iSharkFly-Docs
/
opensearch-docs-cn
mirror of https://github.com/iSharkFly-Docs/opensearch-docs-cn
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'main' into lukas-vlcek-add_node_APIs

This commit is contained in:
Naarcha-AWS 2022-05-19 10:25:13 -05:00 committed by GitHub
parent 6ad4d00060 099ba00c71
commit e437eaacfd
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
20 changed files with 98 additions and 392 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
CONTRIBUTING.md
View File

@ -1,6 +1,6 @@
# Contributing Guidelines
Thank you for your interest in contributing to our project. Whether it's a bug report, new feature, correction, or additional
Thank you for your interest in contributing to the OpenSource documentation! 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
@ -9,7 +9,7 @@ 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.
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:

54
_dashboards/install/rpm.md
View File

@ -1,54 +0,0 @@
---
layout: default
title: RPM
parent: Install OpenSearch Dashboards
nav_order: 31
---
# Run OpenSearch Dashboards using RPM
1. Create a repository file for OpenSearch Dashboards:
```bash
sudo curl -SL https://artifacts.opensearch.org/releases/bundle/opensearch-dashboards/2.x/opensearch-dashboards-2.x.repo -o /etc/yum.repos.d/opensearch-dashboards-2.x.repo
```
2. Clean your YUM cache, to ensure a smooth installation:
```bash
sudo yum clean all
```
3. With the repository file downloaded, list all available versions of OpenSearch:
```bash
sudo yum list | grep opensearch-dashboards
```
4. Choose the version of OpenSearch Dashboards you want to install:
```bash
sudo yum install opensearch-dashboards
```
Unless otherwise indicated, the highest minor version of OpenSearch Dashboards installs.
5. During installation, the installer stops to see if the GPG key matches the OpenSearch project. Verify that the `Fingerprint` matches the following:
```bash
Fingerprint: c5b7 4989 65ef d1c2 924b a9d5 39d3 1987 9310 d3fc
```
If correct, enter `yes` or `y`. The OpenSearch Dashboards installation continues.
6. Run OpenSearch Dashboards using `systemctl`.
```bash
sudo systemctl start opensearch-dashboards.service
```
7. To stop running OpenSearch Dashboards, enter
```bash
sudo systemctl stop opensearch-dashboards.service
```

2
_im-plugin/ism/policies.md
View File

@ -127,7 +127,7 @@ Parameter | Description | Type | Required
### read_only
Sets a managed index to be read only. Read-only indexes don't refresh.
Sets a managed index to be read only.
```json
{

2
_ml-commons-plugin/api.md
View File

@ -20,7 +20,7 @@ The Machine Learning (ML) commons API lets you train ML algorithms synchronously
In order to train tasks through the API, three inputs are required.
- Algorithm name: Must be one of a [FunctionaName](https://github.com/opensearch-project/ml-commons/blob/1.3/common/src/main/java/org/opensearch/ml/common/parameter/FunctionName.java). This determines what algorithm the ML Engine runs. To add a new function, see [How To Add a New Function](https://github.com/opensearch-project/ml-commons/blob/main/docs/how-to-add-new-function.md).
- Algorithm name: Must be one of a [FunctionName](https://github.com/opensearch-project/ml-commons/blob/1.3/common/src/main/java/org/opensearch/ml/common/parameter/FunctionName.java). This determines what algorithm the ML Engine runs. To add a new function, see [How To Add a New Function](https://github.com/opensearch-project/ml-commons/blob/main/docs/how-to-add-new-function.md).
- Model hyper parameters: Adjust these parameters to make the model train better.
- Input data: The data input that trains the ML model, or applies the ML models to predictions. You can input data in two ways, query against your index or use data frame.

39
_opensearch/cluster.md
View File

@ -12,23 +12,26 @@ OpenSearch can operate as a single-node or multi-node cluster. The steps to conf
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:
There are many ways to design a cluster. The following illustration shows a basic architecture that includes a four-node cluster that has one dedicated cluster manager node, one dedicated coordinating node, and two data nodes that are cluster manager eligible and also used for ingesting data.
The nomenclature recently changed for the master node; it is now called the cluster manager node.
{: .note }
![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.
### Nodes
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.
Cluster manager | Manages the overall operation of a cluster and keeps track of the cluster state. This includes creating and deleting indexes, 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 cluster manager 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.
Cluster manager eligible | Elects one node among them as the cluster manager node through a voting process. | For production clusters, make sure you have dedicated cluster manager 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 cluster manager 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 | Pre-processes 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.
By default, each node is a cluster-manager-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/).
@ -62,18 +65,18 @@ Make the same change on all the nodes to make sure that they'll join to form a c
After you name the cluster, set node attributes for each node in your cluster.
#### Master node
#### Cluster manager 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.
Give your cluster manager 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
node.name: opensearch-cluster_manager
```
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.
You can also explicitly specify that this node is a cluster manager node, even though it is already set to true by default. Set the node role to `cluster_manager` to make it easier to identify the cluster manager node.
```yml
node.roles: [ master ]
node.roles: [ cluster_manager ]
```
#### Data nodes
@ -88,7 +91,7 @@ node.name: opensearch-d1
node.name: opensearch-d2
```
You can make them master-eligible data nodes that will also be used for ingesting data:
You can make them cluster-manager-eligible data nodes that will also be used for ingesting data:
```yml
node.roles: [ data, ingest ]
@ -132,9 +135,9 @@ Now that you've configured the network hosts, you need to configure the discover
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.
You can generally just add all of your cluster-manager-eligible nodes to the `discovery.seed_hosts` array. When a node starts up, it finds the other cluster-manager-eligible nodes, determines which one is the cluster manager, and asks to join the cluster.
For example, for `opensearch-master` the line looks something like this:
For example, for `opensearch-cluster_manager` 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>"]
@ -161,8 +164,8 @@ 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
ip heap.percent ram.percent cpu load_1m load_5m load_15m node.role cluster_manager name
x.x.x.x 13 61 0 0.02 0.04 0.05 mi * opensearch-cluster_manager
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

