prepper in a clustered deployment.
Option | Required | Description
:--- | :--- | :---
time_out | No | Integer, forwarded request timeout in seconds. Defaults to 3 seconds.
span_agg_count | No | Integer, batch size for number of spans per request. Defaults to 48.
discovery_mode | No | String, peer discovery mode to be used. Allowable values are `static` and `dns`. Defaults to `static`.
static_endpoints | No | List, containing string endpoints of all Data Prepper instances.
domain_name | No | String, single domain name to query DNS against. Typically used by creating multiple DNS A Records for the same domain.
ssl | No | Boolean, indicating whether TLS should be used. Default is true.
sslKeyCertChainFile | No | String, path to the security certificate
### string_converter
Converts strings to uppercase or lowercase. Mostly useful as an example if you want to develop your own prepper.
Option | Required | Description
:--- | :--- | :---
upper_case | No | Boolean, whether to convert to uppercase (`true`) or lowercase (`false`).
## Sinks
Sinks define where Data Prepper writes your data to.
### opensearch
Sink for an OpenSearch cluster.
Option | Required | Description
:--- | :--- | :---
hosts | Yes | List of OpenSearch hosts to write to (e.g. `["https://localhost:9200", "https://remote-cluster:9200"]`).
cert | No | String, path to the security certificate (e.g. `"config/root-ca.pem"`) if the cluster uses the OpenSearch security plugin.
username | No | String, username for HTTP basic authentication.
password | No | String, password for HTTP basic authentication.
aws_sigv4 | No | Boolean, whether to use IAM signing to connect to an Amazon ES cluster. For your access key, secret key, and optional session token, Data Prepper uses the default credential chain (environment variables, Java system properties, `~/.aws/credential`, etc.).
aws_region | No | String, AWS region for the cluster (e.g. `"us-east-1"`) if you are connecting to Amazon ES.
trace_analytics_raw | No | Boolean, default false. Whether to export as trace data to the `otel-v1-apm-span-*` index pattern (alias `otel-v1-apm-span`) for use with the Trace Analytics OpenSearch Dashboards plugin.
trace_analytics_service_map | No | Boolean, default false. Whether to export as trace data to the `otel-v1-apm-service-map` index for use with the service map component of the Trace Analytics OpenSearch Dashboards plugin.
index | No | String, name of the index to export to. Only required if you don't use the `trace_analytics_raw` or `trace_analytics_service_map` presets.
template_file | No | String, the path to a JSON [index template]({{site.url}}{{site.baseurl}}/opensearch/index-templates/) file (e.g. `/your/local/template-file.json` if you do not use the `trace_analytics_raw` or `trace_analytics_service_map`. See [otel-v1-apm-span-index-template.json](https://github.com/opensearch-project/data-prepper/blob/main/data-prepper-plugins/opensearch/src/main/resources/otel-v1-apm-span-index-template.json) for an example.
document_id_field | No | String, the field from the source data to use for the OpenSearch document ID (e.g. `"my-field"`) if you don't use the `trace_analytics_raw` or `trace_analytics_service_map` presets.
dlq_file | No | String, the path to your preferred dead letter queue file (e.g. `/your/local/dlq-file`). Data Prepper writes to this file when it fails to index a document on the OpenSearch cluster.
bulk_size | No | Integer (long), default 5. The maximum size (in MiB) of bulk requests to the OpenSearch cluster. Values below 0 indicate an unlimited size. If a single document exceeds the maximum bulk request size, Data Prepper sends it individually.
### file
Sink for flat file output.
Option | Required | Description
:--- | :--- | :---
path | Yes | String, path for the output file (e.g. `logs/my-transformed-log.log`).
### pipeline
Sink for writing to another pipeline.
Option | Required | Description
:--- | :--- | :---
name | Yes | String, name of the pipeline to write to.
### stdout
Sink for console output. Can be useful for testing. No options.

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---
layout: default
title: Data Prepper
parent: Trace analytics
nav_order: 20
---
# Data Prepper
Data Prepper is an independent component, not an OpenSearch plugin, that converts data for use with OpenSearch. It's not bundled with the all-in-one OpenSearch installation packages.
## Install Data Prepper
To use the Docker image, pull it like any other image:
```bash
docker pull opensearchproject/data-prepper:latest
```
Otherwise, [download](https://opensearch.org/downloads.html) the appropriate archive for your operating system and unzip it.
## Configure pipelines
To use Data Prepper, you define pipelines in a configuration YAML file. Each pipeline is a combination of a source, a buffer, zero or more preppers, and one or more sinks:
```yml
sample-pipeline:
workers: 4 # the number of workers
delay: 100 # in milliseconds, how long workers wait between read attempts
source:
otel_trace_source:
ssl: true
sslKeyCertChainFile: "config/demo-data-prepper.crt"
sslKeyFile: "config/demo-data-prepper.key"
buffer:
bounded_blocking:
buffer_size: 1024 # max number of records the buffer accepts
batch_size: 256 # max number of records the buffer drains after each read
prepper:
- otel_trace_raw_prepper:
sink:
- opensearch:
hosts: ["https:localhost:9200"]
cert: "config/root-ca.pem"
username: "ta-user"
password: "ta-password"
trace_analytics_raw: true
```
- Sources define where your data comes from. In this case, the source is the OpenTelemetry Collector (`otel_trace_source`) with some optional SSL settings.
- Buffers store data as it passes through the pipeline.
By default, Data Prepper uses its one and only buffer, the `bounded_blocking` buffer, so you can omit this section unless you developed a custom buffer or need to tune the buffer settings.
- Preppers perform some action on your data: filter, transform, enrich, etc.
You can have multiple preppers, which run sequentially from top to bottom, not in parallel. The `otel_trace_raw_prepper` prepper converts OpenTelemetry data into OpenSearch-compatible JSON documents.
- Sinks define where your data goes. In this case, the sink is an OpenSearch cluster.
Pipelines can act as the source for other pipelines. In the following example, a pipeline takes data from the OpenTelemetry Collector and uses two other pipelines as sinks:
```yml
entry-pipeline:
delay: "100"
source:
otel_trace_source:
ssl: true
sslKeyCertChainFile: "config/demo-data-prepper.crt"
sslKeyFile: "config/demo-data-prepper.key"
sink:
- pipeline:
name: "raw-pipeline"
- pipeline:
name: "service-map-pipeline"
raw-pipeline:
source:
pipeline:
name: "entry-pipeline"
prepper:
- otel_trace_raw_prepper:
sink:
- opensearch:
hosts: ["https://localhost:9200" ]
cert: "config/root-ca.pem"
username: "ta-user"
password: "ta-password"
trace_analytics_raw: true
service-map-pipeline:
delay: "100"
source:
pipeline:
name: "entry-pipeline"
prepper:
- service_map_stateful:
sink:
- opensearch:
hosts: ["https://localhost:9200"]
cert: "config/root-ca.pem"
username: "ta-user"
password: "ta-password"
trace_analytics_service_map: true
```
To learn more, see the [Data Prepper configuration reference]({{site.url}}{{site.baseurl}}/monitoring-plugins/trace/data-prepper-reference/).
## Configure the Data Prepper server
Data Prepper itself provides administrative HTTP endpoints such as `/list` to list pipelines and `/metrics/prometheus` to provide Prometheus-compatible metrics data. The port which serves these endpoints, as well as TLS configuration, is specified by a separate YAML file. Example:
```yml
ssl: true
keyStoreFilePath: "/usr/share/data-prepper/keystore.jks"
keyStorePassword: "password"
privateKeyPassword: "other_password"
serverPort: 1234
```
## Start Data Prepper
**Docker**
```bash
docker run --name data-prepper --expose 21890 -v /full/path/to/pipelines.yaml:/usr/share/data-prepper/pipelines.yaml -v /full/path/to/data-prepper-config.yaml:/usr/share/data-prepper/data-prepper-config.yaml opensearchproject/opensearch-data-prepper:latest
```
**macOS and Linux**
```bash
./data-prepper-tar-install.sh config/pipelines.yaml config/data-prepper-config.yaml
```
For production workloads, you likely want to run Data Prepper on a dedicated machine, which makes connectivity a concern. Data Prepper uses port 21890 and must be able to connect to both the OpenTelemetry Collector and the OpenSearch cluster. In the [sample applications](https://github.com/opensearch-project/Data-Prepper/tree/main/examples), you can see that all components use the same Docker network and expose the appropriate ports.

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---
layout: default
title: Get Started
parent: Trace analytics
nav_order: 1
---
# Get started with Trace Analytics
OpenSearch Trace Analytics consists of two components---Data Prepper and the Trace Analytics OpenSearch Dashboards plugin---that fit into the OpenTelemetry and OpenSearch ecosystems. The Data Prepper repository has several [sample applications](https://github.com/opensearch-project/data-prepper/tree/main/examples) to help you get started.
## Basic flow of data
![Data flow diagram from a distributed application to OpenSearch]({{site.url}}{{site.baseurl}}/images/ta.svg)
1. Trace Analytics relies on you adding instrumentation to your application and generating trace data. The [OpenTelemetry documentation](https://opentelemetry.io/docs/) contains example applications for many programming languages that can help you get started, including Java, Python, Go, and JavaScript.
(In the [Jaeger HotROD](#jaeger-hotrod) example below, an extra component, the Jaeger agent, runs alongside the application and sends the data to the OpenTelemetry Collector, but the concept is similar.)
1. The [OpenTelemetry Collector](https://opentelemetry.io/docs/collector/getting-started/) receives data from the application and formats it into OpenTelemetry data.
1. [Data Prepper]({{site.url}}{{site.baseurl}}/monitoring-plugins/trace/data-prepper/) processes the OpenTelemetry data, transforms it for use in OpenSearch, and indexes it on an OpenSearch cluster.
1. The [Trace Analytics OpenSearch Dashboards plugin]({{site.url}}{{site.baseurl}}/monitoring-plugins/trace/ta-dashboards/) displays the data in near real-time as a series of charts and tables, with an emphasis on service architecture, latency, error rate, and throughput.
## Jaeger HotROD
One Trace Analytics sample application is the Jaeger HotROD demo, which mimics the flow of data through a distributed application.
Download or clone the [Data Prepper repository](https://github.com/opensearch-project/data-prepper). Then navigate to `examples/jaeger-hotrod/` and open `docker-compose.yml` in a text editor. This file contains a container for each element from [Basic flow of data](#basic-flow-of-data):
- A distributed application (`jaeger-hot-rod`) with the Jaeger agent (`jaeger-agent`)
- The [OpenTelemetry Collector](https://opentelemetry.io/docs/collector/getting-started/) (`otel-collector`)
- Data Prepper (`data-prepper`)
- A single-node OpenSearch cluster (`opensearch`)
- OpenSearch Dashboards (`opensearch-dashboards`).
Close the file and run `docker-compose up --build`. After the containers start, navigate to `http://localhost:8080` in a web browser.
![HotROD web interface]({{site.url}}{{site.baseurl}}/images/hot-rod.png)
Click one of the buttons in the web interface to send a request to the application. Each request starts a series of operations across the services that make up the application. From the console logs, you can see that these operations share the same `trace-id`, which lets you track all of the operations in the request as a single *trace*:
```
jaeger-hot-rod | http://0.0.0.0:8081/customer?customer=392
jaeger-hot-rod | 2020-11-19T16:29:53.425Z INFO frontend/server.go:92 HTTP request received {"service": "frontend", "trace_id": "12091bd60f45ea2c", "span_id": "12091bd60f45ea2c", "method": "GET", "url": "/dispatch?customer=392&nonse=0.6509021735471818"}
jaeger-hot-rod | 2020-11-19T16:29:53.426Z INFO customer/client.go:54 Getting customer{"service": "frontend", "component": "customer_client", "trace_id": "12091bd60f45ea2c", "span_id": "12091bd60f45ea2c", "customer_id": "392"}
jaeger-hot-rod | 2020-11-19T16:29:53.430Z INFO customer/server.go:67 HTTP request received {"service": "customer", "trace_id": "12091bd60f45ea2c", "span_id": "252ff7d0e1ac533b", "method": "GET", "url": "/customer?customer=392"}
jaeger-hot-rod | 2020-11-19T16:29:53.430Z INFO customer/database.go:73 Loading customer{"service": "customer", "component": "mysql", "trace_id": "12091bd60f45ea2c", "span_id": "252ff7d0e1ac533b", "customer_id": "392"}
```
These operations also have a `span_id`. *Spans* are units of work from a single service. Each trace contains some number of spans. Shortly after the application starts processing the request, you can see the OpenTelemetry Collector starts exporting the spans:
```
otel-collector | 2020-11-19T16:29:53.781Z INFO loggingexporter/logging_exporter.go:296 TraceExporter {"#spans": 1}
otel-collector | 2020-11-19T16:29:53.787Z INFO loggingexporter/logging_exporter.go:296 TraceExporter {"#spans": 3}
```
Then Data Prepper processes the data from the OpenTelemetry Collector and indexes it:
```
data-prepper | 1031918 [service-map-pipeline-process-worker-2-thread-1] INFO com.amazon.dataprepper.pipeline.ProcessWorker – service-map-pipeline Worker: Processing 3 records from buffer
data-prepper | 1031923 [entry-pipeline-process-worker-1-thread-1] INFO com.amazon.dataprepper.pipeline.ProcessWorker – entry-pipeline Worker: Processing 1 records from buffer
```
Finally, you can see the OpenSearch node responding to the indexing request.
```
node-0.example.com | [2020-11-19T16:29:55,064][INFO ][o.e.c.m.MetadataMappingService] [9fb4fb37a516] [otel-v1-apm-span-000001/NGYbmVD9RmmqnxjfTzBQsQ] update_mapping [_doc]
node-0.example.com | [2020-11-19T16:29:55,267][INFO ][o.e.c.m.MetadataMappingService] [9fb4fb37a516] [otel-v1-apm-span-000001/NGYbmVD9RmmqnxjfTzBQsQ] update_mapping [_doc]
```
In a new terminal window, run the following command to see one of the raw documents in the OpenSearch cluster:
```bash
curl -X GET -u 'admin:admin' -k 'https://localhost:9200/otel-v1-apm-span-000001/_search?pretty&size=1'
```
Navigate to `http://localhost:5601` in a web browser and choose **Trace Analytics**. You can see the results of your single click in the Jaeger HotROD web interface: the number of traces per API and HTTP method, latency trends, a color-coded map of the service architecture, and a list of trace IDs that you can use to drill down on individual operations.
If you don't see your trace, adjust the timeframe in OpenSearch Dashboards. For more information on using the plugin, see [OpenSearch Dashboards plugin]({{site.url}}{{site.baseurl}}/monitoring-plugins/trace/ta-dashboards/).

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---
layout: default
title: Trace analytics
nav_order: 48
has_children: true
has_toc: false
redirect_from:
- /monitoring-plugins/trace/
---
# Trace Analytics
Trace Analytics provides a way to ingest and visualize [OpenTelemetry](https://opentelemetry.io/) data in OpenSearch. This data can help you find and fix performance problems in distributed applications.
A single operation, such as a user clicking a button, can trigger an extended series of events. The front end might call a back end service, which calls another service, which queries a database, processes the data, and sends it to the original service, which sends a confirmation to the front end.
Trace Analytics can help you visualize this flow of events and identify performance problems.
![Detailed trace view]({{site.url}}{{site.baseurl}}/images/ta-trace.png)

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---
layout: default
title: OpenSearch Dashboards plugin
parent: Trace analytics
nav_order: 50
---
# Trace Analytics OpenSearch Dashboards plugin
The Trace Analytics plugin for OpenSearch Dashboards provides at-a-glance visibility into your application performance, along with the ability to drill down on individual traces. For installation instructions, see [Standalone OpenSearch Dashboards plugin install]({{site.url}}{{site.baseurl}}/dashboards/install/plugins/).
The **Dashboard** view groups traces together by HTTP method and path so that you can see the average latency, error rate, and trends associated with a particular operation. For a more focused view, try filtering by trace group name.
![Dashboard view]({{site.url}}{{site.baseurl}}/images/ta-dashboard.png)
To drill down on the traces that make up a trace group, choose the number of traces in righthand column. Then choose an individual trace for a detailed summary.
![Detailed trace view]({{site.url}}{{site.baseurl}}/images/ta-trace.png)
The **Services** view lists all services in the application, plus an interactive map that shows how the various services connect to each other. In contrast to the dashboard, which helps identify problems by operation, the service map helps identify problems by service. Try sorting by error rate or latency to get a sense of potential problem areas of your application.
![Service view]({{site.url}}{{site.baseurl}}/images/ta-services.png)

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---
layout: default
title: Aggregations
nav_order: 14
has_children: true
---
# Aggregations
OpenSearch isn’t just for search. Aggregations let you tap into OpenSearch's powerful analytics engine to analyze your data and extract statistics from it.
The use cases of aggregations vary from analyzing data in real time to take some action to using OpenSearch Dashboards to create a visualization dashboard.
OpenSearch can perform aggregations on massive datasets in milliseconds. Compared to queries, aggregations consume more CPU cycles and memory.
## Aggregations on text fields
By default, OpenSearch doesn't support aggregations on a text field. Because text fields are tokenized, an aggregation on a text field has to reverse the tokenization process back to its original string and then formulate an aggregation based on that. This kind of an operation consumes significant memory and degrades cluster performance.
While you can enable aggregations on text fields by setting the `fielddata` parameter to `true` in the mapping, the aggregations are still based on the tokenized words and not on the raw text.
We recommend keeping a raw version of the text field as a `keyword` field that you can aggregate on.
In this case, you can perform aggregations on the `title.raw` field, instead of on the `title` field:
```json
PUT movies
{
"mappings": {
"properties": {
"title": {
"type": "text",
"fielddata": true,
"fields": {
"raw": {
"type": "keyword"
}
}
}
}
}
}
```
## General aggregation structure
The structure of an aggregation query is as follows:
```json
GET _search
{
"size": 0,
"aggs": {
"NAME": {
"AGG_TYPE": {}
}
}
}
```
If you’re only interested in the aggregation result and not in the results of the query, set `size` to 0.
In the `aggs` property (you can use `aggregations` if you want), you can define any number of aggregations. Each aggregation is defined by its name and one of the types of aggregations that OpenSearch supports.
The name of the aggregation helps you to distinguish between different aggregations in the response. The `AGG_TYPE` property is where you specify the type of aggregation.
## Sample aggregation
This section uses the OpenSearch Dashboards sample ecommerce data and web log data. To add the sample data, log in to OpenSearch Dashboards, choose **Home**, and then choose **Try our sample data**. For **Sample eCommerce orders** and **Sample web logs**, choose **Add data**.
### avg
To find the average value of the `taxful_total_price` field:
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"avg_taxful_total_price": {
"avg": {
"field": "taxful_total_price"
}
}
}
}
```
#### Sample response
```json
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 4675,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"avg_taxful_total_price" : {
"value" : 75.05542864304813
}
}
}
```
The aggregation block in the response shows the average value for the `taxful_total_price` field.
## Types of aggregations
There are three main types of aggregations:
- Metric aggregations - Calculate metrics such as `sum`, `min`, `max`, and `avg` on numeric fields.
- Bucket aggregations - Sort query results into groups based on some criteria.
- Pipeline aggregations - Pipe the output of one aggregation as an input to another.
## Nested aggregations
Aggregations within aggregations are called nested or subaggregations.
Metric aggregations produce simple results and can't contain nested aggregations.
Bucket aggregations produce buckets of documents that you can nest in other aggregations. You can perform complex analysis on your data by nesting metric and bucket aggregations within bucket aggregations.
### General nested aggregation syntax
```json
{
"aggs": {
"name": {
"type": {
"data"
},
"aggs": {
"nested": {
"type": {
"data"
}
}
}
}
}
}
```
The inner `aggs` keyword begins a new nested aggregation. The syntax of the parent aggregation and the nested aggregation is the same. Nested aggregations run in the context of the preceding parent aggregations.
You can also pair your aggregations with search queries to narrow down things you’re trying to analyze before aggregating. If you don't add a query, OpenSearch implicitly uses the `match_all` query.

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---
layout: default
title: Cluster formation
nav_order: 7
---
# Cluster formation
Before diving into OpenSearch and searching and aggregating data, you first need to create an OpenSearch cluster.
OpenSearch can operate as a single-node or multi-node cluster. The steps to configure both are, in general, quite similar. This page demonstrates how to create and configure a multi-node cluster, but with only a few minor adjustments, you can follow the same steps to create a single-node cluster.
To create and deploy an OpenSearch cluster according to your requirements, it’s important to understand how node discovery and cluster formation work and what settings govern them.
There are many ways to design a cluster. The following illustration shows a basic architecture:
![multi-node cluster architecture diagram]({{site.url}}{{site.baseurl}}/images/cluster.png)
This is a four-node cluster that has one dedicated master node, one dedicated coordinating node, and two data nodes that are master-eligible and also used for ingesting data.
The following table provides brief descriptions of the node types:
Node type | Description | Best practices for production
:--- | :--- | :-- |
`Master` | Manages the overall operation of a cluster and keeps track of the cluster state. This includes creating and deleting indices, keeping track of the nodes that join and leave the cluster, checking the health of each node in the cluster (by running ping requests), and allocating shards to nodes. | Three dedicated master nodes in three different zones is the right approach for almost all production use cases. This configuration ensures your cluster never loses quorum. Two nodes will be idle for most of the time except when one node goes down or needs some maintenance.
`Master-eligible` | Elects one node among them as the master node through a voting process. | For production clusters, make sure you have dedicated master nodes. The way to achieve a dedicated node type is to mark all other node types as false. In this case, you have to mark all the other nodes as not master-eligible.
`Data` | Stores and searches data. Performs all data-related operations (indexing, searching, aggregating) on local shards. These are the worker nodes of your cluster and need more disk space than any other node type. | As you add data nodes, keep them balanced between zones. For example, if you have three zones, add data nodes in multiples of three, one for each zone. We recommend using storage and RAM-heavy nodes.
`Ingest` | Preprocesses data before storing it in the cluster. Runs an ingest pipeline that transforms your data before adding it to an index. | If you plan to ingest a lot of data and run complex ingest pipelines, we recommend you use dedicated ingest nodes. You can also optionally offload your indexing from the data nodes so that your data nodes are used exclusively for searching and aggregating.
`Coordinating` | Delegates client requests to the shards on the data nodes, collects and aggregates the results into one final result, and sends this result back to the client. | A couple of dedicated coordinating-only nodes is appropriate to prevent bottlenecks for search-heavy workloads. We recommend using CPUs with as many cores as you can.
By default, each node is a master-eligible, data, ingest, and coordinating node. Deciding on the number of nodes, assigning node types, and choosing the hardware for each node type depends on your use case. You must take into account factors like the amount of time you want to hold on to your data, the average size of your documents, your typical workload (indexing, searches, aggregations), your expected price-performance ratio, your risk tolerance, and so on.
After you assess all these requirements, we recommend you use a benchmark testing tool like Rally to provision a small sample cluster and run tests with varying workloads and configurations. Compare and analyze the system and query metrics for these tests to design an optimum architecture. To get started with Rally, see the [Rally documentation](https://esrally.readthedocs.io/en/stable/).
This page demonstrates how to work with the different node types. It assumes that you have a four-node cluster similar to the preceding illustration.