4
_opensearch/install/docker-security.md
View File

@ -24,7 +24,7 @@ services:
- cluster.name=opensearch-cluster
- node.name=opensearch-node1
- discovery.seed_hosts=opensearch-node1,opensearch-node2
- cluster.initial_master_nodes=opensearch-node1,opensearch-node2
- cluster.initial_cluster_manager_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
@ -60,7 +60,7 @@ services:
- cluster.name=opensearch-cluster
- node.name=opensearch-node2
- discovery.seed_hosts=opensearch-node1,opensearch-node2
- cluster.initial_master_nodes=opensearch-node1,opensearch-node2
- cluster.initial_cluster_manager_nodes=opensearch-node1,opensearch-node2
- bootstrap.memory_lock=true
- "OPENSEARCH_JAVA_OPTS=-Xms512m -Xmx512m"
- network.host=0.0.0.0

157
_opensearch/install/rpm.md
View File

@ -1,157 +0,0 @@
---
layout: default
title: RPM
parent: Install OpenSearch
nav_order: 51
---
# RPM
The RPM Package Manager (RPM) installation provides everything you need to run OpenSearch inside Red Hat or Red Hat-based Linux Distributions.
RPM supports CentOS 7 and 8, and Amazon Linux 2. If you have your own Java installation and set `JAVA_HOME` in your terminal application, macOS works, as well.
There are two methods for installing OpenSearch on RPM:
## Manual method
1. Download the RPM package directly from the [OpenSearch downloads page](https://opensearch.org/downloads.html){:target='\_blank'}. The RPM package can be download both as `x64` and `arm64`.
2. Import the public GPG key. This key verifies that the your OpenSearch instance is signed.
```bash
sudo rpm --import https://artifacts.opensearch.org/publickeys/opensearch.pgp
```
3. On your host, use `sudo yum install` or `sudo rpm -ivh` to install the package.
**x64**
```bash
sudo yum install opensearch-{{site.opensearch_version}}-linux-x64.rpm
sudo yum install opensearch-dashboards-{{site.opensearch_version}}-linux-x64.rpm
```
```bash
sudo rpm -ivh opensearch-{{site.opensearch_version}}-linux-x64.rpm
sudo rpm -ivh opensearch-dashboards-{{site.opensearch_version}}-linux-x64.rpm
```
**arm64**
```bash
sudo yum install opensearch-{{site.opensearch_version}}-linux-x64.rpm
sudo yum install opensearch-dashboards-{{site.opensearch_version}}-linux-arm64.rpm
```
```bash
sudo rpm -ivh opensearch-{{site.opensearch_version}}-linux-x64.rpm
sudo rpm -ivh opensearch-dashboards-{{site.opensearch_version}}-linux-arm64.rpm
```
Once complete, you can run OpenSearch inside your distribution.
## YUM method
YUM, an RPM package management tool, allows you to pull the RPM package from the YUM repository library.
1. Create a repository file for both OpenSearch and OpenSearch Dashboards:
```bash
sudo curl -SL https://artifacts.opensearch.org/releases/bundle/opensearch/2.x/opensearch-2.x.repo -o /etc/yum.repos.d/opensearch-2.x.repo
```
```bash
sudo curl -SL https://artifacts.opensearch.org/releases/bundle/opensearch-dashboards/2.x/opensearch-dashboards-2.x.repo -o /etc/yum.repos.d/opensearch-dashboards-2.x.repo
```
To verify that the repos appear in your repo list, use `sudo yum repolist`.
2. Clean your YUM cache, to ensure a smooth installation:
```bash
sudo yum clean all
```
3. With the repository file downloaded, list all available versions of OpenSearch:
```bash
sudo yum list | grep opensearch
```
4. Choose the version of OpenSearch you want to install:
```bash
sudo yum install opensearch
sudo yum install opensearch-dashboards
```
Unless otherwise indicated, the highest minor version of OpenSearch installs.
To install a specific version of OpenSearch:
```bash
sudo yum install 'opensearch-{{site.opensearch_version}}'
```
5. During installation, the installer stops to see if the GPG key matches the OpenSearch project. Verify that the `Fingerprint` matches the following:
```bash
Fingerprint: c5b7 4989 65ef d1c2 924b a9d5 39d3 1987 9310 d3fc
```
If correct, enter `yes` or `y`. The OpenSearch installation continues.
Once complete, you can run OpenSearch inside your distribution.
## Run OpenSearch
1. Run OpenSearch and OpenSearch Dashboards using `systemctl`.
```bash
sudo systemctl start opensearch.service
sudo systemctl start opensearch-dashboards.service
```
2. Send requests to the server to verify that OpenSearch is running:
```bash
curl -XGET https://localhost:9200 -u 'admin:admin' --insecure
curl -XGET https://localhost:9200/_cat/config?v -u 'admin:admin' --insecure
```
3. To stop running OpenSearch, enter:
```bash
sudo systemctl stop opensearch.service
sudo systemctl stop opensearch-dashboards.service
```
## *(Optional)* Set up Performance Analyzer
When enabled, the Performance Analyzer plugin collects data related to the performance of your OpenSearch instance. To start the Performance Analyzer plugin, enter:
```bash
sudo systemctl start opensearch-performance-analyzer.service
```
To stop the Performance Analyzer, enter:
```bash
sudo systemctl stop opensearch-performance-analyzer.service
```
## Upgrade RPM
You can upgrade your RPM OpenSearch instance both manually and through YUM.
### Manual
Download the new version of OpenSearch you want to use, and then use `rmp -Uvh` to upgrade.
### YUM
To upgrade to the latest version of OpenSearch with YUM, use `sudo yum update`. You can also upgrade to a specific OpenSearch version by using `sudo yum update opensearch-<version-number>`.

2
_opensearch/query-dsl/full-text.md
View File

@ -7,7 +7,7 @@ nav_order: 40
# Full-text queries
This page lists all full-text query types and common options. There are many options for full-text queries, each with its own subtle behavior difference, so the best method to ensure that you obtain useful search results is to test different queries against representative indexes and verify the outputs individually.
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.
---

33
_opensearch/query-dsl/index.md
View File

@ -12,31 +12,9 @@ redirect_from:
# Query DSL
OpenSearch provides a query domain-specific language (DSL) that you can use to search with more options than a simple search via HTTP request parameter alone. The query DSL uses the HTTP request body, so you can more easily customize your queries to get the exact results that you want.
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.
The OpenSearch query DSL provides three query options: 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.
## DSL Query Types
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:
| Metrics | Term-level queries | Full-text queries
:--- | :--- | :---
*Query results* | 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 }
To show the difference between a simple HTTP search versus a search via query DSL, we have an example of each one so that you can see how they differ.
## Example: HTTP simple search
The following request performs a simple search to search for a `speaker` field that has a value of `queen`.
For example, the following request performs a simple search to search for a `speaker` field that has a value of `queen`.
**Sample request**
```json
@ -77,9 +55,7 @@ GET _search?q=speaker:queen
}
```
## Example: Query DSL search
With a query DSL search, 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`.
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
@ -142,4 +118,5 @@ With a query DSL search, you can include an HTTP request body to look for result
]
}
}
```
```
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.