## Prerequisites
Before you get started, you must install and configure OpenSearch on all of your nodes. For information about the available options, see [Install and configure OpenSearch]({{site.url}}{{site.baseurl}}/opensearch/install/).
After you're done, use SSH to connect to each node, then open the `config/opensearch.yml` file. You can set all configurations for your cluster in this file.
## Step 1: Name a cluster
Specify a unique name for the cluster. If you don't specify a cluster name, it's set to `opensearch` by default. Setting a descriptive cluster name is important, especially if you want to run multiple clusters inside a single network.
To specify the cluster name, change the following line:
```yml
#cluster.name: my-application
```
to
```yml
cluster.name: opensearch-cluster
```
Make the same change on all the nodes to make sure that they'll join to form a cluster.
## Step 2: Set node attributes for each node in a cluster
After you name the cluster, set node attributes for each node in your cluster.
#### Master node
Give your master node a name. If you don't specify a name, OpenSearch assigns a machine-generated name that makes the node difficult to monitor and troubleshoot.
```yml
node.name: opensearch-master
```
You can also explicitly specify that this node is a master node. This is already true by default, but adding it makes it easier to identify the master node:
```yml
node.master: true
```
Then make the node a dedicated master that won’t perform double-duty as a data node:
```yml
node.data: false
```
Specify that this node will not be used for ingesting data:
```yml
node.ingest: false
```
#### Data nodes
Change the name of two nodes to `opensearch-d1` and `opensearch-d2`, respectively:
```yml
node.name: opensearch-d1
```
```yml
node.name: opensearch-d2
```
You can make them master-eligible data nodes that will also be used for ingesting data:
```yml
node.master: true
node.data: true
node.ingest: true
```
You can also specify any other attributes that you'd like to set for the data nodes.
#### Coordinating node
Change the name of the coordinating node to `opensearch-c1`:
```yml
node.name: opensearch-c1
```
Every node is a coordinating node by default, so to make this node a dedicated coordinating node, set `node.master`, `node.data`, and `node.ingest` to `false`:
```yml
node.master: false
node.data: false
node.ingest: false
```
## Step 3: Bind a cluster to specific IP addresses
`network_host` defines the IP address used to bind the node. By default, OpenSearch listens on a local host, which limits the cluster to a single node. You can also use `_local_` and `_site_` to bind to any loopback or site-local address, whether IPv4 or IPv6:
```yml
network.host: [_local_, _site_]
```
To form a multi-node cluster, specify the IP address of the node:
```yml
network.host: <IP address of the node>
```
Make sure to configure these settings on all of your nodes.
## Step 4: Configure discovery hosts for a cluster
Now that you've configured the network hosts, you need to configure the discovery hosts.
Zen Discovery is the built-in, default mechanism that uses [unicast](https://en.wikipedia.org/wiki/Unicast) to find other nodes in the cluster.
You can generally just add all your master-eligible nodes to the `discovery.seed_hosts` array. When a node starts up, it finds the other master-eligible nodes, determines which one is the master, and asks to join the cluster.
For example, for `opensearch-master` the line looks something like this:
```yml
discovery.seed_hosts: ["<private IP of opensearch-d1>", "<private IP of opensearch-d2>", "<private IP of opensearch-c1>"]
```
## Step 5: Start the cluster
After you set the configurations, start OpenSearch on all nodes:
```bash
sudo systemctl start opensearch.service
```
Then go to the logs file to see the formation of the cluster:
```bash
less /var/log/opensearch/opensearch-cluster.log
```
Perform the following `_cat` query on any node to see all the nodes formed as a cluster:
```bash
curl -XGET https://<private-ip>:9200/_cat/nodes?v -u 'admin:admin' --insecure
```
```
ip heap.percent ram.percent cpu load_1m load_5m load_15m node.role master name
x.x.x.x 13 61 0 0.02 0.04 0.05 mi * opensearch-master
x.x.x.x 16 60 0 0.06 0.05 0.05 md - opensearch-d1
x.x.x.x 34 38 0 0.12 0.07 0.06 md - opensearch-d2
x.x.x.x 23 38 0 0.12 0.07 0.06 md - opensearch-c1
```
To better understand and monitor your cluster, use the [cat API]({{site.url}}{{site.baseurl}}/opensearch/catapis/).
## (Advanced) Step 6: Configure shard allocation awareness or forced awareness
If your nodes are spread across several geographical zones, you can configure shard allocation awareness to allocate all replica shards to a zone that’s different from their primary shard.
With shard allocation awareness, if the nodes in one of your zones fail, you can be assured that your replica shards are spread across your other zones. It adds a layer of fault tolerance to ensure your data survives a zone failure beyond just individual node failures.
To configure shard allocation awareness, add zone attributes to `opensearch-d1` and `opensearch-d2`, respectively:
```yml
node.attr.zone: zoneA
```
```yml
node.attr.zone: zoneB
```
Update the cluster settings:
```json
PUT _cluster/settings
{
"persistent": {
"cluster.routing.allocation.awareness.attributes": "zone"
}
}
```
You can either use `persistent` or `transient` settings. We recommend the `persistent` setting because it persists through a cluster reboot. Transient settings don't persist through a cluster reboot.
Shard allocation awareness attempts to separate primary and replica shards across multiple zones. However, if only one zone is available (such as after a zone failure), OpenSearch allocates replica shards to the only remaining zone.
Another option is to require that primary and replica shards are never allocated to the same zone. This is called forced awareness.
To configure forced awareness, specify all the possible values for your zone attributes:
```json
PUT _cluster/settings
{
"persistent": {
"cluster.routing.allocation.awareness.attributes": "zone",
"cluster.routing.allocation.awareness.force.zone.values":["zoneA", "zoneB"]
}
}
```
Now, if a data node fails, forced awareness doesn't allocate the replicas to a node in the same zone. Instead, the cluster enters a yellow state and only allocates the replicas when nodes in another zone come online.
In our two-zone architecture, we can use allocation awareness if `opensearch-d1` and `opensearch-d2` are less than 50% utilized, so that each of them have the storage capacity to allocate replicas in the same zone.
If that is not the case, and `opensearch-d1` and `opensearch-d2` do not have the capacity to contain all primary and replica shards, we can use forced awareness. This approach helps to make sure that, in the event of a failure, OpenSearch doesn't overload your last remaining zone and lock up your cluster due to lack of storage.
Choosing allocation awareness or forced awareness depends on how much space you might need in each zone to balance your primary and replica shards.
## (Advanced) Step 7: Set up a hot-warm architecture
You can design a hot-warm architecture where you first index your data to hot nodes---fast and expensive---and after a certain period of time move them to warm nodes---slow and cheap.
If you analyze time series data that you rarely update and want the older data to go onto cheaper storage, this architecture can be a good fit.
This architecture helps save money on storage costs. Rather than increasing the number of hot nodes and using fast, expensive storage, you can add warm nodes for data that you don't access as frequently.
To configure a hot-warm storage architecture, add `temp` attributes to `opensearch-d1` and `opensearch-d2`, respectively:
```yml
node.attr.temp: hot
```
```yml
node.attr.temp: warm
```
You can set the attribute name and value to whatever you want as long as it’s consistent for all your hot and warm nodes.
To add an index `newindex` to the hot node:
```json
PUT newindex
{
"settings": {
"index.routing.allocation.require.temp": "hot"
}
}
```
Take a look at the following shard allocation for `newindex`:
```json
GET _cat/shards/newindex?v
index shard prirep state docs store ip node
new_index 2 p STARTED 0 230b 10.0.0.225 opensearch-d1
new_index 2 r UNASSIGNED
new_index 3 p STARTED 0 230b 10.0.0.225 opensearch-d1
new_index 3 r UNASSIGNED
new_index 4 p STARTED 0 230b 10.0.0.225 opensearch-d1
new_index 4 r UNASSIGNED
new_index 1 p STARTED 0 230b 10.0.0.225 opensearch-d1
new_index 1 r UNASSIGNED
new_index 0 p STARTED 0 230b 10.0.0.225 opensearch-d1
new_index 0 r UNASSIGNED
```
In this example, all primary shards are allocated to `opensearch-d1`, which is our hot node. All replica shards are unassigned, because we're forcing this index to allocate only to hot nodes.
To add an index `oldindex` to the warm node:
```json
PUT oldindex
{
"settings": {
"index.routing.allocation.require.temp": "warm"
}
}
```
The shard allocation for `oldindex`:
```json
GET _cat/shards/oldindex?v
index shard prirep state docs store ip node
old_index 2 p STARTED 0 230b 10.0.0.74 opensearch-d2
old_index 2 r UNASSIGNED
old_index 3 p STARTED 0 230b 10.0.0.74 opensearch-d2
old_index 3 r UNASSIGNED
old_index 4 p STARTED 0 230b 10.0.0.74 opensearch-d2
old_index 4 r UNASSIGNED
old_index 1 p STARTED 0 230b 10.0.0.74 opensearch-d2
old_index 1 r UNASSIGNED
old_index 0 p STARTED 0 230b 10.0.0.74 opensearch-d2
old_index 0 r UNASSIGNED
```
In this case, all primary shards are allocated to `opensearch-d2`. Again, all replica shards are unassigned because we only have one warm node.
A popular approach is to configure your [index templates]({{site.url}}{{site.baseurl}}/opensearch/index-templates/) to set the `index.routing.allocation.require.temp` value to `hot`. This way, OpenSearch stores your most recent data on your hot nodes.
You can then use the [Index State Management (ISM)]({{site.url}}{{site.baseurl}}/im-plugin/) plugin to periodically check the age of an index and specify actions to take on it. For example, when the index reaches a specific age, change the `index.routing.allocation.require.temp` setting to `warm` to automatically move your data from hot nodes to warm nodes.
## Next steps
If you are using the security plugin, the previous request to `_cat/nodes?v` might have failed with an initialization error. To initialize the plugin, run `opensearch/plugins/opensearch-security/tools/securityadmin.sh`. A sample command that uses the demo certificates might look like this:
```bash
sudo ./securityadmin.sh -cd {{site.url}}{{site.baseurl}}/securityconfig/ -icl -nhnv -cacert /etc/opensearch/root-ca.pem -cert /etc/opensearch/kirk.pem -key /etc/opensearch/kirk-key.pem -h <private-ip>
```
For full guidance around configuration options, see [Security configuration]({{site.url}}{{site.baseurl}}/security-plugin/configuration/index/).

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---
layout: default
title: Common REST Parameters
nav_order: 93
---
# Common REST parameters
OpenSearch supports the following parameters for all REST operations:
Option | Description | Example
:--- | :--- | :---
Human-readable output | To convert output units to human-readable values (for example, `1h` for 1 hour and `1kb` for 1,024 bytes), add `?human=true` to the request URL. | `GET <index_name>/_search?human=true`
Pretty result | To get back JSON responses in a readable format, add `?pretty=true` to the request URL. | `GET <index_name>/_search?pretty=true`
Content type | To specify the type of content in the request body, use the `Content-Type` key name in the request header. Most operations support JSON, YAML, and CBOR formats. | `POST _scripts/<template_name> -H 'Content-Type: application/json`
Request body in query string | If the client library does not accept a request body for non-POST requests, use the `source` query string parameter to pass the request body. Also, specify the `source_content_type` parameter with a supported media type such as `application/json`. | `GET _search?source_content_type=application/json&source={"query":{"match_all":{}}}`
Stack traces | To include the error stack trace in the response when an exception is raised, add `error_trace=true` to the request URL. | `GET <index_name>/_search?error_trace=true`

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---
layout: default
title: Configuration
nav_order: 5
---
# OpenSearch configuration
Most OpenSearch configuration can take place in the cluster settings API. Certain operations require you to modify `opensearch.yml` and restart the cluster.
Whenever possible, use the cluster settings API instead; `opensearch.yml` is local to each node, whereas the API applies the setting to all nodes in the cluster. Certain settings, however, require `opensearch.yml`. In general, these settings relate to networking, cluster formation, and the local file system. To learn more, see [Cluster formation]({{site.url}}{{site.baseurl}}/opensearch/cluster/).
## Update cluster settings using the API
The first step in changing a setting is to view the current settings:
```
GET _cluster/settings?include_defaults=true
```
For a more concise summary of non-default settings:
```
GET _cluster/settings
```
Three categories of setting exist in the cluster settings API: persistent, transient, and default. Persistent settings, well, persist after a cluster restart. After a restart, OpenSearch clears transient settings.
If you specify the same setting in multiple places, OpenSearch uses the following precedence:
1. Transient settings
2. Persistent settings
3. Settings from `opensearch.yml`
4. Default settings
To change a setting, just specify the new one as either persistent or transient. This example shows the flat settings form:
```json
PUT _cluster/settings
{
"persistent" : {
"action.auto_create_index" : false
}
}
```
You can also use the expanded form, which lets you copy and paste from the GET response and change existing values:
```json
PUT _cluster/settings
{
"persistent": {
"action": {
"auto_create_index": false
}
}
}
```
---
## Configuration file
You can find `opensearch.yml` in `/usr/share/opensearch/config/opensearch.yml` (Docker) or `/etc/opensearch/opensearch.yml` (most Linux distributions) on each node.
The demo configuration includes a number of settings for the security plugin that you should modify before using OpenSearch for a production workload. To learn more, see [Security]({{site.url}}{{site.baseurl}}/security-plugin/).

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---
layout: default
title: Data streams
nav_order: 13
---
# Data streams
If you're ingesting continuously generated time-series data such as logs, events, and metrics into OpenSearch, you're likely in a scenario where:
- You’re ingesting documents that grow rapidly.
- You don’t need to update older documents.
- Your searches generally target the newer documents.
A typical workflow to manage time-series data is as follows:
- To split your data into an index for each day, use the rollover operation.
- To perform searches on a virtual index name that gets expanded to the underlying indices, create an [index alias]({{site.url}}{{site.baseurl}}/opensearch/index-alias/).
- To perform a write operation on an index alias, configure the latest index as the write index.
- To configure new indices, extract common mappings and settings into an [index template]({{site.url}}{{site.baseurl}}/opensearch/index-templates/).
Even after you perform all these operations, you’re still not enforcing the best practices when dealing with time-series data. For example, you can modify the indices directly. You’re able to ingest documents without a timestamp field, which might result in slower queries.
Data streams abstract the complexity and enforce the best practices for managing time-series data.
With data streams, you can store append-only time-series data across multiple indices with a single endpoint for ingesting and searching data. It replaces index aliases for time-series data.
## About data streams
A data stream consists of one or more hidden auto-generated backing indices. These backing indices are named using the following convention:
```
.ds-<data-stream-name>-<generation-id>
```
For example, `.ds-logs-redis-000003`, where generation-id is a six-digit, zero-padded integer that acts as a cumulative count of the data stream’s rollovers, starting at `000001`.
The most recently created backing index is the data stream’s write index. You can’t add documents directly to any of the backing indices. You can only add them via the data stream handle:
![data stream indexing diagram]({{site.url}}{{site.baseurl}}/images/data_stream_indexing.png)
The data stream routes search requests to all of its backing indices. It uses the timestamp field to intelligently route search requests to the right set of indices and shards:
![data stream indexing diagram]({{site.url}}{{site.baseurl}}/images/data_stream_searching.png)
The following operations are not supported on the write index because they might hinder the indexing operation:
- close
- clone
- delete
- shrink
- split
## Get started with data streams
### Step 1: Create an index template
To create a data stream, you first need to create an index template that configures a set of indices as a data stream. The `data_stream` object indicates that it’s a data stream and not a regular index template. The index pattern matches with the name of the data stream:
```json
PUT _index_template/logs-template
{
"index_patterns": [
"my-data-stream",
"logs-*"
],
"data_stream": {},
"priority": 100
}
```
In this case, each ingested document must have an `@timestamp` field.
You also have the ability to define your own custom timestamp field as a property in the `data_stream` object. You can also add index mappings and other settings here, just as you would for a regular index template.
```json
PUT _index_template/logs-template-nginx
{
"index_patterns": "logs-nginx",
"data_stream": {
"timestamp_field": {
"name": "request_time"
}
},
"priority": 200,
"template": {
"settings": {
"number_of_shards": 1,
"number_of_replicas": 0
}
}
}
```
In this case, `logs-nginx` index matches both the `logs-template` and `logs-template-nginx` templates. When you have a tie, OpenSearch selects the matching index template with the higher priority value.
### Step 2: Create a data stream
After you create an index template, you can create a data stream.
You can use the data stream API to explicitly create a data stream. The data stream API initializes the first backing index:
```json
PUT _data_stream/logs-redis
PUT _data_stream/logs-nginx
```
You can also directly start ingesting data without creating a data stream.
Because we have a matching index template with a data_stream object, OpenSearch automatically creates the data stream:
```json
POST logs-staging/_doc
{
"message": "login attempt failed",
"@timestamp": "2013-03-01T00:00:00"
}
```
To see information about a specific data stream:
```json
GET _data_stream/logs-nginx
```
#### Sample response
```json
{
"data_streams" : [
{
"name" : "logs-nginx",
"timestamp_field" : {
"name" : "request_time"
},
"indices" : [
{
"index_name" : ".ds-logs-nginx-000001",
"index_uuid" : "-VhmuhrQQ6ipYCmBhn6vLw"
}
],
"generation" : 1,
"status" : "GREEN",
"template" : "logs-template-nginx"
}
]
}
```
You can see the name of the timestamp field, the list of the backing indices, and the template that's used to create the data stream. You can also see the health of the data stream, which represents the lowest status of all its backing indices.
To see more insights about the data stream, use the `_stats` endpoint:
```json
GET _data_stream/logs-nginx/_stats
```
#### Sample response
```json
{
"_shards" : {
"total" : 1,
"successful" : 1,
"failed" : 0
},
"data_stream_count" : 1,
"backing_indices" : 1,
"total_store_size_bytes" : 208,
"data_streams" : [
{
"data_stream" : "logs-nginx",
"backing_indices" : 1,
"store_size_bytes" : 208,
"maximum_timestamp" : 0
}
]
}
```
### Step 3: Ingest data into the data stream
To ingest data into a data stream, you can use the regular indexing APIs. Make sure every document that you index has a timestamp field. If you try to ingest a document that doesn't have a timestamp field, you get an error.
```json
POST logs-redis/_doc
{
"message": "login attempt",
"@timestamp": "2013-03-01T00:00:00"
}
```
### Step 4: Searching a data stream
You can search a data stream just like you search a regular index or an index alias.
The search operation applies to all of the backing indices (all data present in the stream).
```json
GET logs-redis/_search
{
"query": {
"match": {
"message": "login"
}
}
}
```
#### Sample response
```json
{
"took" : 514,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 0.2876821,
"hits" : [
{
"_index" : ".ds-logs-redis-000001",
"_type" : "_doc",
"_id" : "-rhVmXoBL6BAVWH3mMpC",
"_score" : 0.2876821,
"_source" : {
"message" : "login attempt",
"@timestamp" : "2013-03-01T00:00:00"
}
}
]
}
}
```
### Step 5: Rollover a data stream
A rollover operation creates a new backing index that becomes the data stream’s new write index.
To perform manual rollover operation on the data stream:
```json
POST logs-redis/_rollover
```
#### Sample response
```json
{
"acknowledged" : true,
"shards_acknowledged" : true,
"old_index" : ".ds-logs-redis-000001",
"new_index" : ".ds-logs-redis-000002",
"rolled_over" : true,
"dry_run" : false,
"conditions" : { }
}
```
If you now perform a `GET` operation on the `logs-redis` data stream, you see that the generation ID is incremented from 1 to 2.
You can also set up an [Index State Management (ISM) policy]({{site.url}}{{site.baseurl}}/ism/policies/) to automate the rollover process for the data stream.
The ISM policy is applied to the backing indices at the time of their creation. When you associate a policy to a data stream, it only affects the future backing indices of that data stream.
You also don’t need to provide the `rollover_alias` setting, because the ISM policy infers this information from the backing index.
### Step 6: Manage data streams in OpenSearch Dashboards
To manage data streams from OpenSearch Dashboards, open **OpenSearch Dashboards**, choose **Index Management**, select **Indices** or **Policy managed indices**.
You see a toggle switch for data streams that you can use to show or hide indices belonging to a data stream.
When you enable this switch, you see a data stream multi-select dropdown menu that you can use for filtering data streams.
You also see a data stream column that shows you the name of the parent data stream the index is contained in.
![data stream toggle]({{site.url}}{{site.baseurl}}/images/data_streams_toggle.png)
You can select one or more data streams and apply an ISM policy on them. You can also apply a policy on any individual backing index.
You can performing visualizations on a data stream just like you would on a regular index or index alias.
### Step 7: Delete a data stream
The delete operation first deletes the backing indices of a data stream and then deletes the data stream itself.
To delete a data stream and all of its hidden backing indices:
```json
DELETE _data_stream/<name_of_data_stream>
```
You can use wildcards to delete more than one data stream.
We recommend deleting data from a data stream using an ISM policy.
You can also use [asynchronous search]({{site.url}}{{site.baseurl}}/async/index/) and [SQL]({{site.url}}{{site.baseurl}}/sql/index/) and [PPL]({{site.url}}{{site.baseurl}}/ppl/index/) to query your data stream directly. You can also use the security plugin to define granular permissions on the data stream name.

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---
layout: default
title: Index aliases
nav_order: 12
---
# Index aliases
An alias is a virtual index name that can point to one or more indices.
If your data is spread across multiple indices, rather than keeping track of which indices to query, you can create an alias and query it instead.
For example, if you’re storing logs into indices based on the month and you frequently query the logs for the previous two months, you can create a `last_2_months` alias and update the indices it points to each month.
Because you can change the indices an alias points to at any time, referring to indices using aliases in your applications allows you to reindex your data without any downtime.
---
#### Table of contents
1. TOC
{:toc}
---
## Create aliases
To create an alias, use a POST request:
```json
POST _aliases
```
Use the `actions` method to specify the list of actions that you want to perform. This command creates an alias named `alias1` and adds `index-1` to this alias:
```json
POST _aliases
{
"actions": [
{
"add": {
"index": "index-1",
"alias": "alias1"
}
}
]
}
```
You should see the following response:
```json
{
"acknowledged": true
}
```
If this request fails, make sure the index that you're adding to the alias already exists.
To check if `alias1` refers to `index-1`, run the following command:
```json
GET alias1
```
## Add or remove indices
You can perform multiple actions in the same `_aliases` operation.
For example, the following command removes `index-1` and adds `index-2` to `alias1`:
```json
POST _aliases
{
"actions": [
{
"remove": {
"index": "index-1",
"alias": "alias1"
}
},
{
"add": {
"index": "index-2",
"alias": "alias1"
}
}
]
}
```
The `add` and `remove` actions occur atomically, which means that at no point will `alias1` point to both `index-1` and `index-2`.
You can also add indices based on an index pattern:
```json
POST _aliases
{
"actions": [
{
"add": {
"index": "index*",
"alias": "alias1"
}
}
]
}
```
## Manage aliases
To list the mapping of aliases to indices, run the following command:
```json
GET _cat/aliases?v
```
#### Sample response
```json
alias index filter routing.index routing.search
alias1 index-1 * - -
```
To check which indices an alias points to, run the following command:
```json
GET _alias/alias1
```
#### Sample response
```json
{
"index-2": {
"aliases": {
"alias1": {}
}
}
}
```
Conversely, to find which alias points to a specific index, run the following command:
```json
GET /index-2/_alias/*
```
To check if an alias exists, run the following command:
```json
HEAD /alias1/_alias/
```
## Add aliases at index creation
You can add an index to an alias as you create the index:
```json
PUT index-1
{
"aliases": {
"alias1": {}
}
}
```
## Create filtered aliases
You can create a filtered alias to access a subset of documents or fields from the underlying indices.