115
_opensearch/query-dsl/term.md
View File

@ -7,6 +7,20 @@ nav_order: 30
# 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
@ -214,12 +228,7 @@ The search query “HAMLET” is also searched literally. So, to get a match on
---
## Term-level query operations
This section provides examples for term-level query operations that you can use for specific search use cases.
## Single term
## Term
Use the `term` query to search for an exact term in a field.
@ -236,9 +245,9 @@ GET shakespeare/_search
}
```
## Multiple terms
## Terms
Use the `terms` operation to search for multiple values for same query field.
Use the `terms` query to search for multiple terms in the same field.
```json
GET shakespeare/_search
@ -255,86 +264,8 @@ GET shakespeare/_search
```
You get back documents that match any of the terms.
#### Sample response
```json
{
"took" : 11,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "shakespeare",
"_id" : "61808",
"_score" : 1.0,
"_source" : {
"type" : "line",
"line_id" : 61809,
"play_name" : "Merchant of Venice",
"speech_number" : 33,
"line_number" : "1.3.115",
"speaker" : "SHYLOCK",
"text_entry" : "Go to, then; you come to me, and you say"
}
},
{
"_index" : "shakespeare",
"_id" : "61809",
"_score" : 1.0,
"_source" : {
"type" : "line",
"line_id" : 61810,
"play_name" : "Merchant of Venice",
"speech_number" : 33,
"line_number" : "1.3.116",
"speaker" : "SHYLOCK",
"text_entry" : "Shylock, we would have moneys: you say so;"
}
}
]
}
}
```
## Terms lookup query (TLQ)
Use a terms lookup query (TLQ) to retrieve multiple field values in a specific document within a specific index. Use the `terms` operation, and specify the index name, document Id and specify the field you want to look up with the `path` parameter.
Parameter | Behavior
:--- | :---
`index` | The index name that contains the document that you want search.
`id` | Specifies the exact document to query for terms.
`path` | Specifies the field name for the query.
To get all the lines from a Shakespeare play for a role (or roles) specified in the index `play-assignments` for the document `42`:
```json
GET shakespeare/_search
{
"query": {
"terms": {
"speaker": {
"index": "play-assignments",
"id": "42",
"path": "role"
}
}
}
}
```
## Document IDs
## IDs
Use the `ids` query to search for one or more document ID values.
@ -352,7 +283,7 @@ GET shakespeare/_search
}
```
## Range of values
## Range
Use the `range` query to search for a range of values in a field.
@ -433,7 +364,7 @@ GET products/_search
The keyword `now` refers to the current date and time.
## Multiple terms by prefix
## Prefix
Use the `prefix` query to search for terms that begin with a specific prefix.
@ -448,7 +379,7 @@ GET shakespeare/_search
}
```
## All instances of a specific field in a document
## Exists
Use the `exists` query to search for documents that contain a specific field.
@ -463,7 +394,7 @@ GET shakespeare/_search
}
```
## Wildcard patterns
## Wildcards
Use wildcard queries to search for terms that match a wildcard pattern.
@ -491,7 +422,7 @@ If we change `*` to `?`, we get no matches, because `?` refers to a single chara
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.
## Regular expressions (Regex)
## Regex
Use the `regexp` query to search for terms that match a regular expression.

16
_opensearch/rest-api/cat/cat-master.md → _opensearch/rest-api/cat/cat-cluster_manager.md
View File

@ -1,44 +1,44 @@
---
layout: default
title: cat master
title: CAT cluster manager
parent: CAT
grand_parent: REST API reference
nav_order: 30
has_children: false
---
# cat master
# CAT cluster_manager
Introduced 1.0
{: .label .label-purple }
The cat master operation lists information that helps identify the elected master node.
The cat cluster manager operation lists information that helps identify the elected cluster manager node.
## Example
```
GET _cat/master?v
GET _cat/cluster_manager?v
```
## Path and HTTP methods
```
GET _cat/master
GET _cat/cluster_manager
```
## URL parameters
All cat master URL parameters are optional.
All cat cluster manager 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.
cluster_manager_timeout | Time | The amount of time to wait for a connection to the cluster manager node. Default is 30 seconds.
## Response
```json
id | host | ip | node
ZaIkkUd4TEiAihqJGkp5CA | 172.18.0.3 | 172.18.0.3 | odfe-node2
ZaIkkUd4TEiAihqJGkp5CA | 172.18.0.3 | 172.18.0.3 | opensearch-node2
```