This command adds only a specific timestamp field to `alias1`:
```json
POST _aliases
{
"actions": [
{
"add": {
"index": "index-1",
"alias": "alias1",
"filter": {
"term": {
"timestamp": "1574641891142"
}
}
}
}
]
}
```
## Index alias options
You can specify the options shown in the following table.
Option | Valid values | Description | Required
:--- | :--- | :---
`index` | String | The name of the index that the alias points to. | Yes
`alias` | String | The name of the alias. | No
`filter` | Object | Add a filter to the alias. | No
`routing` | String | Limit search to an associated shard value. You can specify `search_routing` and `index_routing` independently. | No
`is_write_index` | String | Specify the index that accepts any write operations to the alias. If this value is not specified, then no write operations are allowed. | No

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---
layout: default
title: Index data
nav_order: 10
---
# Index data
You index data using the OpenSearch REST API. Two APIs exist: the index API and the `_bulk` API.
For situations in which new data arrives incrementally (for example, customer orders from a small business), you might use the index API to add documents individually as they arrive. For situations in which the flow of data is less frequent (for example, weekly updates to a marketing website), you might prefer to generate a file and send it to the `_bulk` API. For large numbers of documents, lumping requests together and using the `_bulk` API offers superior performance. If your documents are enormous, however, you might need to index them individually.
## Introduction to indexing
Before you can search data, you must *index* it. Indexing is the method by which search engines organize data for fast retrieval. The resulting structure is called, fittingly, an index.
In OpenSearch, the basic unit of data is a JSON *document*. Within an index, OpenSearch identifies each document using a unique ID.
A request to the index API looks like this:
```json
PUT <index>/_doc/<id>
{ "A JSON": "document" }
```
A request to the `_bulk` API looks a little different, because you specify the index and ID in the bulk data:
```json
POST _bulk
{ "index": { "_index": "<index>", "_id": "<id>" } }
{ "A JSON": "document" }
```
Bulk data must conform to a specific format, which requires a newline character (`\n`) at the end of every line, including the last line. This is the basic format:
```
Action and metadata\n
Optional document\n
Action and metadata\n
Optional document\n
```
The document is optional, because `delete` actions don't require a document. The other actions (`index`, `create`, and `update`) all require a document. If you specifically want the action to fail if the document already exists, use the `create` action instead of the `index` action.
{: .note }
To index bulk data using the `curl` command, navigate to the folder where you have your file saved and run the following command:
```json
curl -H "Content-Type: application/x-ndjson" -POST https://localhost:9200/data/_bulk -u 'admin:admin' --insecure --data-binary "@data.json"
```
If any one of the actions in the `_bulk` API fail, OpenSearch continues to execute the other actions. Examine the `items` array in the response to figure out what went wrong. The entries in the `items` array are in the same order as the actions specified in the request.
OpenSearch automatically creates an index when you add a document to an index that doesn't already exist. It also automatically generates an ID if you don't specify an ID in the request. This simple example automatically creates the movies index, indexes the document, and assigns it a unique ID:
```json
POST movies/_doc
{ "title": "Spirited Away" }
```
Automatic ID generation has a clear downside: because the indexing request didn't specify a document ID, you can't easily update the document at a later time. Also, if you run this request 10 times, OpenSearch indexes this document as 10 different documents with unique IDs. To specify an ID of 1, use the following request (note the use of PUT instead of POST):
```json
PUT movies/_doc/1
{ "title": "Spirited Away" }
```
Because you must specify an ID, if you run this command 10 times, you still have just one document indexed with the `_version` field incremented to 10.
Indices default to one primary shard and one replica. If you want to specify non-default settings, create the index before adding documents:
```json
PUT more-movies
{ "settings": { "number_of_shards": 6, "number_of_replicas": 2 } }
```
## Naming restrictions for indices
OpenSearch indices have the following naming restrictions:
- All letters must be lowercase.
- Index names can't begin with underscores (`_`) or hyphens (`-`).
- Index names can't contain spaces, commas, or the following characters:
`:`, `"`, `*`, `+`, `/`, `\`, `|`, `?`, `#`, `>`, or `<`
## Read data
After you index a document, you can retrieve it by sending a GET request to the same endpoint that you used for indexing:
```json
GET movies/_doc/1
{
"_index" : "movies",
"_type" : "_doc",
"_id" : "1",
"_version" : 1,
"_seq_no" : 0,
"_primary_term" : 1,
"found" : true,
"_source" : {
"title" : "Spirited Away"
}
}
```
You can see the document in the `_source` object. If the document is not found, the `found` key is `false` and the `_source` object is not part of the response.
To retrieve multiple documents with a single command, use the `_mget` operation.
The format for retrieving multiple documents is similar to the `_bulk` operation, where you must specify the index and ID in the request body:
```json
GET _mget
{
"docs": [
{
"_index": "<index>",
"_id": "<id>"
},
{
"_index": "<index>",
"_id": "<id>"
}
]
}
```
To only return specific fields in a document:
```json
GET _mget
{
"docs": [
{
"_index": "<index>",
"_id": "<id>",
"_source": "field1"
},
{
"_index": "<index>",
"_id": "<id>",
"_source": "field2"
}
]
}
```
To check if a document exists:
```json
HEAD movies/_doc/<doc-id>
```
If the document exists, you get back a `200 OK` response, and if it doesn't, you get back a `404 - Not Found` error.
## Update data
To update existing fields or to add new fields, send a POST request to the `_update` operation with your changes in a `doc` object:
```json
POST movies/_update/1
{
"doc": {
"title": "Castle in the Sky",
"genre": ["Animation", "Fantasy"]
}
}
```
Note the updated `title` field and new `genre` field:
```json
GET movies/_doc/1
{
"_index" : "movies",
"_type" : "_doc",
"_id" : "1",
"_version" : 2,
"_seq_no" : 1,
"_primary_term" : 1,
"found" : true,
"_source" : {
"title" : "Castle in the Sky",
"genre" : [
"Animation",
"Fantasy"
]
}
}
```
The document also has an incremented `_version` field. Use this field to keep track of how many times a document is updated.
POST requests make partial updates to documents. To altogether replace a document, use a PUT request:
```json
PUT movies/_doc/1
{
"title": "Spirited Away"
}
```
The document with ID of 1 will contain only the `title` field, because the entire document will be replaced with the document indexed in this PUT request.
Use the `upsert` object to conditionally update documents based on whether they already exist. Here, if the document exists, its `title` field changes to `Castle in the Sky`. If it doesn't, OpenSearch indexes the document in the `upsert` object.
```json
POST movies/_update/2
{
"doc": {
"title": "Castle in the Sky"
},
"upsert": {
"title": "Only Yesterday",
"genre": ["Animation", "Fantasy"],
"date": 1993
}
}
```
#### Sample response
```json
{
"_index" : "movies",
"_type" : "_doc",
"_id" : "2",
"_version" : 2,
"result" : "updated",
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
},
"_seq_no" : 3,
"_primary_term" : 1
}
```
Each update operation for a document has a unique combination of the `_seq_no` and `_primary_term` values.
OpenSearch first writes your updates to the primary shard and then sends this change to all the replica shards. An uncommon issue can occur if multiple users of your OpenSearch-based application make updates to existing documents in the same index. In this situation, another user can read and update a document from a replica before it receives your update from the primary shard. Your update operation then ends up updating an older version of the document. In the best case, you and the other user make the same changes, and the document remains accurate. In the worst case, the document now contains out-of-date information.
To prevent this situation, use the `_seq_no` and `_primary_term` values in the request header:
```json
POST movies/_update/2?if_seq_no=3&if_primary_term=1
{
"doc": {
"title": "Castle in the Sky",
"genre": ["Animation", "Fantasy"]
}
}
```
If the document is updated after we retrieved it, the `_seq_no` and `_primary_term` values are different and our update operation fails with a `409 — Conflict` error.
When using the `_bulk` API, specify the `_seq_no` and `_primary_term` values within the action metadata.
## Delete data
To delete a document from an index, use a DELETE request:
```json
DELETE movies/_doc/1
```
The DELETE operation increments the `_version` field. If you add the document back to the same ID, the `_version` field increments again. This behavior occurs because OpenSearch deletes the document `_source`, but retains its metadata.

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---
layout: default
title: Index templates
nav_order: 15
---
# Index templates
Index templates let you initialize new indices with predefined mappings and settings. For example, if you continuously index log data, you can define an index template so that all of these indices have the same number of shards and replicas.
### Create a template
To create an index template, use a POST request:
```json
POST _index_template
```
This command creates a template named `daily_logs` and applies it to any new index whose name matches the regular expression `logs-2020-01-*` and also adds it to the `my_logs` alias:
```json
PUT _index_template/daily_logs
{
"index_patterns": [
"logs-2020-01-*"
],
"template": {
"aliases": {
"my_logs": {}
},
"settings": {
"number_of_shards": 2,
"number_of_replicas": 1
},
"mappings": {
"properties": {
"timestamp": {
"type": "date",
"format": "yyyy-MM-dd HH:mm:ss||yyyy-MM-dd||epoch_millis"
},
"value": {
"type": "double"
}
}
}
}
}
```
You should see the following response:
```json
{
"acknowledged": true
}
```
If you create an index named `logs-2020-01-01`, you can see that it has the mappings and settings from the template:
```json
PUT logs-2020-01-01
GET logs-2020-01-01
```
```json
{
"logs-2020-01-01": {
"aliases": {
"my_logs": {}
},
"mappings": {
"properties": {
"timestamp": {
"type": "date",
"format": "yyyy-MM-dd HH:mm:ss||yyyy-MM-dd||epoch_millis"
},
"value": {
"type": "double"
}
}
},
"settings": {
"index": {
"creation_date": "1578107970779",
"number_of_shards": "2",
"number_of_replicas": "1",
"uuid": "U1vMDMOHSAuS2IzPcPHpOA",
"version": {
"created": "7010199"
},
"provided_name": "logs-2020-01-01"
}
}
}
}
```
Any additional indices that match this pattern---`logs-2020-01-02`, `logs-2020-01-03`, and so on---will inherit the same mappings and settings.
### Retrieve a template
To list all index templates:
```json
GET _cat/templates
```
To find a template by its name:
```json
GET _index_template/daily_logs
```
To get a list of all your templates:
```json
GET _index_template/daily_logs
```
To get a list of all templates that match a pattern:
```json
GET _index_template/daily*
```
To check if a specific template exists:
```json
HEAD _index_template/<name>
```
### Configure multiple templates
You can create multiple index templates for your indices. If the index name matches more than one template, OpenSearch merges all mappings and settings from all matching templates and applies them to the index.
The settings from the more recently created index templates override the settings of older index templates. So, you can first define a few common settings in a generic template that can act as a catch-all and then add more specialized settings as required.
An even better approach is to explicitly specify template priority using the `order` parameter. OpenSearch applies templates with lower priority numbers first and then overrides them with templates with higher priority numbers.
For example, say you have the following two templates that both match the `logs-2020-01-02` index and there’s a conflict in the `number_of_shards` field:
#### Template 1
```json
PUT _index_template/template-01
{
"index_patterns": [
"logs*"
],
"priority": 0,
"template": {
"settings": {
"number_of_shards": 2
}
}
}
```
#### Template 2
```json
PUT _index_template/template-02
{
"index_patterns": [
"logs-2020-01-*"
],
"priority": 1,
"template": {
"settings": {
"number_of_shards": 3
}
}
}
```
Because `template-02` has a higher `priority` value, it takes precedence over `template-01` . The `logs-2020-01-02` index would have the `number_of_shards` value as 3.
### Delete a template
You can delete an index template using its name:
```json
DELETE _index_template/daily_logs
```
## Composable index templates
Managing multiple index templates has the following challenges:
- If you have duplication between index templates, storing these index templates results in a bigger cluster state.
- If you want to make a change across all your index templates, you have to manually make the change for each template.
- If an index matches multiple templates, OpenSearch might merge the templates in an unexpected way that you discover only after an index is created.
You can use composable index templates to overcome these challenges. Composable index templates let you abstract common settings, mappings, and aliases into a reusable building block called a component template.
You can combine component templates to compose an index template.
Settings and mappings that you specify directly in the [create index]({{site.url}}{{site.baseurl}}/opensearch/rest-api/create-index/) request override any settings or mappings specified in an index template and its component templates.
{: .note }
### Create a component template
Let's define two component templates⁠---`component_template_1` and `component_template_2`:
#### Component template 1
```json
PUT _component_template/component_template_1
{
"template": {
"mappings": {
"properties": {
"@timestamp": {
"type": "date"
}
}
}
}
}
```
#### Component template 2
```json
PUT _component_template/component_template_2
{
"template": {
"mappings": {
"properties": {
"ip_address": {
"type": "ip"
}
}
}
}
}
```
### Use component templates to create an index template
When creating index templates, you need to include the component templates in a `composed_of` list.
OpenSearch applies the component templates in the order in which you specify them within the index template. The settings, mappings, and aliases that you specify inside the index template are applied last.
```json
PUT _index_template/daily_logs
{
"index_patterns": [
"logs-2020-01-*"
],
"template": {
"aliases": {
"my_logs": {}
},
"settings": {
"number_of_shards": 2,
"number_of_replicas": 1
},
"mappings": {
"properties": {
"timestamp": {
"type": "date",
"format": "yyyy-MM-dd HH:mm:ss||yyyy-MM-dd||epoch_millis"
},
"value": {
"type": "double"
}
}
}
},
"priority": 200,
"composed_of": [
"component_template_1",
"component_template_2"
],
"version": 3,
"_meta": {
"description": "using component templates"
}
}
```
If you create an index named `logs-2020-01-01`, you can see that it derives its mappings and settings from both the component templates:
```json
PUT logs-2020-01-01
GET logs-2020-01-01
```
#### Sample response
```json
{
"logs-2020-01-01": {
"aliases": {
"my_logs": {}
},
"mappings": {
"properties": {
"@timestamp": {
"type": "date"
},
"ip_address": {
"type": "ip"
},
"timestamp": {
"type": "date",
"format": "yyyy-MM-dd HH:mm:ss||yyyy-MM-dd||epoch_millis"
},
"value": {
"type": "double"
}
}
},
"settings": {
"index": {
"creation_date": "1625382479459",
"number_of_shards": "2",
"number_of_replicas": "1",
"uuid": "rYUlpOXDSUSuZifQLPfa5A",
"version": {
"created": "7100299"
},
"provided_name": "logs-2020-01-01"
}
}
}
}
```
### Simulate multi-component templates
For index templates composed of multiple component templates, you can simulate applying a new template to verify whether the settings are applied as you expect.
To simulate the settings that would be applied to a specific index name:
```json
POST _index_template/_simulate_index/<index_name>
```
To simulate the settings that would be applied from an existing template:
```json
POST _index_template/_simulate/<index_template>
```
You can also specify a template definition in the simulate request:
```json
POST _index_template/_simulate
{
"index_patterns": [
"logs-2020-01-*"
],
"template": {
"settings" : {
"index.number_of_shards" : 3
}
},
"composed_of": ["component_template_1", "component_template_2"]
}
```
The `_simulate` API returns the final settings, mappings, and aliases that will be applied to indices that match the index pattern. You can also see any overlapping templates whose configuration is superseded by the simulated template body or higher priority templates:
```json
{
"template" : {
"settings" : {
"index" : {
"number_of_shards" : "3"
}
},
"mappings" : {
"properties" : {
"@timestamp" : {
"type" : "date"
},
"ip_address" : {
"type" : "ip"
}
}
},
"aliases" : { }
},
"overlapping" : [
{
"name" : "daily_logs",
"index_patterns" : [
"logs-2020-01-*"
]
}
]
}
```
## Index template options
You can specify the following template options:
Option | Type | Description | Required
:--- | :--- | :--- | :---
`template` | `Object` | Specify index settings, mappings, and aliases. | No
`priority` | `Integer` | The priority of the index template. | No
`composed_of` | `String array` | The names of component templates applied on a new index together with the current template. | No
`version` | `Integer` | Specify a version number to simplify template management. Default is `null`. | No
`_meta ` | `Object` | Specify meta information about the template. | No

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---
layout: default
title: About OpenSearch
nav_order: 1
has_children: false
has_toc: false
redirect_from:
- /opensearch/
---
# Introduction to OpenSearch
OpenSearch is a distributed search and analytics engine based on [Apache Lucene](https://lucene.apache.org/). After adding your data to OpenSearch, you can perform full-text searches on it with all of the features you might expect: search by field, search multiple indices, boost fields, rank results by score, sort results by field, and aggregate results.
Unsurprisingly, people often use search engines like OpenSearch as the backend for a search application---think [Wikipedia](https://en.wikipedia.org/wiki/Wikipedia:FAQ/Technical#What_software_is_used_to_run_Wikipedia?) or an online store. It offers excellent performance and can scale up and down as the needs of the application grow or shrink.
An equally popular, but less obvious use case is log analytics, in which you take the logs from an application, feed them into OpenSearch, and use the rich search and visualization functionality to identify issues. For example, a malfunctioning web server might throw a 500 error 0.5% of the time, which can be hard to notice unless you have a real-time graph of all HTTP status codes that the server has thrown in the past four hours. You can use [OpenSearch Dashboards]({{site.url}}{{site.baseurl}}/dashboards/) to build these sorts of visualizations from data in OpenSearch.
## Clusters and nodes
Its distributed design means that you interact with OpenSearch *clusters*. Each cluster is a collection of one or more *nodes*, servers that store your data and process search requests.
You can run OpenSearch locally on a laptop---its system requirements are minimal---but you can also scale a single cluster to hundreds of powerful machines in a data center.
In a single node cluster, such as a laptop, one machine has to do everything: manage the state of the cluster, index and search data, and perform any preprocessing of data prior to indexing it. As a cluster grows, however, you can subdivide responsibilities. Nodes with fast disks and plenty of RAM might be great at indexing and searching data, whereas a node with plenty of CPU power and a tiny disk could manage cluster state. For more information on setting node types, see [Cluster formation]({{site.url}}{{site.baseurl}}/opensearch/cluster/).
## Indices and documents
OpenSearch organizes data into *indices*. Each index is a collection of JSON *documents*. If you have a set of raw encyclopedia articles or log lines that you want to add to OpenSearch, you must first convert them to [JSON](https://www.json.org/). A simple JSON document for a movie might look like this:
```json
{
"title": "The Wind Rises",
"release_date": "2013-07-20"
}
```
When you add the document to an index, OpenSearch adds some metadata, such as the unique document *ID*:
```json
{
"_index": "<index-name>",
"_type": "_doc",
"_id": "<document-id>",
"_version": 1,
"_source": {
"title": "The Wind Rises",
"release_date": "2013-07-20"
}
}
```
Indices also contain mappings and settings:
- A *mapping* is the collection of *fields* that documents in the index have. In this case, those fields are `title` and `release_date`.
- Settings include data like the index name, creation date, and number of shards.
## Primary and replica shards
OpenSearch splits indices into *shards* for even distribution across nodes in a cluster. For example, a 400 GB index might be too large for any single node in your cluster to handle, but split into ten shards, each one 40 GB, OpenSearch can distribute the shards across ten nodes and work with each shard individually.
By default, OpenSearch creates a *replica* shard for each *primary* shard. If you split your index into ten shards, for example, OpenSearch also creates ten replica shards. These replica shards act as backups in the event of a node failure---OpenSearch distributes replica shards to different nodes than their corresponding primary shards---but they also improve the speed and rate at which the cluster can process search requests. You might specify more than one replica per index for a search-heavy workload.
Despite being a piece of an OpenSearch index, each shard is actually a full Lucene index---confusing, we know. This detail is important, though, because each instance of Lucene is a running process that consumes CPU and memory. More shards is not necessarily better. Splitting a 400 GB index into 1,000 shards, for example, would place needless strain on your cluster. A good rule of thumb is to keep shard size between 10--50 GB.
## REST API
You interact with OpenSearch clusters using the REST API, which offers a lot of flexibility. You can use clients like [curl](https://curl.haxx.se/) or any programming language that can send HTTP requests. To add a JSON document to an OpenSearch index (i.e. index a document), you send an HTTP request:
```json
PUT https://<host>:<port>/<index-name>/_doc/<document-id>
{
"title": "The Wind Rises",
"release_date": "2013-07-20"
}
```
To run a search for the document:
```
GET https://<host>:<port>/<index-name>/_search?q=wind
```
To delete the document:
```
DELETE https://<host>:<port>/<index-name>/_doc/<document-id>
```
You can change most OpenSearch settings using the REST API, modify indices, check the health of the cluster, get statistics---almost everything.

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---
layout: default
title: Compatibility
parent: Install OpenSearch
nav_order: 2
---
# Operating system and JVM compatibility
- We recommend installing OpenSearch on RHEL- or Debian-based Linux distributions that use [systemd](https://en.wikipedia.org/wiki/Systemd), such as CentOS, Amazon Linux 2, and Ubuntu (LTS). OpenSearch should work on many Linux distributions, but we only test a handful.
- The OpenSearch tarball ships with a compatible version of Java in the `jdk` directory. To find its version, run `./jdk/bin/java -version`. For example, the OpenSearch 1.0.0 tarball ships with Java 15 (non-LTS).
{% comment %}`./jdk/bin/java -version` doesn't work on macOS with zsh at the moment, and I have no idea why. Maybe we need a macOS artifact. Regardless, the command works on Amazon Linux 2 with bash and presumably other distros. - aetter{% endcomment %}
To use a different Java installation, set the `OPENSEARCH_JAVA_HOME` environment variable to the Java install location. We recommend Java 11 (LTS), but OpenSearch also works with Java 8.
OpenSearch version | Compatible Java versions | Recommended operating systems
:--- | :--- | :---
1.x | 8, 11 | Red Hat Enterprise Linux 7, 8; CentOS 7, 8; Amazon Linux 2; Ubuntu 16.04, 18.04, 20.04

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---
layout: default
title: Docker security configuration
parent: Install OpenSearch
nav_order: 5
---
# Docker security configuration
Before deploying to a production environment, you should replace the demo security certificates and configuration YAML files with your own. With the tarball, you have direct access to the file system, but the Docker image requires modifying the Docker storage volumes to include the replacement files.
Additionally, you can set the Docker environment variable `DISABLE_INSTALL_DEMO_CONFIG` to `true`. This change completely disables the demo installer.
## Sample Docker Compose file
```yml
version: '3'
services:
opensearch-node1:
image: opensearchproject/opensearch:{{site.opensearch_version}}
container_name: opensearch-node1
environment:
- cluster.name=opensearch-cluster
- node.name=opensearch-node1
- discovery.seed_hosts=opensearch-node1,opensearch-node2
- cluster.initial_master_nodes=opensearch-node1,opensearch-node2
- bootstrap.memory_lock=true # along with the memlock settings below, disables swapping
- "OPENSEARCH_JAVA_OPTS=-Xms512m -Xmx512m" # minimum and maximum Java heap size, recommend setting both to 50% of system RAM
- network.host=0.0.0.0 # required if not using the demo security configuration
ulimits:
memlock:
soft: -1
hard: -1
nofile:
soft: 65536 # maximum number of open files for the OpenSearch user, set to at least 65536 on modern systems
hard: 65536
volumes:
- opensearch-data1:/usr/share/opensearch/data
- ./root-ca.pem:/usr/share/opensearch/config/root-ca.pem
- ./node.pem:/usr/share/opensearch/config/node.pem
- ./node-key.pem:/usr/share/opensearch/config/node-key.pem
- ./admin.pem:/usr/share/opensearch/config/admin.pem
- ./admin-key.pem:/usr/share/opensearch/config/admin-key.pem
- ./custom-opensearch.yml:/usr/share/opensearch/config/opensearch.yml
- ./internal_users.yml:/usr/share/opensearch/plugins/opensearch-security/securityconfig/internal_users.yml
- ./roles_mapping.yml:/usr/share/opensearch/plugins/opensearch-security/securityconfig/roles_mapping.yml
- ./tenants.yml:/usr/share/opensearch/plugins/opensearch-security/securityconfig/tenants.yml
- ./roles.yml:/usr/share/opensearch/plugins/opensearch-security/securityconfig/roles.yml
- ./action_groups.yml:/usr/share/opensearch/plugins/opensearch-security/securityconfig/action_groups.yml
ports:
- 9200:9200
- 9600:9600 # required for Performance Analyzer
networks:
- opensearch-net
opensearch-node2:
image: opensearchproject/opensearch:{{site.opensearch_version}}
container_name: opensearch-node2
environment:
- cluster.name=opensearch-cluster
- node.name=opensearch-node2
- discovery.seed_hosts=opensearch-node1,opensearch-node2
- cluster.initial_master_nodes=opensearch-node1,opensearch-node2
- bootstrap.memory_lock=true
- "OPENSEARCH_JAVA_OPTS=-Xms512m -Xmx512m"
- network.host=0.0.0.0
ulimits:
memlock:
soft: -1
hard: -1
nofile:
soft: 65536
hard: 65536
volumes:
- opensearch-data2:/usr/share/opensearch/data
- ./root-ca.pem:/usr/share/opensearch/config/root-ca.pem
- ./node.pem:/usr/share/opensearch/config/node.pem
- ./node-key.pem:/usr/share/opensearch/config/node-key.pem
- ./admin.pem:/usr/share/opensearch/config/admin.pem
- ./admin-key.pem:/usr/share/opensearch/config/admin-key.pem
- ./custom-opensearch.yml:/usr/share/opensearch/config/opensearch.yml
- ./internal_users.yml:/usr/share/opensearch/plugins/opensearch-security/securityconfig/internal_users.yml
- ./roles_mapping.yml:/usr/share/opensearch/plugins/opensearch-security/securityconfig/roles_mapping.yml
- ./tenants.yml:/usr/share/opensearch/plugins/opensearch-security/securityconfig/tenants.yml
- ./roles.yml:/usr/share/opensearch/plugins/opensearch-security/securityconfig/roles.yml
- ./action_groups.yml:/usr/share/opensearch/plugins/opensearch-security/securityconfig/action_groups.yml
networks:
- opensearch-net
opensearch-dashboards
image: opensearchproject/opensearch-dashboards:{{site.opensearch_version}}
container_name: opensearch-dashboards
ports:
- 5601:5601
expose:
- "5601"
environment:
OPENSEARCH_HOSTS: '["https://opensearch-node1:9200","https://opensearch-node2:9200"]' # must be a string with no spaces when specified as an environment variable
volumes:
- ./custom-opensearch_dashboards.yml:/usr/share/opensearch-dashboards/config/opensearch_dashboards.yml
networks:
- opensearch-net
volumes:
opensearch-data1:
opensearch-data2:
networks:
opensearch-net:
```
Then make your changes to `opensearch.yml`. For a full list of settings, see [Security]({{site.url}}{{site.baseurl}}/security-plugin/configuration/index/). This example adds (extremely) verbose audit logging:
```yml
plugins.security.ssl.transport.pemcert_filepath: node.pem
plugins.security.ssl.transport.pemkey_filepath: node-key.pem
plugins.security.ssl.transport.pemtrustedcas_filepath: root-ca.pem
plugins.security.ssl.transport.enforce_hostname_verification: false
plugins.security.ssl.http.enabled: true
plugins.security.ssl.http.pemcert_filepath: node.pem
plugins.security.ssl.http.pemkey_filepath: node-key.pem
plugins.security.ssl.http.pemtrustedcas_filepath: root-ca.pem
plugins.security.allow_default_init_securityindex: true
plugins.security.authcz.admin_dn:
- CN=A,OU=UNIT,O=ORG,L=TORONTO,ST=ONTARIO,C=CA
plugins.security.nodes_dn:
- 'CN=N,OU=UNIT,O=ORG,L=TORONTO,ST=ONTARIO,C=CA'
plugins.security.audit.type: internal_opensearch
plugins.security.enable_snapshot_restore_privilege: true
plugins.security.check_snapshot_restore_write_privileges: true
plugins.security.restapi.roles_enabled: ["all_access", "security_rest_api_access"]
cluster.routing.allocation.disk.threshold_enabled: false
plugins.security.audit.config.disabled_rest_categories: NONE
plugins.security.audit.config.disabled_transport_categories: NONE
```
Use this same override process to specify new [authentication settings]({{site.url}}{{site.baseurl}}/security-plugin/configuration/configuration/) in `/usr/share/opensearch/plugins/opensearch-security/securityconfig/config.yml`, as well as new default [internal users, roles, mappings, action groups, and tenants]({{site.url}}{{site.baseurl}}/security-plugin/configuration/yaml/).
To start the cluster, run `docker-compose up`.
If you encounter any `File /usr/share/opensearch/config/opensearch.yml has insecure file permissions (should be 0600)` messages, you can use `chmod` to set file permissions before running `docker-compose up`. Docker Compose passes files to the container as-is.
{: .note }
Finally, you can reach OpenSearch Dashboards at http://localhost:5601, sign in, and use the **Security** panel to perform other management tasks.
## Using certificates with Docker
To use your own certificates in your configuration, add all of the necessary certificates to the volumes section of the Docker Compose file:
```yml
volumes:
- ./root-ca.pem:/full/path/to/certificate.pem
- ./admin.pem:/full/path/to/certificate.pem
- ./admin-key.pem:/full/path/to/certificate.pem
#Add other certificates
```
After replacing the demo certificates with your own, you must also include a custom `opensearch.yml` in your setup, which you need to specify in the volumes section.
```yml
volumes:
#Add certificates here
- ./custom-opensearch.yml: /full/path/to/custom-opensearch.yml
```
Remember that the certificates you specify in your Docker Compose file must be the same as the certificates listed in your custom `opensearch.yml` file. At a minimum, you should replace the root, admin, and node certificates with your own. For more information about adding and using certificates, see [Configure TLS certificates]({{site.url}}{{site.baseurl}}/security-plugin/configuration/tls).
```yml
plugins.security.ssl.transport.pemcert_filepath: new-node-cert.pem
plugins.security.ssl.transport.pemkey_filepath: new-node-cert-key.pem
plugins.security.ssl.transport.pemtrustedcas_filepath: new-root-ca.pem
plugins.security.ssl.http.pemcert_filepath: new-node-cert.pem
plugins.security.ssl.http.pemkey_filepath: new-node-cert-key.pem
plugins.security.ssl.http.pemtrustedcas_filepath: new-root-ca.pem
plugins.security.authcz.admin_dn:
- CN=admin,OU=SSL,O=Test,L=Test,C=DE
```
To start the cluster, run `docker-compose up` as usual.

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@ -1,324 +0,0 @@
---
layout: default
title: Docker
parent: Install OpenSearch
nav_order: 3
---
# Docker image
You can pull the OpenSearch Docker image just like any other image:
```bash
docker pull opensearchproject/opensearch:{{site.opensearch_version}}
docker pull opensearchproject/opensearch-dashboards:{{site.opensearch_version}}
```
To check available versions, see [Docker Hub](https://hub.docker.com/u/opensearchproject).
OpenSearch images use `amazonlinux:2` as the base image. If you run Docker locally, set Docker to use at least 4 GB of RAM in **Preferences** > **Resources**.
---
#### Table of contents
1. TOC
{:toc}
---
## Run the image
To run the image for local development:
```bash
docker run -p 9200:9200 -p 9600:9600 -e "discovery.type=single-node" opensearchproject/opensearch:{{site.opensearch_version}}
```
Then send requests to the server to verify that OpenSearch is up and running:
```bash
curl -XGET https://localhost:9200 -u 'admin:admin' --insecure
curl -XGET https://localhost:9200/_cat/nodes?v -u 'admin:admin' --insecure
curl -XGET https://localhost:9200/_cat/plugins?v -u 'admin:admin' --insecure
```
To find the container ID:
```bash
docker ps
```
Then you can stop the container using:
```bash
docker stop <container-id>
```
## Start a cluster
To deploy multiple nodes and simulate a more realistic deployment, create a [docker-compose.yml](https://docs.docker.com/compose/compose-file/) file appropriate for your environment and run:
```bash
docker-compose up
```
To stop the cluster, run:
```bash
docker-compose down
```
To stop the cluster and delete all data volumes, run:
```bash
docker-compose down -v
```
#### Sample Docker Compose file
This sample file starts two data nodes and a container for OpenSearch Dashboards.
```yml
version: '3'
services:
opensearch-node1:
image: opensearchproject/opensearch:{{site.opensearch_version}}
container_name: opensearch-node1
environment:
- cluster.name=opensearch-cluster
- node.name=opensearch-node1
- discovery.seed_hosts=opensearch-node1,opensearch-node2
- cluster.initial_master_nodes=opensearch-node1,opensearch-node2
- bootstrap.memory_lock=true # along with the memlock settings below, disables swapping
- "OPENSEARCH_JAVA_OPTS=-Xms512m -Xmx512m" # minimum and maximum Java heap size, recommend setting both to 50% of system RAM
ulimits:
memlock:
soft: -1
hard: -1
nofile:
soft: 65536 # maximum number of open files for the OpenSearch user, set to at least 65536 on modern systems
hard: 65536
volumes:
- opensearch-data1:/usr/share/opensearch/data
ports:
- 9200:9200
- 9600:9600 # required for Performance Analyzer
networks:
- opensearch-net
opensearch-node2:
image: opensearchproject/opensearch:{{site.opensearch_version}}
container_name: opensearch-node2
environment:
- cluster.name=opensearch-cluster
- node.name=opensearch-node2
- discovery.seed_hosts=opensearch-node1,opensearch-node2
- cluster.initial_master_nodes=opensearch-node1,opensearch-node2
- bootstrap.memory_lock=true
- "OPENSEARCH_JAVA_OPTS=-Xms512m -Xmx512m"
ulimits:
memlock:
soft: -1
hard: -1
nofile:
soft: 65536
hard: 65536
volumes:
- opensearch-data2:/usr/share/opensearch/data
networks:
- opensearch-net
opensearch-dashboards:
image: opensearchproject/opensearch-dashboards:{{site.opensearch_version}}
container_name: opensearch-dashboards
ports:
- 5601:5601
expose:
- "5601"
environment:
OPENSEARCH_HOSTS: '["https://opensearch-node1:9200","https://opensearch-node2:9200"]' # must be a string with no spaces when specified as an environment variable
networks:
- opensearch-net
volumes:
opensearch-data1:
opensearch-data2:
networks:
opensearch-net:
```
If you override `opensearch_dashboards.yml` settings using environment variables, as seen above, use all uppercase letters and periods in place of underscores (e.g. for `opensearch.hosts`, use `OPENSEARCH_HOSTS`).
{: .note}
## Configure OpenSearch
You can pass a custom `opensearch.yml` file to the Docker container using the [`-v` flag](https://docs.docker.com/engine/reference/commandline/run#mount-volume--v---read-only) for `docker run`:
```bash
docker run \
-p 9200:9200 -p 9600:9600 \
-e "discovery.type=single-node" \
-v /<full-path-to>/custom-opensearch.yml:/usr/share/opensearch/config/opensearch.yml \
opensearchproject/opensearch:{{site.opensearch_version}}
```
You can perform the same operation in `docker-compose.yml` using a relative path:
```yml
services:
opensearch-node1:
volumes:
- opensearch-data1:/usr/share/opensearch/data
- ./custom-opensearch.yml:/usr/share/opensearch/config/opensearch.yml
opensearch-node2:
volumes:
- opensearch-data2:/usr/share/opensearch/data
- ./custom-opensearch.yml:/usr/share/opensearch/config/opensearch.yml
opensearch-dashboards
volumes:
- ./custom-opensearch_dashboards.yml:/usr/share/opensearch-dashboards/config/opensearch_dashboards.yml
```
You can also configure `docker-compose.yml` and `opensearch.yml` [to take your own certificates]({{site.url}}{{site.baseurl}}/opensearch/install/docker-security/) for use with the [Security]({{site.url}}{{site.baseurl}}/security-plugin/configuration/index/) plugin.
### (Optional) Set up Performance Analyzer
1. Enable the Performance Analyzer plugin:
```bash
curl -XPOST localhost:9200/_plugins/_performanceanalyzer/cluster/config -H 'Content-Type: application/json' -d '{"enabled": true}'
```
If you receive the `curl: (52) Empty reply from server` error, you are likely protecting your cluster with the security plugin and you need to provide credentials. Modify the following command to use your username and password:
```bash
curl -XPOST https://localhost:9200/_plugins/_performanceanalyzer/cluster/config -H 'Content-Type: application/json' -d '{"enabled": true}' -u 'admin:admin' -k
```
1. Enable the Root Cause Analyzer (RCA) framework
```bash
curl -XPOST localhost:9200/_plugins/_performanceanalyzer/rca/cluster/config -H 'Content-Type: application/json' -d '{"enabled": true}'
```
Similar to step 1, if you run into `curl: (52) Empty reply from server`, run the command below to enable RCA
```bash
curl -XPOST https://localhost:9200/_plugins/_performanceanalyzer/rca/cluster/config -H 'Content-Type: application/json' -d '{"enabled": true}' -u 'admin:admin' -k
```
1. By default, Performance Analyzer's endpoints are not accessible from outside the Docker container.
To edit this behavior, open a shell session in the container and modify the configuration:
```bash
docker ps # Look up the container id
docker exec -it <container-id> /bin/bash
# Inside container
cd plugins/opensearch_performance_analyzer/pa_config/
vi performance-analyzer.properties
```
Uncomment the line `#webservice-bind-host` and set it to `0.0.0.0`:
```
# ======================== OpenSearch performance analyzer plugin config =========================
# NOTE: this is an example for Linux. Please modify the config accordingly if you are using it under other OS.
# WebService bind host; default to all interfaces
webservice-bind-host = 0.0.0.0
# Metrics data location
metrics-location = /dev/shm/performanceanalyzer/
# Metrics deletion interval (minutes) for metrics data.
# Interval should be between 1 to 60.
metrics-deletion-interval = 1
# If set to true, the system cleans up the files behind it. So at any point, we should expect only 2
# metrics-db-file-prefix-path files. If set to false, no files are cleaned up. This can be useful, if you are archiving
# the files and wouldn't like for them to be cleaned up.
cleanup-metrics-db-files = true
# WebService exposed by App's port
webservice-listener-port = 9600
# Metric DB File Prefix Path location
metrics-db-file-prefix-path = /tmp/metricsdb_
https-enabled = false
#Setup the correct path for certificates
certificate-file-path = specify_path
private-key-file-path = specify_path
# Plugin Stats Metadata file name, expected to be in the same location
plugin-stats-metadata = plugin-stats-metadata
# Agent Stats Metadata file name, expected to be in the same location
agent-stats-metadata = agent-stats-metadata
```
1. Then restart the Performance Analyzer agent:
```bash
kill $(ps aux | grep -i 'PerformanceAnalyzerApp' | grep -v grep | awk '{print $2}')
```
## Bash access to containers
To create an interactive Bash session in a container, run `docker ps` to find the container ID. Then run:
```bash
docker exec -it <container-id> /bin/bash
```
## Customize the Docker image
To run the image with a custom plugin, first create a [`Dockerfile`](https://docs.docker.com/engine/reference/builder/):
```
FROM opensearchproject/opensearch:{{site.opensearch_version}}
RUN /usr/share/opensearch/bin/opensearch-plugin install --batch <plugin-name-or-url>
```
Then run the following commands:
```bash
docker build --tag=opensearch-custom-plugin .
docker run -p 9200:9200 -p 9600:9600 -v /usr/share/opensearch/data opensearch-custom-plugin
```
You can also use a `Dockerfile` to pass your own certificates for use with the [security]({{site.url}}{{site.baseurl}}/security-plugin/) plugin, similar to the `-v` argument in [Configure OpenSearch](#configure-opensearch):
```
FROM opensearchproject/opensearch:{{site.opensearch_version}}
COPY --chown=opensearch:opensearch opensearch.yml /usr/share/opensearch/config/
COPY --chown=opensearch:opensearch my-key-file.pem /usr/share/opensearch/config/
COPY --chown=opensearch:opensearch my-certificate-chain.pem /usr/share/opensearch/config/
COPY --chown=opensearch:opensearch my-root-cas.pem /usr/share/opensearch/config/
```
Alternately, you might want to remove a plugin. This `Dockerfile` removes the security plugin:
```
FROM opensearchproject/opensearch:{{site.opensearch_version}}
RUN /usr/share/opensearch/bin/opensearch-plugin remove opensearch-security
COPY --chown=opensearch:opensearch opensearch.yml /usr/share/opensearch/config/
```
In this case, `opensearch.yml` is a "vanilla" version of the file with no plugin entries. It might look like this:
```yml
cluster.name: "docker-cluster"
network.host: 0.0.0.0
```

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---
layout: default
title: Important settings
parent: Install OpenSearch
nav_order: 70
---
# Important settings
For production workloads, make sure the [Linux setting](https://www.kernel.org/doc/Documentation/sysctl/vm.txt) `vm.max_map_count` is set to at least 262144. On the OpenSearch Docker image, this setting is the default. To check, start a Bash session in the container and run:
```bash
cat /proc/sys/vm/max_map_count
```
To increase this value, you have to modify the Docker image. For other install types, add this setting to the host machine's `/etc/sysctl.conf` file with the following line:
```
vm.max_map_count=262144
```
Then run `sudo sysctl -p` to reload.
The [sample docker-compose.yml]({{site.url}}{{site.baseurl}}/opensearch/install/docker#sample-docker-compose-file) file also contains several key settings:
- `bootstrap.memory_lock=true`
Disbles swapping (along with `memlock`). Swapping can dramatically decrease performance and stability, so you should ensure it is disabled on production clusters.
- `OPENSEARCH_JAVA_OPTS=-Xms512m -Xmx512m`
Sets the size of the Java heap (we recommend half of system RAM).
- `nofile 65536`
Sets a limit of 65536 open files for the OpenSearch user.
- `port 9600`
Allows you to access Performance Analyzer on port 9600.

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@ -1,13 +0,0 @@
---
layout: default
title: Install OpenSearch
nav_order: 2
redirect_from:
- /docs/install/
- /opensearch/install/
has_children: true
---
# Install and configure OpenSearch
OpenSearch has two installation options at this time: Docker images and tarballs.

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---
layout: default
title: OpenSearch plugins
parent: Install OpenSearch
nav_order: 90
---
# Standalone OpenSearch plugin installation
If you don't want to use the all-in-one OpenSearch installation options, you can install the individual plugins on a compatible OpenSearch cluster, just like any other plugin.
---
#### Table of contents
1. TOC
{:toc}
---
## Plugin compatibility
<table>
<thead style="text-align: left">
<tr>
<th>OpenSearch version</th>
<th>Plugin versions</th>
</tr>
</thead>
<tbody>
<tr>
<td>1.0.0</td>
<td>
<pre>opensearch-alerting 1.0.0.0
opensearch-anomaly-detection 1.0.0.0
opensearch-asynchronous-search 1.0.0.0
opensearch-index-management 1.0.0.0
opensearch-job-scheduler 1.0.0.0
opensearch-knn 1.0.0.0
opensearch-notebooks 1.0.0.0
opensearch-performance-analyzer 1.0.0.0
opensearch-reports-scheduler 1.0.0.0
opensearch-security 1.0.0.0
opensearch-sql 1.0.0.0
</pre>
</td>
</tr>
<tr>
<td>1.0.0-rc1</td>
<td>
<pre>opensearch-alerting 1.0.0.0-rc1
opensearch-anomaly-detection 1.0.0.0-rc1
opensearch-asynchronous-search 1.0.0.0-rc1
opensearch-index-management 1.0.0.0-rc1
opensearch-job-scheduler 1.0.0.0-rc1
opensearch-knn 1.0.0.0-rc1
opensearch-notebooks 1.0.0.0-rc1
opensearch-performance-analyzer 1.0.0.0-rc1
opensearch-reports-scheduler 1.0.0.0-rc1
opensearch-security 1.0.0.0-rc1
opensearch-sql 1.0.0.0-rc1
</pre>
</td>
</tr>
<tr>
<td>1.0.0-beta1</td>
<td>
<pre>opensearch-alerting 1.0.0.0-beta1
opensearch-anomaly-detection 1.0.0.0-beta1
opensearch-asynchronous-search 1.0.0.0-beta1
opensearch-index-management 1.0.0.0-beta1
opensearch-job-scheduler 1.0.0.0-beta1
opensearch-knn 1.0.0.0-beta1
opensearch-performance-analyzer 1.0.0.0-beta1
opensearch-reports-scheduler 1.0.0.0-beta1
opensearch-security 1.0.0.0-beta1
opensearch-sql 1.0.0.0-beta1
</pre>
</td>
</tr>
</tbody>
</table>
To install plugins manually, you must have the exact version of OpenSearch installed, down to the minor version.
{% comment %}
To get a list of available OpenSearch versions on CentOS 7 and Amazon Linux 2, run the following command:
```bash
sudo yum list opensearch-oss --showduplicates
```
Then you can specify the version that you need:
```bash
sudo yum install opensearch-oss-6.7.1
```
## Install plugins
Navigate to the OpenSearch home directory (most likely, it is `/usr/share/opensearch`), and run the install command for each plugin.
### Security
```bash
sudo bin/opensearch-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-plugins/opensearch-security/opensearch-security-{{site.opensearch_major_minor_version}}.1.0.zip
```
After installing the security plugin, you can run `sudo sh /usr/share/opensearch/plugins/opensearch-security/tools/install_demo_configuration.sh` to quickly get started with demo certificates. Otherwise, you must configure it manually and run [securityadmin.sh]({{site.url}}{{site.baseurl}}/security-plugin/configuration/security-admin/).
The security plugin has a corresponding [OpenSearch Dashboards plugin]({{site.url}}{{site.baseurl}}/opensearch-dashboards/install/plugins) that you probably want to install as well.