14
_opensearch/rest-api/cat/cat-nodes.md
View File

@ -13,7 +13,7 @@ Introduced 1.0
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`.
A few important node metrics are `pid`, `name`, `cluster_manager`, `ip`, `port`, `version`, `build`, `jdk`, along with `disk`, `heap`, `ram`, and `file_desc`.
## Example
@ -37,8 +37,8 @@ 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.
local | Boolean | Whether to return information from the local node only instead of from the cluster_manager node. Default is false.
cluster_manager_timeout | Time | The amount of time to wait for a connection to the cluster manager 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.
@ -46,7 +46,9 @@ include_unloaded_segments | Boolean | Whether to include information from segmen
## 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
ip | heap.percent | ram.percent | cpu load_1m | load_5m | load_15m | node.role | cluster_manager | name
172.18.0.3 | 31 | 97 | 3 | 0.03 | 0.10 | 0.14 dimr | * | opensearch-node2
172.18.0.4 | 45 | 97 | 3 | 0.19 | 0.14 | 0.15 dimr | - | opensearch-node1
```

4
_opensearch/rest-api/cat/cat-pending-tasks.md
View File

@ -33,8 +33,8 @@ In addition to the [common URL parameters]({{site.url}}{{site.baseurl}}/opensear
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.
local | Boolean | Whether to return information from the local node only instead of from the cluster_manager node. Default is false.
cluster_manager_timeout | Time | The amount of time to wait for a connection to the cluster manager 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/).

10
_opensearch/rest-api/cat/index.md
View File

@ -1,6 +1,6 @@
---
layout: default
title: CAT
title: CAT API
parent: REST API reference
nav_order: 100
has_children: true
@ -8,15 +8,15 @@ redirect_from:
- /opensearch/catapis/
---
# cat API
# Compact and aligned text (CAT) API
You can get essential statistics about your cluster in an easy-to-understand, tabular format using the compact and aligned text (CAT) API. The cat API is a human-readable interface that returns plain text instead of traditional JSON.
You can get essential statistics about your cluster in an easy-to-understand, tabular format using the compact and aligned text (CAT) API. The CAT API is a human-readable interface that returns plain text instead of traditional JSON.
Using the cat API, you can answer questions like which node is the elected master, what state is the cluster in, how many documents are in each index, and so on.
Using the CAT API, you can answer questions like which node is the elected master, what state is the cluster in, how many documents are in each index, and so on.
## Example
To see the available operations in the cat API, use the following command:
To see the available operations in the CAT API, use the following command:
```
GET _cat

6
_opensearch/rest-api/cluster-health.md
View File

@ -36,10 +36,10 @@ All cluster health parameters are optional.
Parameter | Type | Description
:--- | :--- | :---
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`.
expand_wildcards | Enum | Expands wildcard expressions to concrete indexes. Combine multiple values with commas. Supported values are `all`, `open`, `closed`, `hidden`, and `none`. Default is `open`.
level | Enum | The level of detail for returned health information. Supported values are `cluster`, `indices`, and `shards`. Default is `cluster`.
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.
local | Boolean | Whether to return information from the local node only instead of from the cluster manager node. Default is false.
cluster_manager_timeout | Time | The amount of time to wait for a connection to the cluster manager node. Default is 30 seconds.
timeout | Time | The amount of time to wait for a response. If the timeout expires, the request fails. Default is 30 seconds.
wait_for_active_shards | String | Wait until the specified number of shards is active before returning a response. `all` for all shards. Default is `0`.
wait_for_events | Enum | Wait until all currently queued events with the given priority are processed. Supported values are `immediate`, `urgent`, `high`, `normal`, `low`, and `languid`.

2
_opensearch/rest-api/cluster-settings.md
View File

@ -44,7 +44,7 @@ Parameter | Type | Description
:--- | :--- | :---
flat_settings | Boolean | Whether to return settings in the flat form, which can improve readability, especially for heavily nested settings. For example, the flat form of `"cluster": { "max_shards_per_node": 500 }` is `"cluster.max_shards_per_node": "500"`.
include_defaults (GET only) | Boolean | Whether to include default settings as part of the response. This parameter is useful for identifying the names and current values of settings you want to update.
master_timeout | Time | The amount of time to wait for a response from the master node. Default is 30 seconds.
cluster_manager_timeout | Time | The amount of time to wait for a response from the cluster manager node. Default is 30 seconds.
timeout (PUT only) | Time | The amount of time to wait for a response from the cluster. Default is 30 seconds.