### Job scheduler
```bash
sudo bin/opensearch-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-plugins/opensearch-job-scheduler/opensearch-job-scheduler-{{site.opensearch_major_minor_version}}.0.0.zip
```
### Alerting
```bash
sudo bin/opensearch-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-plugins/opensearch-alerting/opensearch-alerting-{{site.opensearch_major_minor_version}}.1.0.zip
```
To install Alerting, you must first install the Job Scheduler plugin. Alerting has a corresponding [OpenSearch Dashboards plugin]({{site.url}}{{site.baseurl}}/opensearch-dashboards/install/plugins/) that you probably want to install as well.
### SQL
```bash
sudo bin/opensearch-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-plugins/opensearch-sql/opensearch-sql-{{site.opensearch_major_minor_version}}.2.0.zip
```
### Reports scheduler
```bash
sudo bin/opensearch-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-plugins/opensearch-reports-scheduler/opensearch-reports-scheduler-{{site.opensearch_major_minor_version}}.0.0.zip
```
### Index State Management
```bash
sudo bin/opensearch-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-plugins/opensearch-index-management/opensearch-index-management-{{site.opensearch_major_minor_version}}.2.0.zip
```
To install Index State Management, you must first install the Job Scheduler plugin. ISM has a corresponding [OpenSearch Dashboards plugin]({{site.url}}{{site.baseurl}}/opensearch-dashboards/install/plugins/) that you probably want to install as well.
### k-NN
k-NN is only available as part of the all-in-one installs: Docker, RPM, and Debian.
### Anomaly detection
```bash
sudo bin/opensearch-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-plugins/opensearch-anomaly-detection/opensearch-anomaly-detection-{{site.opensearch_major_minor_version}}.0.0.zip
```
### Asynchronous search
```bash
sudo bin/opensearch-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-plugins/opensearch-asynchronous-search/opensearch-asynchronous-search-{{site.opensearch_major_minor_version}}.0.1.zip
```
### Performance Analyzer
```bash
sudo bin/opensearch-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/opensearch-plugins/performance-analyzer/opensearch-performance-analyzer-{{site.opensearch_major_minor_version}}.0.0.zip
```
Performance Analyzer requires some manual configuration after installing the plugin:
1. Create `/usr/lib/systemd/system/opensearch-performance-analyzer.service` based on [this file](https://github.com/opensearch-project/performance-analyzer/blob/master/packaging/opensearch-performance-analyzer.service).
1. Make the CLI executable:
```bash
sudo chmod +x /usr/share/opensearch/bin/performance-analyzer-agent-cli
```
1. Run the appropriate `postinst` script for your Linux distribution:
```bash
# Debian-based distros
sudo sh /usr/share/opensearch/plugins/opensearch-performance-analyzer/install/deb/postinst.sh 1
# RPM distros
sudo sh /usr/share/opensearch/plugins/opensearch-performance-analyzer/install/rpm/postinst.sh 1
```
1. Make Performance Analyzer accessible outside of the host machine
```bash
cd /usr/share/opensearch # navigate to the OpenSearch home directory
cd plugins/opensearch_performance_analyzer/pa_config/
vi performance-analyzer.properties
```
Uncomment the line `#webservice-bind-host` and set it to `0.0.0.0`:
```bash
# ======================== OpenSearch performance analyzer plugin config =========================
# NOTE: this is an example for Linux. Please modify the config accordingly if you are using it under other OS.
# WebService bind host; default to all interfaces
webservice-bind-host = 0.0.0.0
# Metrics data location
metrics-location = /dev/shm/performanceanalyzer/
# Metrics deletion interval (minutes) for metrics data.
# Interval should be between 1 to 60.
metrics-deletion-interval = 1
# If set to true, the system cleans up the files behind it. So at any point, we should expect only 2
# metrics-db-file-prefix-path files. If set to false, no files are cleaned up. This can be useful, if you are archiving
# the files and wouldn't like for them to be cleaned up.
cleanup-metrics-db-files = true
# WebService exposed by App's port
webservice-listener-port = 9600
# Metric DB File Prefix Path location
metrics-db-file-prefix-path = /tmp/metricsdb_
https-enabled = false
#Setup the correct path for certificates
certificate-file-path = specify_path
private-key-file-path = specify_path
# Plugin Stats Metadata file name, expected to be in the same location
plugin-stats-metadata = plugin-stats-metadata
# Agent Stats Metadata file name, expected to be in the same location
agent-stats-metadata = agent-stats-metadata
```
1. Start the OpenSearch service:
```bash
sudo systemctl start opensearch.service
```
1. Send a test request:
```bash
curl -XGET "localhost:9600/_plugins/_performanceanalyzer/metrics?metrics=Latency,CPU_Utilization&agg=avg,max&dim=ShardID&nodes=all"
```
{% endcomment %}
## List installed plugins
To check your installed plugins:
```bash
sudo bin/opensearch-plugin list
```
## Remove plugins
If you are removing Performance Analyzer, see below. Otherwise, you can remove the plugin with a single command:
```bash
sudo bin/opensearch-plugin remove <plugin-name>
```
Then restart OpenSearch on the node:
```bash
sudo systemctl restart opensearch.service
```
## Update plugins
OpenSearch doesn't update plugins. Instead, you have to remove and reinstall them:
```bash
sudo bin/opensearch-plugin remove <plugin-name>
sudo bin/opensearch-plugin install <plugin-name>
```

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@ -1,147 +0,0 @@
---
layout: default
title: Tarball
parent: Install OpenSearch
nav_order: 50
---
# Tarball
The tarball installation provides a self-contained directory with everything you need to run OpenSearch, including an integrated Java Development Kit (JDK). The tarball is a good option for testing and development.
The tarball supports most Linux distributions, including CentOS 7, Amazon Linux 2, and Ubuntu 18.04. If you have your own Java installation and set `JAVA_HOME` in the terminal, macOS works, as well.
1. Download the tarball from the [OpenSearch downloads page](https://opensearch.org/downloads.html){:target='\_blank'}.
1. Extract the TAR file to a directory and change to that directory:
```bash
# x64
tar -zxf opensearch-{{site.opensearch_version}}-linux-x64.tar.gz
cd opensearch-{{site.opensearch_version}}{% comment %}# ARM64
tar -zxf opensearch-{{site.opensearch_version}}-linux-arm64.tar.gz
cd opensearch-{{site.opensearch_version}}{% endcomment %}
```
1. Run OpenSearch:
```bash
./opensearch-tar-install.sh
```
1. Open a second terminal session, and send requests to the server to verify that OpenSearch is up and running:
```bash
curl -XGET https://localhost:9200 -u 'admin:admin' --insecure
curl -XGET https://localhost:9200/_cat/plugins?v -u 'admin:admin' --insecure
```
## Configuration
You can modify `config/opensearch.yml` or specify environment variables as arguments using `-E`:
```bash
./opensearch-tar-install.sh -Ecluster.name=opensearch-cluster -Enode.name=opensearch-node1 -Ehttp.host=0.0.0.0 -Ediscovery.type=single-node
```
For other settings, see [Important settings]({{site.url}}{{site.baseurl}}/opensearch/install/important-settings/).
### (Optional) Set up Performance Analyzer
In a tarball installation, Performance Analyzer collects data when it is enabled. But in order to read that data using the REST API on port 9600, you must first manually launch the associated reader agent process:
1. Make Performance Analyzer accessible outside of the host machine
```bash
cd /usr/share/opensearch # navigate to the OpenSearch home directory
cd plugins/opensearch_performance_analyzer/pa_config/
vi performance-analyzer.properties
```
Uncomment the line `#webservice-bind-host` and set it to `0.0.0.0`:
```
# ======================== OpenSearch performance analyzer plugin config =========================
# NOTE: this is an example for Linux. Please modify the config accordingly if you are using it under other OS.
# WebService bind host; default to all interfaces
webservice-bind-host = 0.0.0.0
# Metrics data location
metrics-location = /dev/shm/performanceanalyzer/
# Metrics deletion interval (minutes) for metrics data.
# Interval should be between 1 to 60.
metrics-deletion-interval = 1
# If set to true, the system cleans up the files behind it. So at any point, we should expect only 2
# metrics-db-file-prefix-path files. If set to false, no files are cleaned up. This can be useful, if you are archiving
# the files and wouldn't like for them to be cleaned up.
cleanup-metrics-db-files = true
# WebService exposed by App's port
webservice-listener-port = 9600
# Metric DB File Prefix Path location
metrics-db-file-prefix-path = /tmp/metricsdb_
https-enabled = false
#Setup the correct path for certificates
certificate-file-path = specify_path
private-key-file-path = specify_path
# Plugin Stats Metadata file name, expected to be in the same location
plugin-stats-metadata = plugin-stats-metadata
# Agent Stats Metadata file name, expected to be in the same location
agent-stats-metadata = agent-stats-metadata
```
1. Make the CLI executable:
```bash
sudo chmod +x ./bin/performance-analyzer-agent-cli
```
1. Launch the agent CLI:
```bash
ES_HOME="$PWD" ./bin/performance-analyzer-agent-cli
```
1. In a separate window, enable the Performance Analyzer plugin:
```bash
curl -XPOST localhost:9200/_plugins/_performanceanalyzer/cluster/config -H 'Content-Type: application/json' -d '{"enabled": true}'
```
If you receive the `curl: (52) Empty reply from server` error, you are likely protecting your cluster with the security plugin and you need to provide credentials. Modify the following command to use your username and password:
```bash
curl -XPOST https://localhost:9200/_plugins/_performanceanalyzer/cluster/config -H 'Content-Type: application/json' -d '{"enabled": true}' -u 'admin:admin' -k
```
1. Finally, enable the Root Cause Analyzer (RCA) framework
```bash
curl -XPOST localhost:9200/_plugins/_performanceanalyzer/rca/cluster/config -H 'Content-Type: application/json' -d '{"enabled": true}'
```
Similar to step 4, if you run into `curl: (52) Empty reply from server`, run the command below to enable RCA
```bash
curl -XPOST https://localhost:9200/_plugins/_performanceanalyzer/rca/cluster/config -H 'Content-Type: application/json' -d '{"enabled": true}' -u 'admin:admin' -k
```
{% comment %}
### (Optional) Removing Performance Analyzer
See [Clean up Performance Analyzer files]({{site.url}}{{site.baseurl}}/plugins#optional-clean-up-performance-analyzer-files).
{% endcomment %}

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@ -1,174 +0,0 @@
---
layout: default
title: Logs
nav_order: 60
---
# Logs
The OpenSearch logs include valuable information for monitoring cluster operations and troubleshooting issues. The location of the logs differs based on the installation type:
- On Docker, OpenSearch writes most logs to the console and stores the remainder in `opensearch/logs/`. The tarball installation also uses `opensearch/logs/`.
- On most Linux installations, OpenSearch writes logs to `/var/log/opensearch/`.
Logs are available as `.log` (plain text) and `.json` files. Permissions for the OpenSearch logs are `-rw-r--r--` by default, meaning that any user account on the node can read them. You can change this behavior _for each log type_ in `log4j2.properties` using the `filePermissions` option. For example, you might add `appender.rolling.filePermissions = rw-r-----` to change permissions for the JSON server log. For details, see the [Log4j 2 documentation](https://logging.apache.org/log4j/2.x/manual/appenders.html#RollingFileAppender).
## Application logs
For its application logs, OpenSearch uses [Apache Log4j 2](https://logging.apache.org/log4j/2.x/) and its built-in log levels (from least to most severe) of TRACE, DEBUG, INFO, WARN, ERROR, and FATAL. The default OpenSearch log level is INFO.
Rather than changing the default log level (`logger.level`), you change the log level for individual OpenSearch modules:
```json
PUT /_cluster/settings
{
"persistent" : {
"logger.org.opensearch.index.reindex" : "DEBUG"
}
}
```
The easiest way to identify modules is not from the logs, which abbreviate the path (for example, `o.o.i.r`), but from the [OpenSearch source code](https://github.com/opensearch-project/opensearch/tree/master/server/src/main/java/org/opensearch).
{: .tip }
After this sample change, OpenSearch emits much more detailed logs during reindex operations:
```
[2019-10-18T16:52:51,184][DEBUG][o.o.i.r.TransportReindexAction] [node1] [1626]: starting
[2019-10-18T16:52:51,186][DEBUG][o.o.i.r.TransportReindexAction] [node1] executing initial scroll against [some-index]
[2019-10-18T16:52:51,291][DEBUG][o.o.i.r.TransportReindexAction] [node1] scroll returned [3] documents with a scroll id of [DXF1Z==]
[2019-10-18T16:52:51,292][DEBUG][o.o.i.r.TransportReindexAction] [node1] [1626]: got scroll response with [3] hits
[2019-10-18T16:52:51,294][DEBUG][o.o.i.r.WorkerBulkByScrollTaskState] [node1] [1626]: preparing bulk request for [0s]
[2019-10-18T16:52:51,297][DEBUG][o.o.i.r.TransportReindexAction] [node1] [1626]: preparing bulk request
[2019-10-18T16:52:51,299][DEBUG][o.o.i.r.TransportReindexAction] [node1] [1626]: sending [3] entry, [222b] bulk request
[2019-10-18T16:52:51,310][INFO ][o.e.c.m.MetaDataMappingService] [node1] [some-new-index/R-j3adc6QTmEAEb-eAie9g] create_mapping [_doc]
[2019-10-18T16:52:51,383][DEBUG][o.o.i.r.TransportReindexAction] [node1] [1626]: got scroll response with [0] hits
[2019-10-18T16:52:51,384][DEBUG][o.o.i.r.WorkerBulkByScrollTaskState] [node1] [1626]: preparing bulk request for [0s]
[2019-10-18T16:52:51,385][DEBUG][o.o.i.r.TransportReindexAction] [node1] [1626]: preparing bulk request
[2019-10-18T16:52:51,386][DEBUG][o.o.i.r.TransportReindexAction] [node1] [1626]: finishing without any catastrophic failures
[2019-10-18T16:52:51,395][DEBUG][o.o.i.r.TransportReindexAction] [node1] Freed [1] contexts
```
The DEBUG and TRACE levels are extremely verbose. If you enable either one to troubleshoot a problem, disable it after you finish.
There are other ways to change log levels:
1. Add lines to `opensearch.yml`:
```yml
logger.org.opensearch.index.reindex: debug
```
Modifying `opensearch.yml` makes the most sense if you want to reuse your logging configuration across multiple clusters or debug startup issues with a single node.
2. Modify `log4j2.properties`:
```
# Define a new logger with unique ID of reindex
logger.reindex.name = org.opensearch.index.reindex
# Set the log level for that ID
logger.reindex.level = debug
```
This approach is extremely flexible, but requires familiarity with the [Log4j 2 property file syntax](https://logging.apache.org/log4j/2.x/manual/configuration.html#Properties). In general, the other options offer a simpler configuration experience.
If you examine the default `log4j2.properties` file in the configuration directory, you can see a few OpenSearch-specific variables:
```
appender.console.layout.pattern = [%d{ISO8601}][%-5p][%-25c{1.}] [%node_name]%marker %m%n
appender.rolling_old.fileName = ${sys:os.logs.base_path}${sys:file.separator}${sys:os.logs.cluster_name}.log
```
- `${sys:os.logs.base_path}` is the directory for logs (for example, `/var/log/opensearch/`).
- `${sys:os.logs.cluster_name}` is the name of the cluster.
- `[%node_name]` is the name of the node.
## Slow logs
OpenSearch has two *slow logs*, logs that help you identify performance issues: the search slow log and the indexing slow log.
These logs rely on thresholds to define what qualifies as a "slow" search or indexing operation. For example, you might decide that a query is slow if it takes more than 15 seconds to complete. Unlike application logs, which you configure for modules, you configure slow logs for indices. By default, both logs are disabled (all thresholds are set to `-1`):
```json
GET <some-index>/_settings?include_defaults=true
{
"indexing": {
"slowlog": {
"reformat": "true",
"threshold": {
"index": {
"warn": "-1",
"trace": "-1",
"debug": "-1",
"info": "-1"
}
},
"source": "1000",
"level": "TRACE"
}
},
"search": {
"slowlog": {
"level": "TRACE",
"threshold": {
"fetch": {
"warn": "-1",
"trace": "-1",
"debug": "-1",
"info": "-1"
},
"query": {
"warn": "-1",
"trace": "-1",
"debug": "-1",
"info": "-1"
}
}
}
}
}
```
To enable these logs, increase one or more thresholds:
```json
PUT <some-index>/_settings
{
"indexing": {
"slowlog": {
"threshold": {
"index": {
"warn": "15s",
"trace": "750ms",
"debug": "3s",
"info": "10s"
}
},
"source": "500",
"level": "INFO"
}
}
}
```
In this example, OpenSearch logs indexing operations that take 15 seconds or longer at the WARN level and operations that take between 10 and 14.*x* seconds at the INFO level. If you set a threshold to 0 seconds, OpenSearch logs all operations, which can be useful for testing whether slow logs are indeed enabled.
- `reformat` specifies whether to log the document `_source` field as a single line (`true`) or let it span multiple lines (`false`).
- `source` is the number of characters of the document `_source` field to log.
- `level` is the minimum log level to include.
A line from `opensearch_index_indexing_slowlog.log` might look like this:
```
node1 | [2019-10-24T19:48:51,012][WARN][i.i.s.index] [node1] [some-index/i86iF5kyTyy-PS8zrdDeAA] took[3.4ms], took_millis[3], type[_doc], id[1], routing[], source[{"title":"Your Name", "Director":"Makoto Shinkai"}]
```
Slow logs can consume considerable disk space if you set thresholds or levels too low. Consider enabling them temporarily for troubleshooting or performance tuning. To disable slow logs, return all thresholds to `-1`.
## Deprecation logs
Deprecation logs record when clients make deprecated API calls to your cluster. These logs can help you identify and fix issues prior to upgrading to a new major version. By default, OpenSearch logs deprecated API calls at the WARN level, which works well for almost all use cases. If desired, configure `logger.deprecation.level` using `_cluster/settings`, `opensearch.yml`, or `log4j2.properties`.

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@ -1,636 +0,0 @@
---
layout: default
title: Metric Aggregations
parent: Aggregations
nav_order: 1
has_children: false
---
# Metric aggregations
Metric aggregations let you perform simple calculations such as finding the minimum, maximum, and average values of a field.
## Types of metric aggregations
Metric aggregations are of two types: single-value metric aggregations and multi-value metric aggregations.
### Single-value metric aggregations
Single-value metric aggregations return a single metric. For example, `sum`, `min`, `max`, `avg`, `cardinality`, and `value_count`.
### Multi-value metric aggregations
Multi-value metric aggregations return more than one metric. For example, `stats`, `extended_stats`, `matrix_stats`, `percentile`, `percentile_ranks`, `geo_bound`, `top_hits`, and `scripted_metric`.
## sum, min, max, avg
The `sum`, `min`, `max`, and `avg` metrics are single-value metric aggregations that return the sum, minimum, maximum, and average values of a field, respectively.
The following example calculates the total sum of the `taxful_total_price` field:
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"sum_taxful_total_price": {
"sum": {
"field": "taxful_total_price"
}
}
}
}
```
#### Sample Response
```json
...
"aggregations" : {
"sum_taxful_total_price" : {
"value" : 350884.12890625
}
}
}
```
In a similar fashion, you can find the minimum, maximum, and average values of a field.
## cardinality
The `cardinality` metric is a single-value metric aggregation that counts the number of unique or distinct values of a field.
The following example finds the number of unique products in an eCommerce store:
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"unique_products": {
"cardinality": {
"field": "products.product_id"
}
}
}
}
```
#### Sample response
```json
...
"aggregations" : {
"unique_products" : {
"value" : 7033
}
}
}
```
The cardinality count is approximate.
If you had tens of thousands of products in your store, an accurate cardinality calculation requires loading all the values into a hash set and returning its size. This approach doesn't scale well because it requires more memory and causes high latency.
You can control the trade-off between memory and accuracy with the `precision_threshold` setting. This setting defines the threshold below which counts are expected to be close to accurate. Above this value, counts might become a bit less accurate. The default value of `precision_threshold` is 3,000. The maximum supported value is 40,000.
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"unique_products": {
"cardinality": {
"field": "products.product_id",
"precision_threshold": 10000
}
}
}
}
```
## value_count
The `value_count` metric is a single-value metric aggregation that calculates the number of values that an aggregation is based on.
For example, you can use the `value_count` metric with the `avg` metric to find how many numbers the aggregation uses to calculate an average value.
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"number_of_values": {
"value_count": {
"field": "taxful_total_price"
}
}
}
}
```
#### Sample response
```json
...
"aggregations" : {
"number_of_values" : {
"value" : 4675
}
}
}
```
## stats, extended_stats, matrix_stats
The `stats` metric is a multi-value metric aggregation that returns all basic metrics such as `min`, `max`, `sum`, `avg`, and `value_count` in one aggregation query.
The following example returns the basic stats for the `taxful_total_price` field:
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"stats_taxful_total_price": {
"stats": {
"field": "taxful_total_price"
}
}
}
}
```
#### Sample response
```json
...
"aggregations" : {
"stats_taxful_total_price" : {
"count" : 4675,
"min" : 6.98828125,
"max" : 2250.0,
"avg" : 75.05542864304813,
"sum" : 350884.12890625
}
}
}
```
The `extended_stats` aggregation is an extended version of the `stats` aggregation. Apart from including basic stats, `extended_stats` also returns stats such as `sum_of_squares`, `variance`, and `std_deviation`.
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"extended_stats_taxful_total_price": {
"extended_stats": {
"field": "taxful_total_price"
}
}
}
}
```
#### Sample Response
```json
...
"aggregations" : {
"extended_stats_taxful_total_price" : {
"count" : 4675,
"min" : 6.98828125,
"max" : 2250.0,
"avg" : 75.05542864304813,
"sum" : 350884.12890625,
"sum_of_squares" : 3.9367749294174194E7,
"variance" : 2787.59157113862,
"variance_population" : 2787.59157113862,
"variance_sampling" : 2788.187974983536,
"std_deviation" : 52.79764740155209,
"std_deviation_population" : 52.79764740155209,
"std_deviation_sampling" : 52.80329511482722,
"std_deviation_bounds" : {
"upper" : 180.6507234461523,
"lower" : -30.53986616005605,
"upper_population" : 180.6507234461523,
"lower_population" : -30.53986616005605,
"upper_sampling" : 180.66201887270256,
"lower_sampling" : -30.551161586606312
}
}
}
}
```
The `std_deviation_bounds` object provides a visual variance of the data with an interval of plus/minus two standard deviations from the mean.
To set the standard deviation to a different value, say 3, set `sigma` to 3:
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"extended_stats_taxful_total_price": {
"extended_stats": {
"field": "taxful_total_price",
"sigma": 3
}
}
}
}
```
The `matrix_stats` aggregation generates advanced stats for multiple fields in a matrix form.
The following example returns advanced stats in a matrix form for the `taxful_total_price` and `products.base_price` fields:
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"matrix_stats_taxful_total_price": {
"matrix_stats": {
"fields": ["taxful_total_price", "products.base_price"]
}
}
}
}
```
#### Sample response
```json
...
"aggregations" : {
"matrix_stats_taxful_total_price" : {
"doc_count" : 4675,
"fields" : [
{
"name" : "products.base_price",
"count" : 4675,
"mean" : 34.994239430147196,
"variance" : 360.5035285833703,
"skewness" : 5.530161335032702,
"kurtosis" : 131.16306324042148,
"covariance" : {
"products.base_price" : 360.5035285833703,
"taxful_total_price" : 846.6489362233166
},
"correlation" : {
"products.base_price" : 1.0,
"taxful_total_price" : 0.8444765264325268
}
},
{
"name" : "taxful_total_price",
"count" : 4675,
"mean" : 75.05542864304839,
"variance" : 2788.1879749835402,
"skewness" : 15.812149139924037,
"kurtosis" : 619.1235507385902,
"covariance" : {
"products.base_price" : 846.6489362233166,
"taxful_total_price" : 2788.1879749835402
},
"correlation" : {
"products.base_price" : 0.8444765264325268,
"taxful_total_price" : 1.0
}
}
]
}
}
}
```
Statistic | Description
:--- | :---
`count` | The number of samples measured.
`mean` | The average value of the field measured from the sample.
`variance` | How far the values of the field measured are spread out from its mean value. The larger the variance, the more it's spread from its mean value.
`skewness` | An asymmetric measure of the distribution of the field's values around the mean.
`kurtosis` | A measure of the tail heaviness of a distribution. As the tail becomes lighter, kurtosis decreases. As the tail becomes heavier, kurtosis increases. To learn about kurtosis, see [Wikipedia](https://en.wikipedia.org/wiki/Kurtosis).
`covariance` | A measure of the joint variability between two fields. A positive value means their values move in the same direction and vice versa.
`correlation` | A measure of the strength of the relationship between two fields. The valid values are between [-1, 1]. A value of -1 means that the value is negatively correlated and a value of 1 means that it's positively correlated. A value of 0 means that there's no identifiable relationship between them.
## percentile, percentile_ranks
Percentile is the percentage of the data that's at or below a certain threshold value.
The `percentile` metric is a multi-value metric aggregation that lets you find outliers in your data or figure out the distribution of your data.
Like the `cardinality` metric, the `percentile` metric is also approximate.
The following example calculates the percentile in relation to the `taxful_total_price` field:
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"percentile_taxful_total_price": {
"percentiles": {
"field": "taxful_total_price"
}
}
}
}
```
#### Sample response
```json
...
"aggregations" : {
"percentile_taxful_total_price" : {
"values" : {
"1.0" : 21.984375,
"5.0" : 27.984375,
"25.0" : 44.96875,
"50.0" : 64.22061688311689,
"75.0" : 93.0,
"95.0" : 156.0,
"99.0" : 222.0
}
}
}
}
```
Percentile rank is the percentile of values at or below a threshold grouped by a specified value. For example, if a value is greater than or equal to 80% of the values, it has a percentile rank of 80.
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"percentile_rank_taxful_total_price": {
"percentile_ranks": {
"field": "taxful_total_price",
"values": [
10,
15
]
}
}
}
}
```
#### Sample response
```json
...
"aggregations" : {
"percentile_rank_taxful_total_price" : {
"values" : {
"10.0" : 0.055096056411283456,
"15.0" : 0.0830092961834656
}
}
}
}
```
## geo_bound
The `geo_bound` metric is a multi-value metric aggregation that calculates the bounding box in terms of latitude and longitude around a `geo_point` field.
The following example returns the `geo_bound` metrics for the `geoip.location` field:
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"geo": {
"geo_bounds": {
"field": "geoip.location"
}
}
}
}
```
#### Sample response
```json
"aggregations" : {
"geo" : {
"bounds" : {
"top_left" : {
"lat" : 52.49999997206032,
"lon" : -118.20000001229346
},
"bottom_right" : {
"lat" : 4.599999985657632,
"lon" : 55.299999956041574
}
}
}
}
}
```
## top_hits
The `top_hits` metric is a multi-value metric aggregation that ranks the matching documents based on a relevance score for the field that's being aggregated.
You can specify the following options:
- `from`: The starting position of the hit.
- `size`: The maximum size of hits to return. The default value is 3.
- `sort`: How the matching hits are sorted. By default, the hits are sorted by the relevance score of the aggregation query.
The following example returns the top 5 products in your eCommerce data:
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"top_hits_products": {
"top_hits": {
"size": 5
}
}
}
}
```
#### Sample response
```json
...
"aggregations" : {
"top_hits_products" : {
"hits" : {
"total" : {
"value" : 4675,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "opensearch_dashboards_sample_data_ecommerce",
"_type" : "_doc",
"_id" : "glMlwXcBQVLeQPrkHPtI",
"_score" : 1.0,
"_source" : {
"category" : [
"Women's Accessories",
"Women's Clothing"
],
"currency" : "EUR",
"customer_first_name" : "rania",
"customer_full_name" : "rania Evans",
"customer_gender" : "FEMALE",
"customer_id" : 24,
"customer_last_name" : "Evans",
"customer_phone" : "",
"day_of_week" : "Sunday",
"day_of_week_i" : 6,
"email" : "rania@evans-family.zzz",
"manufacturer" : [
"Tigress Enterprises"
],
"order_date" : "2021-02-28T14:16:48+00:00",
"order_id" : 583581,
"products" : [
{
"base_price" : 10.99,
"discount_percentage" : 0,
"quantity" : 1,
"manufacturer" : "Tigress Enterprises",
"tax_amount" : 0,
"product_id" : 19024,
"category" : "Women's Accessories",
"sku" : "ZO0082400824",
"taxless_price" : 10.99,
"unit_discount_amount" : 0,
"min_price" : 5.17,
"_id" : "sold_product_583581_19024",
"discount_amount" : 0,
"created_on" : "2016-12-25T14:16:48+00:00",
"product_name" : "Snood - white/grey/peach",
"price" : 10.99,
"taxful_price" : 10.99,
"base_unit_price" : 10.99
},
{
"base_price" : 32.99,
"discount_percentage" : 0,
"quantity" : 1,
"manufacturer" : "Tigress Enterprises",
"tax_amount" : 0,
"product_id" : 19260,
"category" : "Women's Clothing",
"sku" : "ZO0071900719",
"taxless_price" : 32.99,
"unit_discount_amount" : 0,
"min_price" : 17.15,
"_id" : "sold_product_583581_19260",
"discount_amount" : 0,
"created_on" : "2016-12-25T14:16:48+00:00",
"product_name" : "Cardigan - grey",
"price" : 32.99,
"taxful_price" : 32.99,
"base_unit_price" : 32.99
}
],
"sku" : [
"ZO0082400824",
"ZO0071900719"
],
"taxful_total_price" : 43.98,
"taxless_total_price" : 43.98,
"total_quantity" : 2,
"total_unique_products" : 2,
"type" : "order",
"user" : "rani",
"geoip" : {
"country_iso_code" : "EG",
"location" : {
"lon" : 31.3,
"lat" : 30.1
},
"region_name" : "Cairo Governorate",
"continent_name" : "Africa",
"city_name" : "Cairo"
},
"event" : {
"dataset" : "sample_ecommerce"
}
}
...
}
]
}
}
}
}
```
## scripted_metric
The `scripted_metric` metric is a multi-value metric aggregation that returns metrics calculated from a specified script.
A script has four stages: the initial stage, the map stage, the combine stage, and the reduce stage.
* `init_script`: (OPTIONAL) Sets the initial state and executes before any collection of documents.
* `map_script`: Checks the value of the `type` field and executes the aggregation on the collected documents.
* `combine_script`: Aggregates the state returned from every shard. The aggregated value is returned to the coordinating node.
* `reduce_script`: Provides access to the variable states; this variable combines the results from the `combine_script` on each shard into an array.
The following example aggregates the different HTTP response types in web log data:
```json
GET opensearch_dashboards_sample_data_logs/_search
{
"size": 0,
"aggregations": {
"responses.counts": {
"scripted_metric": {
"init_script": "state.responses = ['error':0L,'success':0L,'other':0L]",
"map_script": """
def code = doc['response.keyword'].value;
if (code.startsWith('5') || code.startsWith('4')) {
state.responses.error += 1 ;
} else if(code.startsWith('2')) {
state.responses.success += 1;
} else {
state.responses.other += 1;
}
""",
"combine_script": "state.responses",
"reduce_script": """
def counts = ['error': 0L, 'success': 0L, 'other': 0L];
for (responses in states) {
counts.error += responses['error'];
counts.success += responses['success'];
counts.other += responses['other'];
}
return counts;
"""
}
}
}
}
```
#### Sample Response
```json
...
"aggregations" : {
"responses.counts" : {
"value" : {
"other" : 0,
"success" : 12832,
"error" : 1242
}
}
}
}
```

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---
layout: default
title: Popular APIs
nav_order: 96
---
# Popular APIs
This page contains sample requests for popular OpenSearch operations.
---
#### Table of contents
1. TOC
{:toc}
---
## Create index with non-default settings
```json
PUT my-logs
{
"settings": {
"number_of_shards": 4,
"number_of_replicas": 2
},
"mappings": {
"properties": {
"title": {
"type": "text"
},
"year": {
"type": "integer"
}
}
}
}
```
## Index a document with a random ID
```json
POST my-logs/_doc
{
"title": "Your Name",
"year": "2016"
}
```
## Index a document with a specific ID
```json
PUT my-logs/_doc/1
{
"title": "Weathering with You",
"year": "2019"
}
```
## Index several documents at once
The blank line at the end of the request body is required. If you omit the `_id` field, OpenSearch generates a random ID.
```json
POST _bulk
{ "index": { "_index": "my-logs", "_id": "2" } }
{ "title": "The Garden of Words", "year": 2013 }
{ "index" : { "_index": "my-logs", "_id" : "3" } }
{ "title": "5 Centimeters Per Second", "year": 2007 }
```
## List all indices
```
GET _cat/indices?v&expand_wildcards=all
```
## Open or close all indices that match a pattern
```
POST my-logs*/_open
POST my-logs*/_close
```
## Delete all indices that match a pattern
```
DELETE my-logs*
```
## Create an index alias
This request creates the alias `my-logs-today` for the index `my-logs-2019-11-13`.
```
PUT my-logs-2019-11-13/_alias/my-logs-today
```
## List all aliases
```
GET _cat/aliases?v
```
## Search an index or all indices that match a pattern
```
GET my-logs/_search?q=test
GET my-logs*/_search?q=test
```
## Get cluster settings, including defaults
```
GET _cluster/settings?include_defaults=true
```
## Change disk watermarks (or other cluster settings)
```json
PUT _cluster/settings
{
"transient": {
"cluster.routing.allocation.disk.watermark.low": "80%",
"cluster.routing.allocation.disk.watermark.high": "85%"
}
}
```
## Get cluster health
```
GET _cluster/health
```
## List nodes in the cluster
```
GET _cat/nodes?v
```
## Get node statistics
```
GET _nodes/stats
```
## Get snapshots in a repository
```
GET _snapshot/my-repository/_all
```
## Take a snapshot
```
PUT _snapshot/my-repository/my-snapshot
```
## Restore a snapshot
```json
POST _snapshot/my-repository/my-snapshot/_restore
{
"indices": "-.opendistro_security",
"include_global_state": false
}
```

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---
layout: default
title: Boolean queries
parent: Query DSL
nav_order: 45
redirect_from: /docs/opensearch/bool/
---
# Boolean queries
The `bool` query lets you combine multiple search queries with boolean logic. You can use boolean logic between queries to either narrow or broaden your search results.
The `bool` query is a go-to query because it allows you to construct an advanced query by chaining together several simple ones.
Use the following clauses (subqueries) within the `bool` query:
Clause | Behavior
:--- | :---
`must` | The results must match the queries in this clause. If you have multiple queries, every single one must match. Acts as an `and` operator.
`must_not` | This is the anti-must clause. All matches are excluded from the results. Acts as a `not` operator.
`should` | The results should, but don't have to, match the queries. Each matching `should` clause increases the relevancy score. As an option, you can require one or more queries to match the value of the `minimum_number_should_match` parameter (default is 1).
`filter` | Filters reduce your dataset before applying the queries. A query within a filter clause is a yes-no option, where if a document matches the query it's included in the results. Otherwise, it's not. Filter queries do not affect the relevancy score that the results are sorted by. The results of a filter query are generally cached so they tend to run faster. Use the filter query to filter the results based on exact matches, ranges, dates, numbers, and so on.
The structure of a `bool` query is as follows:
```json
GET _search
{
"query": {
"bool": {
"must": [
{}
],
"must_not": [
{}
],
"should": [
{}
],
"filter": {}
}
}
}
```
For example, assume you have the complete works of Shakespeare indexed in an OpenSearch cluster. You want to construct a single query that meets the following requirements:
1. The `text_entry` field must contain the word `love` and should contain either `life` or `grace`.
2. The `speaker` field must not contain `ROMEO`.
3. Filter these results to the play `Romeo and Juliet` without affecting the relevancy score.
Use the following query:
```json
GET shakespeare/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"text_entry": "love"
}
}
],
"should": [
{
"match": {
"text_entry": "life"
}
},
{
"match": {
"text_entry": "grace"
}
}
],
"minimum_should_match": 1,
"must_not": [
{
"match": {
"speaker": "ROMEO"
}
}
],
"filter": {
"term": {
"play_name": "Romeo and Juliet"
}
}
}
}
}
```
#### Sample output
```json
{
"took": 12,
"timed_out": false,
"_shards": {
"total": 4,
"successful": 4,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 1,
"relation": "eq"
},
"max_score": 11.356054,
"hits": [
{
"_index": "shakespeare",
"_type": "_doc",
"_id": "88020",
"_score": 11.356054,
"_source": {
"type": "line",
"line_id": 88021,
"play_name": "Romeo and Juliet",
"speech_number": 19,
"line_number": "4.5.61",
"speaker": "PARIS",
"text_entry": "O love! O life! not life, but love in death!"
}
}
]
}
}
```
If you want to identify which of these clauses actually caused the matching results, name each query with the `_name` parameter.
To add the `_name` parameter, change the field name in the `match` query to an object:
```json
GET shakespeare/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"text_entry": {
"query": "love",
"_name": "love-must"
}
}
}
],
"should": [
{
"match": {
"text_entry": {
"query": "life",
"_name": "life-should"
}
}
},
{
"match": {
"text_entry": {
"query": "grace",
"_name": "grace-should"
}
}
}
],
"minimum_should_match": 1,
"must_not": [
{
"match": {
"speaker": {
"query": "ROMEO",
"_name": "ROMEO-must-not"
}
}
}
],
"filter": {
"term": {
"play_name": "Romeo and Juliet"
}
}
}
}
}
```
OpenSearch returns a `matched_queries` array that lists the queries that matched these results:
```json
"matched_queries": [
"love-must",
"life-should"
]
```
If you remove the queries not in this list, you will still see the exact same result.
By examining which `should` clause matched, you can better understand the relevancy score of the results.
You can also construct complex boolean expressions by nesting `bool` queries.
For example, to find a `text_entry` field that matches (`love` OR `hate`) AND (`life` OR `grace`) in the play `Romeo and Juliet`:
```json
GET shakespeare/_search
{
"query": {
"bool": {
"must": [
{
"bool": {
"should": [
{
"match": {
"text_entry": "love"
}
},
{
"match": {
"text": "hate"
}
}
]
}
},
{
"bool": {
"should": [
{
"match": {
"text_entry": "life"
}
},
{
"match": {
"text": "grace"
}
}
]
}
}
],
"filter": {
"term": {
"play_name": "Romeo and Juliet"
}
}
}
}
}
```
#### Sample output
```json
{
"took": 10,
"timed_out": false,
"_shards": {
"total": 2,
"successful": 2,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 11.37006,
"hits": [
{
"_index": "shakespeare",
"_type": "doc",
"_id": "88020",
"_score": 11.37006,
"_source": {
"type": "line",
"line_id": 88021,
"play_name": "Romeo and Juliet",
"speech_number": 19,
"line_number": "4.5.61",
"speaker": "PARIS",
"text_entry": "O love! O life! not life, but love in death!"
}
}
]
}
}
```

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@ -1,438 +0,0 @@
---
layout: default
title: Full-text queries
parent: Query DSL
nav_order: 40
redirect_from:
- /docs/opensearch/full-text/
- /opensearch/full-text/
---
# Full-text queries
This page lists all full-text query types and common options. Given the sheer number of options and subtle behaviors, the best method of ensuring useful search results is to test different queries against representative indices and verify the output.
---
#### Table of contents
1. TOC
{:toc}
---
## Match
Creates a [boolean query](https://lucene.apache.org/core/8_4_0/core/org/apache/lucene/search/BooleanQuery.html) that returns results if the search term is present in the field.
The most basic form of the query provides only a field (`title`) and a term (`wind`):
```json
GET _search
{
"query": {
"match": {
"title": "wind"
}
}
}
```
For an example that uses [curl](https://curl.haxx.se/), try:
```bash
curl --insecure -XGET -u 'admin:admin' https://<host>:<port>/<index>/_search \
-H "content-type: application/json" \
-d '{
"query": {
"match": {
"title": "wind"
}
}
}'
```
The query accepts the following options. For descriptions of each, see [Options](#options).
```json
GET _search
{
"query": {
"match": {
"title": {
"query": "wind",
"fuzziness": "AUTO",
"fuzzy_transpositions": true,
"operator": "or",
"minimum_should_match": 1,
"analyzer": "standard",
"zero_terms_query": "none",
"lenient": false,
"cutoff_frequency": 0.01,
"prefix_length": 0,
"max_expansions": 50,
"boost": 1
}
}
}
}
```
## Multi match
Similar to [match](#match), but searches multiple fields.
The `^` lets you "boost" certain fields. Boosts are multipliers that weigh matches in one field more heavily than matches in other fields. In the following example, a match for "wind" in the title field influences `_score` four times as much as a match in the plot field. The result is that films like *The Wind Rises* and *Gone with the Wind* are near the top of the search results, and films like *Twister* and *Sharknado*, which presumably have "wind" in their plot summaries, are near the bottom.
```json
GET _search
{
"query": {
"multi_match": {
"query": "wind",
"fields": ["title^4", "plot"]
}
}
}
```
The query accepts the following options. For descriptions of each, see [Options](#options).
```json
GET _search
{
"query": {
"multi_match": {
"query": "wind",
"fields": ["title^4", "description"],
"type": "most_fields",
"operator": "and",
"minimum_should_match": 3,
"tie_breaker": 0.0,
"analyzer": "standard",
"boost": 1,
"fuzziness": "AUTO",
"fuzzy_transpositions": true,
"lenient": false,
"prefix_length": 0,
"max_expansions": 50,
"auto_generate_synonyms_phrase_query": true,
"cutoff_frequency": 0.01,
"zero_terms_query": "none"
}
}
}
```
## Match boolean prefix
Similar to [match](#match), but creates a [prefix query](https://lucene.apache.org/core/8_4_0/core/org/apache/lucene/search/PrefixQuery.html) out of the last term in the query string.
```json
GET _search
{
"query": {
"match_bool_prefix": {
"title": "rises wi"
}
}
}
```
The query accepts the following options. For descriptions of each, see [Options](#options).
```json
GET _search
{
"query": {
"match_bool_prefix": {
"title": {
"query": "rises wi",
"fuzziness": "AUTO",
"fuzzy_transpositions": true,
"max_expansions": 50,
"prefix_length": 0,
"operator": "or",
"minimum_should_match": 2,
"analyzer": "standard"
}
}
}
}
```
## Match phrase
Creates a [phrase query](https://lucene.apache.org/core/8_4_0/core/org/apache/lucene/search/PhraseQuery.html) that matches a sequence of terms.
```json
GET _search
{
"query": {
"match_phrase": {
"title": "the wind rises"
}
}
}
```
The query accepts the following options. For descriptions of each, see [Options](#options).
```json
GET _search
{
"query": {
"match_phrase": {
"title": {
"query": "wind rises the",
"slop": 3,
"analyzer": "standard",
"zero_terms_query": "none"
}
}
}
}
```
## Match phrase prefix
Similar to [match phrase](#match-phrase), but creates a [prefix query](https://lucene.apache.org/core/8_4_0/core/org/apache/lucene/search/PrefixQuery.html) out of the last term in the query string.
```json
GET _search
{
"query": {
"match_phrase_prefix": {
"title": "the wind ri"
}
}
}
```
The query accepts the following options. For descriptions of each, see [Options](#options).
```json
GET _search
{
"query": {
"match_phrase_prefix": {
"title": {
"query": "the wind ri",
"analyzer": "standard",
"max_expansions": 50,
"slop": 3
}
}
}
}
```
## Common terms
The common terms query separates the query string into high- and low-frequency terms based on number of occurrences on the shard. Low-frequency terms are weighed more heavily in the results, and high-frequency terms are considered only for documents that already matched one or more low-frequency terms. In that sense, you can think of this query as having a built-in, ever-changing list of stop words.
```json
GET _search
{
"query": {
"common": {
"title": {
"query": "the wind rises"
}
}
}
}
```
The query accepts the following options. For descriptions of each, see [Options](#options).
```json
GET _search
{
"query": {
"common": {
"title": {
"query": "the wind rises",
"cutoff_frequency": 0.002,
"low_freq_operator": "or",
"boost": 1,
"analyzer": "standard",
"minimum_should_match": {
"low_freq" : 2,
"high_freq" : 3
}
}
}
}
}
```
## Query string
The query string query splits text based on operators and analyzes each individually.
If you search using the HTTP request parameters (i.e. `_search?q=wind`), OpenSearch creates a query string query.
{: .note }
```json
GET _search
{
"query": {
"query_string": {
"query": "the wind AND (rises OR rising)"
}
}
}
```
The query accepts the following options. For descriptions of each, see [Options](#options).
```json
GET _search
{
"query": {
"query_string": {
"query": "the wind AND (rises OR rising)",
"default_field": "title",
"type": "best_fields",
"fuzziness": "AUTO",
"fuzzy_transpositions": true,
"fuzzy_max_expansions": 50,
"fuzzy_prefix_length": 0,
"minimum_should_match": 1,
"default_operator": "or",
"analyzer": "standard",
"lenient": false,
"boost": 1,
"allow_leading_wildcard": true,
"enable_position_increments": true,
"phrase_slop": 3,
"max_determinized_states": 10000,
"time_zone": "-08:00",
"quote_field_suffix": "",
"quote_analyzer": "standard",
"analyze_wildcard": false,
"auto_generate_synonyms_phrase_query": true
}
}
}
```
## Simple query string
The simple query string query is like the query string query, but it lets advanced users specify many arguments directly in the query string. The query discards any invalid portions of the query string.
```json
GET _search
{
"query": {
"simple_query_string": {
"query": "\"rises wind the\"~4 | *ising~2",
"fields": ["title"]
}
}
}
```
Special character | Behavior
:--- | :---
`+` | Acts as the `and` operator.
`|` | Acts as the `or` operator.
`*` | Acts as a wildcard.
`""` | Wraps several terms into a phrase.
`()` | Wraps a clause for precedence.
`~n` | When used after a term (e.g. `wnid~3`), sets `fuzziness`. When used after a phrase, sets `slop`. See [Options](#options).
`-` | Negates the term.
The query accepts the following options. For descriptions of each, see [Options](#options).
```json
GET _search
{
"query": {
"simple_query_string": {
"query": "\"rises wind the\"~4 | *ising~2",
"fields": ["title"],
"flags": "ALL",
"fuzzy_transpositions": true,
"fuzzy_max_expansions": 50,
"fuzzy_prefix_length": 0,
"minimum_should_match": 1,
"default_operator": "or",
"analyzer": "standard",
"lenient": false,
"quote_field_suffix": "",
"analyze_wildcard": false,
"auto_generate_synonyms_phrase_query": true
}
}
}
```
## Match all
Matches all documents. Can be useful for testing.
```json
GET _search
{
"query": {
"match_all": {}
}
}
```
## Match none
Matches no documents. Rarely useful.
```json
GET _search
{
"query": {
"match_none": {}
}
}
```
## Options
Option | Valid values | Description
:--- | :--- | :---
`allow_leading_wildcard` | Boolean | Whether `*` and `?` are allowed as the first character of a search term. The default is true.
`analyze_wildcard` | Boolean | Whether OpenSearch should attempt to analyze wildcard terms. Some analyzers do a poor job at this task, so the default is false.
`analyzer` | `standard, simple, whitespace, stop, keyword, pattern, <language>, fingerprint` | The analyzer you want to use for the query. Different analyzers have different character filters, tokenizers, and token filters. The `stop` analyzer, for example, removes stop words (e.g. "an," "but," "this") from the query string.
`auto_generate_synonyms_phrase_query` | Boolean | A value of true (default) automatically generates [phrase queries](https://lucene.apache.org/core/8_4_0/core/org/apache/lucene/search/PhraseQuery.html) for multi-term synonyms. For example, if you have the synonym `"ba, batting average"` and search for "ba," OpenSearch searches for `ba OR "batting average"` (if this option is true) or `ba OR (batting AND average)` (if this option is false).
`boost` | Floating-point | Boosts the clause by the given multiplier. Useful for weighing clauses in compound queries. The default is 1.0.
`cutoff_frequency` | Between `0.0` and `1.0` or a positive integer | This value lets you define high and low frequency terms based on number of occurrences in the index. Numbers between 0 and 1 are treated as a percentage. For example, 0.10 is 10%. This value means that if a word occurs within the search field in more than 10% of the documents on the shard, OpenSearch considers the word "high frequency" and deemphasizes it when calculating search score.<br /><br />Because this setting is *per shard*, testing its impact on search results can be challenging unless a cluster has many documents.
`enable_position_increments` | Boolean | When true, result queries are aware of position increments. This setting is useful when the removal of stop words leaves an unwanted "gap" between terms. The default is true.
`fields` | String array | The list of fields to search (e.g. `"fields": ["title^4", "description"]`). If unspecified, defaults to the `index.query.default_field` setting, which defaults to `["*"]`.
`flags` | String | A `|`-delimited string of [flags](#simple-query-string) to enable (e.g. `AND|OR|NOT`). The default is `ALL`.
`fuzziness` | `AUTO`, `0`, or a positive integer | The number of character edits (insert, delete, substitute) that it takes to change one word to another when determining whether a term matched a value. For example, the distance between `wined` and `wind` is 1. The default, `AUTO`, chooses a value based on the length of each term and is a good choice for most use cases.
`fuzzy_transpositions` | Boolean | Setting `fuzzy_transpositions` to true (default) adds swaps of adjacent characters to the insert, delete, and substitute operations of the `fuzziness` option. For example, the distance between `wind` and `wnid` is 1 if `fuzzy_transpositions` is true (swap "n" and "i") and 2 if it is false (delete "n", insert "n"). <br /><br />If `fuzzy_transpositions` is false, `rewind` and `wnid` have the same distance (2) from `wind`, despite the more human-centric opinion that `wnid` is an obvious typo. The default is a good choice for most use cases.
`lenient` | Boolean | Setting `lenient` to true lets you ignore data type mismatches between the query and the document field. For example, a query string of "8.2" could match a field of type `float`. The default is false.
`low_freq_operator` | `and, or` | The operator for low-frequency terms. The default is `or`. See [Common terms](#common-terms) queries and `operator` in this table.
`max_determinized_states` | Positive integer | The maximum number of "[states](https://lucene.apache.org/core/8_4_0/core/org/apache/lucene/util/automaton/Operations.html#DEFAULT_MAX_DETERMINIZED_STATES)" (a measure of complexity) that Lucene can create for query strings that contain regular expressions (e.g. `"query": "/wind.+?/"`). Larger numbers allow for queries that use more memory. The default is 10,000.
`max_expansions` | Positive integer | Fuzzy queries "expand to" a number of matching terms that are within the distance specified in `fuzziness`. Then OpenSearch tries to match those terms against its indices. `max_expansions` specifies the maximum number of terms that the fuzzy query expands to. The default is 50.
`minimum_should_match` | Positive or negative integer, positive or negative percentage, combination | If the query string contains multiple search terms and you used the `or` operator, the number of terms that need to match for the document to be considered a match. For example, if `minimum_should_match` is 2, "wind often rising" does not match "The Wind Rises." If `minimum_should_match` is 1, it matches. This option also has `low_freq` and `high_freq` properties for [Common terms](#common-terms) queries.
`operator` | `or, and` | If the query string contains multiple search terms, whether all terms need to match (`and`) or only one term needs to match (`or`) for a document to be considered a match.
`phrase_slop` | `0` (default) or a positive integer | See `slop`.
`prefix_length` | `0` (default) or a positive integer | The number of leading characters that are not considered in fuzziness.
`quote_field_suffix` | String | This option lets you search different fields depending on whether terms are wrapped in quotes. For example, if `quote_field_suffix` is `".exact"` and you search for `"lightly"` (in quotes) in the `title` field, OpenSearch searches the `title.exact` field. This second field might use a different type (e.g. `keyword` rather than `text`) or a different analyzer. The default is null.
`rewrite` | `constant_score, scoring_boolean, constant_score_boolean, top_terms_N, top_terms_boost_N, top_terms_blended_freqs_N` | Determines how OpenSearch rewrites and scores multi-term queries. The default is `constant_score`.
`slop` | `0` (default) or a positive integer | Controls the degree to which words in a query can be misordered and still be considered a match. From the [Lucene documentation](https://lucene.apache.org/core/8_4_0/core/org/apache/lucene/search/PhraseQuery.html#getSlop--): "The number of other words permitted between words in query phrase. For example, to switch the order of two words requires two moves (the first move places the words atop one another), so to permit re-orderings of phrases, the slop must be at least two. A value of zero requires an exact match."
`tie_breaker` | `0.0` (default) to `1.0` | Changes the way OpenSearch scores searches. For example, a `type` of `best_fields` typically uses the highest score from any one field. If you specify a `tie_breaker` value between 0.0 and 1.0, the score changes to highest score + `tie_breaker` * score for all other matching fields. If you specify a value of 1.0, OpenSearch adds together the scores for all matching fields (effectively defeating the purpose of `best_fields`).
`time_zone` | UTC offset | The time zone to use (e.g. `-08:00`) if the query string contains a date range (e.g. `"query": "wind rises release_date[2012-01-01 TO 2014-01-01]"`). The default is `UTC`.
`type` | `best_fields, most_fields, cross-fields, phrase, phrase_prefix` | Determines how OpenSearch executes the query and scores the results. The default is `best_fields`.
`zero_terms_query` | `none, all` | If the analyzer removes all terms from a query string, whether to match no documents (default) or all documents. For example, the `stop` analyzer removes all terms from the string "an but this."

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---
layout: default
title: Query DSL
nav_order: 27
has_children: true
redirect_from:
- /opensearch/query-dsl/
---
# Query DSL
While you can use HTTP request parameters to perform simple searches, you can also use the OpenSearch query domain-specific language (DSL), which provides a wider range of search options. The query DSL uses the HTTP request body, so you can more easily customize your queries to get the exact results that you want.
For example, the following request performs a simple search to search for a `speaker` field that has a value of `queen`.
**Sample request**
```json
GET _search?q=speaker:queen
```
**Sample response**
```
{
"took": 87,
"timed_out": false,
"_shards": {
"total": 68,
"successful": 68,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 4080,
"relation": "eq"
},
"max_score": 4.4368687,
"hits": [
{
"_index": "new_shakespeare",
"_type": "_doc",
"_id": "28559",
"_score": 4.4368687,
"_source": {
"type": "line",
"line_id": 28560,
"play_name": "Cymbeline",
"speech_number": 20,
"line_number": "1.1.81",
"speaker": "QUEEN",
"text_entry": "No, be assured you shall not find me, daughter,"
}
}
```
With query DSL, however, you can include an HTTP request body to look for results more tailored to your needs. The following example shows how to search for `speaker` and `text_entry` fields that have a value of `QUEEN`.
**Sample request**
```json
{
"query": {
"multi_match": {
"query": "QUEEN",
"fields": ["speaker", "text_entry"]
}
}
}
```
**Sample Response**
```json
{
"took": 39,
"timed_out": false,
"_shards": {
"total": 68,
"successful": 68,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 5837,
"relation": "eq"
},
"max_score": 7.8623476,
"hits": [
{
"_index": "new_shakespeare",
"_type": "_doc",
"_id": "100763",
"_score": 7.8623476,
"_source": {
"type": "line",
"line_id": 100764,
"play_name": "Troilus and Cressida",
"speech_number": 43,
"line_number": "3.1.68",
"speaker": "PANDARUS",
"text_entry": "Sweet queen, sweet queen! thats a sweet queen, i faith."
}
},
{
"_index": "shakespeare",
"_type": "_doc",
"_id": "28559",
"_score": 5.8923807,
"_source": {
"type": "line",
"line_id": 28560,
"play_name": "Cymbeline",
"speech_number": 20,
"line_number": "1.1.81",
"speaker": "QUEEN",
"text_entry": "No, be assured you shall not find me, daughter,"
}
}
]
}
}
```
The OpenSearch query DSL comes in three varieties: term-level queries, full-text queries, and boolean queries. You can even perform more complicated searches by using different elements from each variety to find whatever data you need.

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---
layout: default
title: Term-level queries
parent: Query DSL
nav_order: 30
redirect_from: /docs/opensearch/term/
---
# Term-level queries
OpenSearch supports two types of queries when you search for data: term-level queries and full-text queries.
The following table describes the differences between them:
| | Term-level queries | Full-text queries
:--- | :--- | :---
*Description* | Term-level queries answer which documents match a query. | Full-text queries answer how well the documents match a query.
*Analyzer* | The search term isn't analyzed. This means that the term query searches for your search term as it is. | The search term is analyzed by the same analyzer that was used for the specific field of the document at the time it was indexed. This means that your search term goes through the same analysis process that the document's field did.
*Relevance* | Term-level queries simply return documents that match without sorting them based on the relevance score. They still calculate the relevance score, but this score is the same for all the documents that are returned. | Full-text queries calculate a relevance score for each match and sort the results by decreasing order of relevance.
*Use Case* | Use term-level queries when you want to match exact values such as numbers, dates, tags, and so on, and don't need the matches to be sorted by relevance. | Use full-text queries to match text fields and sort by relevance after taking into account factors like casing and stemming variants.
OpenSearch uses a probabilistic ranking framework called Okapi BM25 to calculate relevance scores. To learn more about Okapi BM25, see [Wikipedia](https://en.wikipedia.org/wiki/Okapi_BM25).
{: .note }
Assume that you have the complete works of Shakespeare indexed in an OpenSearch cluster. We use a term-level query to search for the phrase "To be, or not to be" in the `text_entry` field:
```json
GET shakespeare/_search
{
"query": {
"term": {
"text_entry": "To be, or not to be"
}
}
}
```
#### Sample response
```json
{
"took" : 3,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 0,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
}
}
```
We don’t get back any matches (`hits`). This is because the term “To be, or not to be” is searched literally in the inverted index, where only the analyzed values of the text fields are stored. Term-level queries aren't suited for searching on analyzed text fields because they often yield unexpected results. When working with text data, use term-level queries only for fields mapped as keyword only.
Using a full-text query:
```json
GET shakespeare/_search
{
"query": {
"match": {
"text_entry": "To be, or not to be"
}
}
}
```
The search query “To be, or not to be” is analyzed and tokenized into an array of tokens just like the `text_entry` field of the documents. The full-text query performs an intersection of tokens between our search query and the `text_entry` fields for all the documents, and then sorts the results by relevance scores:
#### Sample response
```json
{
"took" : 19,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 10000,
"relation" : "gte"
},
"max_score" : 17.419369,
"hits" : [
{
"_index" : "shakespeare",
"_type" : "_doc",
"_id" : "34229",
"_score" : 17.419369,
"_source" : {
"type" : "line",
"line_id" : 34230,
"play_name" : "Hamlet",
"speech_number" : 19,
"line_number" : "3.1.64",
"speaker" : "HAMLET",
"text_entry" : "To be, or not to be: that is the question:"
}
},
{
"_index" : "shakespeare",
"_type" : "_doc",
"_id" : "109930",
"_score" : 14.883024,
"_source" : {
"type" : "line",
"line_id" : 109931,
"play_name" : "A Winters Tale",
"speech_number" : 23,
"line_number" : "4.4.153",
"speaker" : "PERDITA",
"text_entry" : "Not like a corse; or if, not to be buried,"
}
},
{
"_index" : "shakespeare",
"_type" : "_doc",
"_id" : "103117",
"_score" : 14.782743,
"_source" : {
"type" : "line",
"line_id" : 103118,
"play_name" : "Twelfth Night",
"speech_number" : 53,
"line_number" : "1.3.95",
"speaker" : "SIR ANDREW",
"text_entry" : "will not be seen; or if she be, its four to one"
}
}
]
}
}
...
```
For a list of all full-text queries, see [Full-text queries]({{site.url}}{{site.baseurl}}/opensearch/full-text/).
If you want to query for an exact term like “HAMLET” in the speaker field and don't need the results to be sorted by relevance scores, a term-level query is more efficient:
```json
GET shakespeare/_search
{
"query": {
"term": {
"speaker": "HAMLET"
}
}
}
```
#### Sample response
```json
{
"took" : 5,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1582,
"relation" : "eq"
},
"max_score" : 4.2540946,
"hits" : [
{
"_index" : "shakespeare",
"_type" : "_doc",
"_id" : "32700",
"_score" : 4.2540946,
"_source" : {
"type" : "line",
"line_id" : 32701,
"play_name" : "Hamlet",
"speech_number" : 9,
"line_number" : "1.2.66",
"speaker" : "HAMLET",
"text_entry" : "[Aside] A little more than kin, and less than kind."
}
},
{
"_index" : "shakespeare",
"_type" : "_doc",
"_id" : "32702",
"_score" : 4.2540946,
"_source" : {
"type" : "line",
"line_id" : 32703,
"play_name" : "Hamlet",
"speech_number" : 11,
"line_number" : "1.2.68",
"speaker" : "HAMLET",
"text_entry" : "Not so, my lord; I am too much i' the sun."
}
},
{
"_index" : "shakespeare",
"_type" : "_doc",
"_id" : "32709",
"_score" : 4.2540946,
"_source" : {
"type" : "line",
"line_id" : 32710,
"play_name" : "Hamlet",
"speech_number" : 13,
"line_number" : "1.2.75",
"speaker" : "HAMLET",
"text_entry" : "Ay, madam, it is common."
}
}
]
}
}
...
```
The term-level queries are exact matches. So, if you search for “Hamlet”, you don’t get back any matches, because “HAMLET” is a keyword field and is stored in OpenSearch literally and not in an analyzed form.
The search query “HAMLET” is also searched literally. So, to get a match on this field, we need to enter the exact same characters.
---
## Term
Use the `term` query to search for an exact term in a field.
```json
GET shakespeare/_search
{
"query": {
"term": {
"line_id": {
"value": "61809"
}
}
}
}
```
## Terms
Use the `terms` query to search for multiple terms in the same field.
```json
GET shakespeare/_search
{
"query": {
"terms": {
"line_id": [
"61809",
"61810"
]
}
}
}
```
You get back documents that match any of the terms.
## IDs
Use the `ids` query to search for one or more document ID values.
```json
GET shakespeare/_search
{
"query": {
"ids": {
"values": [
34229,
91296
]
}
}
}
```
## Range
Use the `range` query to search for a range of values in a field.
To search for documents where the `line_id` value is >= 10 and <= 20:
```json
GET shakespeare/_search
{
"query": {
"range": {
"line_id": {
"gte": 10,
"lte": 20
}
}
}
}
```
Parameter | Behavior
:--- | :---
`gte` | Greater than or equal to.
`gt` | Greater than.
`lte` | Less than or equal to.
`lt` | Less than.
Assume that you have a `products` index and you want to find all the products that were added in the year 2019:
```json
GET products/_search
{
"query": {
"range": {
"created": {
"gte": "2019/01/01",
"lte": "2019/12/31"
}
}
}
}
```
Specify relative dates by using basic math expressions.
To subtract 1 year and 1 day from the specified date:
```json
GET products/_search
{
"query": {
"range": {
"created": {
"gte": "2019/01/01||-1y-1d"
}
}
}
}
```
The first date that we specify is the anchor date or the starting point for the date math. Add two trailing pipe symbols. You could then add one day (`+1d`) or subtract two weeks (`-2w`). This math expression is relative to the anchor date that you specify.
You could also round off dates by adding a forward slash to the date or time unit.
To find products added in the last year and rounded off by month:
```json
GET products/_search
{
"query": {
"range": {
"created": {
"gte": "now-1y/M"
}
}
}
}
```
The keyword `now` refers to the current date and time.
## Prefix
Use the `prefix` query to search for terms that begin with a specific prefix.
```json
GET shakespeare/_search
{
"query": {
"prefix": {
"speaker": "KING"
}
}
}
```
## Exists
Use the `exists` query to search for documents that contain a specific field.
```json
GET shakespeare/_search
{
"query": {
"exists": {
"field": "speaker"
}
}
}
```
## Wildcards
Use wildcard queries to search for terms that match a wildcard pattern.
Feature | Behavior
:--- | :---
`*` | Specifies all valid values.
`?` | Specifies a single valid value.
To search for terms that start with `H` and end with `Y`:
```json
GET shakespeare/_search
{
"query": {
"wildcard": {
"speaker": {
"value": "H*Y"
}
}
}
}
```
If we change `*` to `?`, we get no matches, because `?` refers to a single character.
Wildcard queries tend to be slow because they need to iterate over a lot of terms. Avoid placing wildcard characters at the beginning of a query because it could be a very expensive operation in terms of both resources and time.
## Regex
Use the `regex` query to search for terms that match a regular expression.
This regular expression matches any single uppercase or lowercase letter:
```json
GET shakespeare/_search
{
"query": {
"regexp": {
"play_name": "H[a-zA-Z]+mlet"
}
}
}
```
Regular expressions are applied to the terms in the field and not the entire value of the field.
The efficiency of your regular expression depends a lot on the patterns you write. Make sure that you write `regex` queries with either a prefix or suffix to improve performance.

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---
layout: default
title: Reindex data
nav_order: 16
---
# Reindex data
After creating an index, you might need to make an extensive change such as adding a new field to every document or combining multiple indices to form a new one. Rather than deleting your index, making the change offline, and then indexing your data all over again, you can use the `reindex` operation.
With the `reindex` operation, you can copy all or a subset of documents that you select through a query to another index. Reindex is a `POST` operation. In its most basic form, you specify a source index and a destination index.
Reindexing can be an expensive operation depending on the size of your source index. We recommend you disable replicas in your destination index by setting `number_of_replicas` to `0` and re-enable them once the reindex process is complete.
{: .note }
---
#### Table of contents
1. TOC
{:toc}
---
## Reindex all documents
You can copy all documents from one index to another.
You first need to create a destination index with your desired field mappings and settings or you can copy the ones from your source index:
```json
PUT destination
{
"mappings":{
"Add in your desired mappings"
},
"settings":{
"Add in your desired settings"
}
}
```
This `reindex` command copies all the documents from a source index to a destination index:
```json
POST _reindex
{
"source":{
"index":"source"
},
"dest":{
"index":"destination"
}
}
```
If the destination index is not already created, the `reindex` operation creates a new destination index with default configurations.
## Reindex from a remote cluster
You can copy documents from an index in a remote cluster. Use the `remote` option to specify the remote hostname and the required login credentials.
This command reaches out to a remote cluster, logs in with the username and password, and copies all the documents from the source index in that remote cluster to the destination index in your local cluster:
```json
POST _reindex
{
"source":{
"remote":{
"host":"https://<REST_endpoint_of_remote_cluster>:9200",
"username":"YOUR_USERNAME",
"password":"YOUR_PASSWORD"
}
},
"dest":{
"index":"destination"
}
}
```
You can specify the following options:
Options | Valid values | Description | Required
:--- | :--- | :---
`host` | String | The REST endpoint of the remote cluster. | Yes
`username` | String | The username to log into the remote cluster. | No
`password` | String | The password to log into the remote cluster. | No
`socket_timeout` | Time Unit | The wait time for socket reads (default 30s). | No
`connect_timeout` | Time Unit | The wait time for remote connection timeouts (default 30s). | No
## Reindex a subset of documents
You can copy a specific set of documents that match a search query.
This command copies only a subset of documents matched by a query operation to the destination index:
```json
POST _reindex
{
"source":{
"index":"source",
"query": {
"match": {
"field_name": "text"
}
}
},
"dest":{
"index":"destination"
}
}
```
For a list of all query operations, see [Full-text queries]({{site.url}}{{site.baseurl}}/opensearch/query-dsl/full-text/).
## Combine one or more indices
You can combine documents from one or more indices by adding the source indices as a list.
This command copies all documents from two source indices to one destination index:
```json
POST _reindex
{
"source":{
"index":[
"source_1",
"source_2"
]
},
"dest":{
"index":"destination"
}
}
```
Make sure the number of shards for your source and destination indices are the same.
## Reindex only unique documents
You can copy only documents missing from a destination index by setting the `op_type` option to `create`.
In this case, if a document with the same ID already exists, the operation ignores the one from the source index.
To ignore all version conflicts of documents, set the `conflicts` option to `proceed`.
```json
POST _reindex
{
"conflicts":"proceed",
"source":{
"index":"source"
},
"dest":{
"index":"destination",
"op_type":"create"
}
}
```
## Reindex sorted documents
You can copy certain documents after sorting specific fields in the document.
This command copies the last 10 documents based on the `timestamp` field:
```json
POST _reindex
{
"size":10,
"source":{
"index":"source",
"sort":{
"timestamp":"desc"
}
},
"dest":{
"index":"destination"
}
}
```
## Transform documents during reindexing
You can transform your data during the reindexing process using the `script` option.
We recommend Painless for scripting in OpenSearch.
This command runs the source index through a Painless script that increments a `number` field inside an `account` object before copying it to the destination index:
```json
POST _reindex
{
"source":{
"index":"source"
},
"dest":{
"index":"destination"
},
"script":{
"lang":"painless",
"source":"ctx._account.number++"
}
}
```
You can also specify an ingest pipeline to transform your data during the reindexing process.
You would first have to create a pipeline with `processors` defined. You have a number of different `processors` available to use in your ingest pipeline.
Here's a sample ingest pipeline that defines a `split` processor that splits a `text` field based on a `space` separator and stores it in a new `word` field. The `script` processor is a Painless script that finds the length of the `word` field and stores it in a new `word_count` field. The `remove` processor removes the `test` field.
```json
PUT _ingest/pipeline/pipeline-test
{
"description": "Splits the text field into a list. Computes the length of the 'word' field and stores it in a new 'word_count' field. Removes the 'test' field.",
"processors": [
{
"split": {
"field": "text",
"separator": "\\s+",
"target_field": "word"
},
}
{
"script": {
"lang": "painless",
"source": "ctx.word_count = ctx.word.length"
}
},
{
"remove": {
"field": "test"
}
}
]
}
```
After creating a pipeline, you can use the `reindex` operation:
```json
POST _reindex
{
"source": {
"index": "source",
},
"dest": {
"index": "destination",
"pipeline": "pipeline-test"
}
}
```
## Update documents in the current index
To update the data in your current index itself without copying it to a different index, use the `update_by_query` operation.
The `update_by_query` operation is `POST` operation that you can perform on a single index at a time.
```json
POST <index_name>/_update_by_query
```
If you run this command with no parameters, it increments the version number for all documents in the index.
## Source index options
You can specify the following options for your source index:
Option | Valid values | Description | Required
:--- | :--- | :---
`index` | String | The name of the source index. You can provide multiple source indices as a list. | Yes
`max_docs` | Integer | The maximum number of documents to reindex. | No
`query` | Object | The search query to use for the reindex operation. | No
`size` | Integer | The number of documents to reindex. | No
`slice` | String | Specify manual or automatic slicing to parallelize reindexing. | No
`sort` | List | Sort specific fields in the document before reindexing. | No
## Destination index options
You can specify the following options for your destination index:
Option | Valid values | Description | Required
:--- | :--- | :---
`index` | String | The name of the destination index. | Yes
`version_type` | Enum | The version type for the indexing operation. Valid values: internal, external, external_gt, external_gte. | No

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@ -1,61 +0,0 @@
---
layout: default
title: cat aliases
parent: CAT
grand_parent: REST API reference
nav_order: 1
has_children: false
---
# cat aliases
The cat aliases operation lists the mapping of aliases to indices, plus routing and filtering information.
## Example
```json
GET _cat/aliases?v
```
To limit the information to a specific alias, add the alias name after your query:
```json
GET _cat/aliases/<alias>?v
```
If you want to get information for more than one alias, separate the alias names with commas:
```json
GET _cat/aliases/alias1,alias2,alias3
```
## Path and HTTP methods
```
GET _cat/aliases/<alias>
GET _cat/aliases
```
## URL parameters
All cat aliases URL parameters are optional.
In addition to the [common URL parameters]({{site.url}}{{site.baseurl}}/opensearch/rest-api/cat/index#common-url-parameters), you can specify the following parameters:
Parameter | Type | Description
:--- | :--- | :---
local | Boolean | Whether to return information from the local node only instead of from the master node. Default is false.
expand_wildcards | Enum | Expands wildcard expressions to concrete indices. Combine multiple values with commas. Supported values are `all`, `open`, `closed`, `hidden`, and `none`. Default is `open`.
## Response
The following response shows that `alias1` refers to a `movies` index and has a configured filter:
```json
alias | index | filter | routing.index | routing.search | is_write_index
alias1 | movies | * | - | - | -
.kibana | .kibana_1 | - | - | - | -
```
To learn more about index aliases, see [Index aliases]({{site.url}}{{site.baseurl}}/opensearch/index-alias).

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@ -1,61 +0,0 @@
---
layout: default
title: cat allocation
parent: CAT
grand_parent: REST API reference
nav_order: 5
has_children: false
---
# cat allocation
The cat allocation operation lists the allocation of disk space for indices and the number of shards on each node.
## Example
```json
GET _cat/allocation?v
```
To limit the information to a specific node, add the node name after your query:
```json
GET _cat/allocation/<node_name>
```
If you want to get information for more than one node, separate the node names with commas:
```json
GET _cat/aliases/node_name_1,node_name_2,node_name_3
```
## Path and HTTP methods
```
GET _cat/allocation?v
GET _cat/allocation/<node_name>
```
## URL parameters
All cat allocation URL parameters are optional.
In addition to the [common URL parameters]({{site.url}}{{site.baseurl}}/opensearch/rest-api/cat/index#common-url-parameters), you can specify the following parameters:
Parameter | Type | Description
:--- | :--- | :---
bytes | Byte size | Specify the units for byte size. For example, `7kb` or `6gb`. For more information, see [Supported units]({{site.url}}{{site.baseurl}}/opensearch/units/).
local | Boolean | Whether to return information from the local node only instead of from the master node. Default is false.
master_timeout | Time | The amount of time to wait for a connection to the master node. Default is 30 seconds.
## Response
The following response shows that 8 shards are allocated to each the two nodes available:
```json
shards | disk.indices | disk.used | disk.avail | disk.total | disk.percent host | ip | node
8 | 989.4kb | 25.9gb | 32.4gb | 58.4gb | 44 172.18.0.4 | 172.18.0.4 | odfe-node1
8 | 962.4kb | 25.9gb | 32.4gb | 58.4gb | 44 172.18.0.3 | 172.18.0.3 | odfe-node2
```

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@ -1,51 +0,0 @@
---
layout: default
title: cat count
parent: CAT
grand_parent: REST API reference
nav_order: 10
has_children: false
---
# cat count
The cat count operation lists the number of documents in your cluster.
## Example
```json
GET _cat/count?v
```
To see the number of documents in a specific index or alias, add the index or alias name after your query:
```json
GET _cat/count/<index_or_alias>?v
```
If you want to get information for more than one index or alias, separate the index or alias names with commas:
```json
GET _cat/aliases/index_or_alias_1,index_or_alias_2,index_or_alias_3
```
## Path and HTTP methods
```
GET _cat/count?v
GET _cat/count/<index>?v
```
## URL parameters
All cat count URL parameters are optional. You can specify any of the [common URL parameters]({{site.url}}{{site.baseurl}}/opensearch/rest-api/cat/index#common-url-parameters).
## Response
The following response shows the overall document count as 1625:
```json
epoch | timestamp | count
1624237738 | 01:08:58 | 1625
```

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@ -1,57 +0,0 @@
---
layout: default
title: cat field data
parent: CAT
grand_parent: REST API reference
nav_order: 15
has_children: false
---
# cat fielddata
The cat fielddata operation lists the memory size used by each field per node.
## Example
```json
GET _cat/fielddata?v
```
To limit the information to a specific field, add the field name after your query:
```json
GET _cat/fielddata/<field_name>?v
```
If you want to get information for more than one field, separate the field names with commas:
```json
GET _cat/aliases/field_name_1,field_name_2,field_name_3
```
## Path and HTTP methods
```
GET _cat/fielddata?v
GET _cat/fielddata/<field_name>?v
```
## URL parameters
All cat fielddata URL parameters are optional.
In addition to the [common URL parameters]({{site.url}}{{site.baseurl}}/opensearch/rest-api/cat/index#common-url-parameters), you can specify the following parameter:
Parameter | Type | Description
:--- | :--- | :---
bytes | Byte size | Specify the units for byte size. For example, `7kb` or `6gb`. For more information, see [Supported units]({{site.url}}{{site.baseurl}}/opensearch/units/).
## Response
The following response shows the memory size for all fields as 284 bytes:
```json
id host ip node field size
1vo54NuxSxOrbPEYdkSF0w 172.18.0.4 172.18.0.4 odfe-node1 _id 284b
ZaIkkUd4TEiAihqJGkp5CA 172.18.0.3 172.18.0.3 odfe-node2 _id 284b
```

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@ -1,42 +0,0 @@
---
layout: default
title: cat health
parent: CAT
grand_parent: REST API reference
nav_order: 20
has_children: false
---
# cat health
The cat health operation lists the status of the cluster, how long the cluster has been up, the number of nodes, and other useful information that helps you analyze the health of your cluster.
## Example
```json
GET _cat/health?v
```
## Path and HTTP methods
```
GET _cat/health?v
```
## URL parameters
All cat health URL parameters are optional.
Parameter | Type | Description
:--- | :--- | :---
time | Time | Specify the units for time. For example, `5d` or `7h`. For more information, see [Supported units]({{site.url}}{{site.baseurl}}/opensearch/units/).
ts | Boolean | If true, returns HH:MM:SS and Unix epoch timestamps. Default is true.
## Response
```json
GET _cat/health?v&time=5d
epoch | timestamp | cluster | status | node.total | node.data | shards | pri | relo | init | unassign | pending_tasks | max_task_wait_time | active_shards_percent
1624248112 | 04:01:52 | odfe-cluster | green | 2 | 2 | 16 | 8 | 0 | 0 | 0 | 0 | - | 100.0%
```

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@ -1,61 +0,0 @@
---
layout: default
title: cat indices
parent: CAT
grand_parent: REST API reference
nav_order: 25
has_children: false
---
# cat indices
The cat indices operation lists information related to indices⁠—how much disk space they are using, how many shards they have, their health status, and so on.
## Example
```
GET _cat/indices?v
```
To limit the information to a specific index, add the index name after your query.
```
GET _cat/indices/<index>?v
```
If you want to get information for more than one index, separate the indices with commas:
```json
GET _cat/aliases/index1,index2,index3
```
## Path and HTTP methods
```
GET _cat/indices/<index>
GET _cat/indices
```
## URL parameters
All cat indices URL parameters are optional.
In addition to the [common URL parameters]({{site.url}}{{site.baseurl}}/opensearch/rest-api/cat/index#common-url-parameters), you can specify the following parameters:
Parameter | Type | Description
:--- | :--- | :---
bytes | Byte size | Specify the units for byte size. For example, `7kb` or `6gb`. For more information, see [Supported units]({{site.url}}{{site.baseurl}}/opensearch/units/).
health | String | Limit indices based on their health status. Supported values are `green`, `yellow`, and `red`.
include_unloaded_segments | Boolean | Whether to include information from segments not loaded into memory. Default is false.
master_timeout | Time | The amount of time to wait for a connection to the master node. Default is 30 seconds.
pri | Boolean | Whether to return information only from the primary shards. Default is false.
time | Time | Specify the units for time. For example, `5d` or `7h`. For more information, see [Supported units]({{site.url}}{{site.baseurl}}/opensearch/units/).
expand_wildcards | Enum | Expands wildcard expressions to concrete indices. Combine multiple values with commas. Supported values are `all`, `open`, `closed`, `hidden`, and `none`. Default is `open`.
## Response
```json
health | status | index | uuid | pri | rep | docs.count | docs.deleted | store.size | pri.store.size
green | open | movies | UZbpfERBQ1-3GSH2bnM3sg | 1 | 1 | 1 | 0 | 7.7kb | 3.8kb
```

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@ -1,42 +0,0 @@
---
layout: default
title: cat master
parent: CAT
grand_parent: REST API reference
nav_order: 30
has_children: false
---
# cat master
The cat master operation lists information that helps identify the elected master node.
## Example
```
GET _cat/master?v
```
## Path and HTTP methods
```
GET _cat/master
```
## URL parameters
All cat master URL parameters are optional.
In addition to the [common URL parameters]({{site.url}}{{site.baseurl}}/opensearch/rest-api/cat/index#common-url-parameters), you can specify the following parameters:
Parameter | Type | Description
:--- | :--- | :---
master_timeout | Time | The amount of time to wait for a connection to the master node. Default is 30 seconds.
## Response
```json
id | host | ip | node
ZaIkkUd4TEiAihqJGkp5CA | 172.18.0.3 | 172.18.0.3 | odfe-node2
```

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@ -1,43 +0,0 @@
---
layout: default
title: cat nodeattrs
parent: CAT
grand_parent: REST API reference
nav_order: 35
has_children: false
---
# cat nodeattrs
The cat nodeattrs operation lists the attributes of custom nodes.
## Example
```
GET _cat/nodeattrs?v
```
## Path and HTTP methods
```
GET _cat/nodeattrs
```
## URL parameters
All cat nodeattrs URL parameters are optional.
In addition to the [common URL parameters]({{site.url}}{{site.baseurl}}/opensearch/rest-api/cat/index#common-url-parameters), you can specify the following parameters:
Parameter | Type | Description
:--- | :--- | :---
local | Boolean | Whether to return information from the local node only instead of from the master node. Default is false.
master_timeout | Time | The amount of time to wait for a connection to the master node. Default is 30 seconds.
## Response
```json
node | host | ip | attr | value
odfe-node2 | 172.18.0.3 | 172.18.0.3 | testattr | test
```

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@ -1,50 +0,0 @@
---
layout: default
title: cat nodes
parent: CAT
grand_parent: REST API reference
nav_order: 40
has_children: false
---
# cat nodes
The cat nodes operation lists node-level information, including node roles and load metrics.
A few important node metrics are `pid`, `name`, `master`, `ip`, `port`, `version`, `build`, `jdk`, along with `disk`, `heap`, `ram`, and `file_desc`.
## Example
```
GET _cat/nodes?v
```
## Path and HTTP methods
```
GET _cat/nodes
```
## URL parameters
All cat nodes URL parameters are optional.
In addition to the [common URL parameters]({{site.url}}{{site.baseurl}}/opensearch/rest-api/cat/index#common-url-parameters), you can specify the following parameters:
Parameter | Type | Description
:--- | :--- | :---
bytes | Byte size | Specify the units for byte size. For example, `7kb` or `6gb`. For more information, see [Supported units]({{site.url}}{{site.baseurl}}/opensearch/units/).
full_id | Boolean | If true, return the full node ID. If false, return the shortened node ID. Defaults to false.
local | Boolean | Whether to return information from the local node only instead of from the master node. Default is false.
master_timeout | Time | The amount of time to wait for a connection to the master node. Default is 30 seconds.
time | Time | Specify the units for time. For example, `5d` or `7h`. For more information, see [Supported units]({{site.url}}{{site.baseurl}}/opensearch/units/).
include_unloaded_segments | Boolean | Whether to include information from segments not loaded into memory. Default is false.
## Response
```json
ip | heap.percent | ram.percent | cpu load_1m | load_5m | load_15m | node.role | master | name
172.18.0.3 | 31 | 97 | 3 | 0.03 | 0.10 | 0.14 dimr | * | odfe-node2
172.18.0.4 | 45 | 97 | 3 | 0.19 | 0.14 | 0.15 dimr | - | odfe-node1
```

44
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@ -1,44 +0,0 @@
---
layout: default
title: cat pending tasks
parent: CAT
grand_parent: REST API reference
nav_order: 45
has_children: false
---
# cat pending tasks
The cat pending tasks operation lists the progress of all pending tasks, including task priority and time in queue.
## Example
```
GET _cat/pending_tasks?v
```
## Path and HTTP methods
```
GET _cat/pending_tasks
```
## URL parameters
All cat nodes URL parameters are optional.
In addition to the [common URL parameters]({{site.url}}{{site.baseurl}}/opensearch/rest-api/cat/index#common-url-parameters), you can specify the following parameters:
Parameter | Type | Description
:--- | :--- | :---
local | Boolean | Whether to return information from the local node only instead of from the master node. Default is false.
master_timeout | Time | The amount of time to wait for a connection to the master node. Default is 30 seconds.
time | Time | Specify the units for time. For example, `5d` or `7h`. For more information, see [Supported units]({{site.url}}{{site.baseurl}}/opensearch/units/).
## Response
```json
insertOrder | timeInQueue | priority | source
1786 | 1.8s | URGENT | shard-started
```

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@ -1,62 +0,0 @@
---
layout: default
title: cat plugins
parent: CAT
grand_parent: REST API reference
nav_order: 50
has_children: false
---
# cat plugins
The cat plugins operation lists the names, components, and versions of the installed plugins.
## Example
```
GET _cat/plugins?v
```
## Path and HTTP methods
```
GET _cat/plugins
```
## URL parameters
All cat plugins URL parameters are optional.
In addition to the [common URL parameters]({{site.url}}{{site.baseurl}}/opensearch/rest-api/cat/index#common-url-parameters), you can specify the following parameters:
Parameter | Type | Description
:--- | :--- | :---
local | Boolean | Whether to return information from the local node only instead of from the master node. Default is false.
master_timeout | Time | The amount of time to wait for a connection to the master node. Default is 30 seconds.
## Response
```json
name component version
odfe-node2 opendistro-alerting 1.13.1.0
odfe-node2 opendistro-anomaly-detection 1.13.0.0
odfe-node2 opendistro-asynchronous-search 1.13.0.1
odfe-node2 opendistro-index-management 1.13.2.0
odfe-node2 opendistro-job-scheduler 1.13.0.0
odfe-node2 opendistro-knn 1.13.0.0
odfe-node2 opendistro-performance-analyzer 1.13.0.0
odfe-node2 opendistro-reports-scheduler 1.13.0.0
odfe-node2 opendistro-sql 1.13.2.0
odfe-node2 opendistro_security 1.13.1.0
odfe-node1 opendistro-alerting 1.13.1.0
odfe-node1 opendistro-anomaly-detection 1.13.0.0
odfe-node1 opendistro-asynchronous-search 1.13.0.1
odfe-node1 opendistro-index-management 1.13.2.0
odfe-node1 opendistro-job-scheduler 1.13.0.0
odfe-node1 opendistro-knn 1.13.0.0
odfe-node1 opendistro-performance-analyzer 1.13.0.0
odfe-node1 opendistro-reports-scheduler 1.13.0.0
odfe-node1 opendistro-sql 1.13.2.0
odfe-node1 opendistro_security 1.13.1.0
```

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@ -1,57 +0,0 @@
---
layout: default
title: cat recovery
parent: CAT
grand_parent: REST API reference
nav_order: 50
has_children: false
---
# cat recovery
The cat recovery operation lists all completed and ongoing index and shard recoveries.
## Example
```
GET _cat/recovery?v
```
To see only the recoveries of a specific index, add the index name after your query.
```
GET _cat/recovery/<index>?v
```
If you want to get information for more than one index, separate the indices with commas:
```json
GET _cat/aliases/index1,index2,index3
```
## Path and HTTP methods
```
GET _cat/recovery
```
## URL parameters
All cat recovery URL parameters are optional.
In addition to the [common URL parameters]({{site.url}}{{site.baseurl}}/opensearch/rest-api/cat/index#common-url-parameters), you can specify the following parameters:
Parameter | Type | Description
:--- | :--- | :---
active_only | Boolean | Whether to only include ongoing shard recoveries. Default is false.
bytes | Byte size | Specify the units for byte size. For example, `7kb` or `6gb`. For more information, see [Supported units]({{site.url}}{{site.baseurl}}/opensearch/units/).
detailed | Boolean | Whether to include detailed information about shard recoveries. Default is false.
time | Time | Specify the units for time. For example, `5d` or `7h`. For more information, see [Supported units]({{site.url}}{{site.baseurl}}/opensearch/units/).
## Response
```json
index | shard | time | type | stage | source_host | source_node | target_host | target_node | repository | snapshot | files | files_recovered | files_percent | files_total | bytes | bytes_recovered | bytes_percent | bytes_total | translog_ops | translog_ops_recovered | translog_ops_percent
movies | 0 | 117ms | empty_store | done | n/a | n/a | 172.18.0.4 | odfe-node1 | n/a | n/a | 0 | 0 | 0.0% | 0 | 0 | 0 | 0.0% | 0 | 0 | 0 | 100.0%
movies | 0 | 382ms | peer | done | 172.18.0.4 | odfe-node1 | 172.18.0.3 | odfe-node2 | n/a | n/a | 1 | 1 | 100.0% | 1 | 208 | 208 | 100.0% | 208 | 1 | 1 | 100.0%
```

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@ -1,44 +0,0 @@
---
layout: default
title: cat repositories
parent: CAT
grand_parent: REST API reference
nav_order: 55
has_children: false
---
# cat repositories
The cat repositories operation lists all completed and ongoing index and shard recoveries.
## Example
```
GET _cat/repositories?v
```
## Path and HTTP methods
```
GET _cat/repositories
```
## URL parameters
All cat repositories URL parameters are optional.
In addition to the [common URL parameters]({{site.url}}{{site.baseurl}}/opensearch/rest-api/cat/index#common-url-parameters), you can specify the following parameters:
Parameter | Type | Description
:--- | :--- | :---
local | Boolean | Whether to return information from the local node only instead of from the master node. Default is false.
master_timeout | Time | The amount of time to wait for a connection to the master node. Default is 30 seconds.
## Response
```json
id type
repo1 fs
repo2 s3
```

55
_opensearch/rest-api/cat/cat-segments.md
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@ -1,55 +0,0 @@
---
layout: default
title: cat segments
parent: CAT
grand_parent: REST API reference
nav_order: 55
has_children: false
---
# cat segments
The cat segments operation lists Lucene segment-level information for each index.
## Example
```
GET _cat/segments?v
```
To see only the information about segments of a specific index, add the index name after your query.
```
GET _cat/segments/<index>?v
```
If you want to get information for more than one index, separate the indices with commas:
```
GET _cat/segments/index1,index2,index3
```
## Path and HTTP methods
```
GET _cat/segments
```
## URL parameters
All cat segments URL parameters are optional.
In addition to the [common URL parameters]({{site.url}}{{site.baseurl}}/opensearch/rest-api/cat/index#common-url-parameters), you can specify the following parameter:
Parameter | Type | Description
:--- | :--- | :---
bytes | Byte size | Specify the units for byte size. For example, `7kb` or `6gb`. For more information, see [Supported units]({{site.url}}{{site.baseurl}}/opensearch/units/)..
## Response
```json
index | shard | prirep | ip | segment | generation | docs.count | docs.deleted | size | size.memory | committed | searchable | version | compound
movies | 0 | p | 172.18.0.4 | _0 | 0 | 1 | 0 | 3.5kb | 1364 | true | true | 8.7.0 | true
movies | 0 | r | 172.18.0.3 | _0 | 0 | 1 | 0 | 3.5kb | 1364 | true | true | 8.7.0 | true
```

58
_opensearch/rest-api/cat/cat-shards.md
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@ -1,58 +0,0 @@
---
layout: default
title: cat shards
parent: CAT
grand_parent: REST API reference
nav_order: 60
has_children: false
---
# cat shards
The cat shards operation lists the state of all primary and replica shards and how they are distributed.
## Example
```
GET _cat/shards?v
```
To see only the information about shards of a specific index, add the index name after your query.
```
GET _cat/shards/<index>?v
```
If you want to get information for more than one index, separate the indices with commas:
```
GET _cat/shards/index1,index2,index3
```
## Path and HTTP methods
```
GET _cat/shards
```
## URL parameters
All cat shards URL parameters are optional.
In addition to the [common URL parameters]({{site.url}}{{site.baseurl}}/opensearch/rest-api/cat/index#common-url-parameters), you can specify the following parameter:
Parameter | Type | Description
:--- | :--- | :---
bytes | Byte size | Specify the units for byte size. For example, `7kb` or `6gb`. For more information, see [Supported units]({{site.url}}{{site.baseurl}}/opensearch/units/).
local | Boolean | Whether to return information from the local node only instead of from the master node. Default is false.
master_timeout | Time | The amount of time to wait for a connection to the master node. Default is 30 seconds.
time | Time | Specify the units for time. For example, `5d` or `7h`. For more information, see [Supported units]({{site.url}}{{site.baseurl}}/opensearch/units/).
## Response
```json
index | shard | prirep | state | docs | store | ip | | node
plugins | 0 | p | STARTED | 0 | 208b | 172.18.0.4 | odfe-node1
plugins | 0 | r | STARTED | 0 | 208b | 172.18.0.3 | odfe-node2
```

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