6
_opensearch/rest-api/cluster-stats.md
View File

@ -14,7 +14,7 @@ The cluster stats API operation returns statistics about your cluster.
## Examples
```json
GET _cluster/stats/nodes/_master
GET _cluster/stats/nodes/_cluster_manager
```
## Path and HTTP methods
@ -34,6 +34,9 @@ Parameter | Type | Description
&lt;node-filters&gt; | List | A comma-separated list of [node filters](../nodes-apis/index/#node-filters) that OpenSearch uses to filter results.
Although the `master` node is now called `cluster_manager` for version 2.0, we retained the `master` field for backwards compatibility. If you have a node that has either a `master` role or a `cluster_manager` role, the `count` increases for both fields by 1. To see an example node count increase, see the Response sample.
{: .note }
## Response
```json
@ -218,6 +221,7 @@ Parameter | Type | Description
"data": 1,
"ingest": 1,
"master": 1,
"cluster_manager": 1,
"remote_cluster_client": 1
},
"versions": [

12
_opensearch/stats-api.md
View File

@ -44,7 +44,7 @@ If `enforced` is `true`:
"roles": [
"data",
"ingest",
"master",
"cluster_manager",
"remote_cluster_client"
],
"attributes": {
@ -151,7 +151,7 @@ If `enforced` is `false`:
"roles": [
"data",
"ingest",
"master",
"cluster_manager",
"remote_cluster_client"
],
"attributes": {
@ -264,7 +264,7 @@ GET _nodes/_local/stats/shard_indexing_pressure?include_all
"roles": [
"data",
"ingest",
"master",
"cluster_manager",
"remote_cluster_client"
],
"attributes": {
@ -379,7 +379,7 @@ If `enforced` is `true`:
"roles": [
"data",
"ingest",
"master",
"cluster_manager",
"remote_cluster_client"
],
"attributes": {
@ -422,7 +422,7 @@ If `enforced` is `false`:
"roles": [
"data",
"ingest",
"master",
"cluster_manager",
"remote_cluster_client"
],
"attributes": {
@ -471,7 +471,7 @@ GET _nodes/stats/shard_indexing_pressure
"roles": [
"data",
"ingest",
"master",
"cluster_manager",
"remote_cluster_client"
],
"attributes": {

8
_security-plugin/access-control/document-level-security.md
View File

@ -124,9 +124,9 @@ PUT _plugins/_security/api/roles/abac
}]
}
```
## Use term lookup queries (TLQs) with DLS
## Use term-level lookup queries (TLQs) with DLS
You can perform term lookup queries (TLQs) with document-level security (DLS) using either of two modes: adaptive or filter level. The default mode is adaptive, where OpenSearch automatically switches between Lucene-level or filter-level mode depending on whether or not there is a TLQ. DLS queries that do not contain a TLQ are executed in Lucene-level mode, whereas DLS queries with TLQs are executed in filter-level mode.
You can perform term-level lookup queries (TLQs) with document-level security (DLS) using either of two modes: adaptive or filter level. The default mode is adaptive, where OpenSearch automatically switches between Lucene-level or filter-level mode depending on whether or not there is a TLQ. DLS queries without TLQs are executed in Lucene-level mode, whereas DLS queries with TLQs are executed in filter-level mode.
By default, the security plugin detects if a DLS query contains a TLQ or not and chooses the appropriate mode automatically at runtime.
@ -145,5 +145,5 @@ plugins.security.dls.mode: filter-level
| Evaluation mode | Parameter | Description | Usage |
:--- | :--- | :--- | :--- |
Lucene-level DLS | `lucene-level` | This setting makes all DLS queries apply to the Lucene level. | Lucene-level DLS modifies Lucene queries and data structures directly. This is the most efficient mode but does not allow certain advanced constructs in DLS queries, including TLQs.
Filter-level DLS | `filter-level` | This setting makes all DLS queries apply to the filter level. | In this mode, OpenSearch applies DLS by modifying queries that OpenSearch receives. This allows for TLQs in DLS queries, but you can only use the `get`, `search`, `mget`, and `msearch` operations to retrieve data from the protected index. Additionally, cross-cluster searches are limited with this mode.
Adaptive | `adaptive-level` | The default setting that allows OpenSearch to automatically choose the mode. | DLS queries without TLQs are executed in Lucene-level mode, while DLS queries that contain a TLQ are executed in filter-level mode.
Filter-level DLS | `filter-level` | This setting makes all DLS queries apply to the filter level. | In this mode, OpenSearch applies DLS by modifying queries that OpenSearch receives. This allows for term-level lookup queries in DLS queries, but you can only use the `get`, `search`, `mget`, and `msearch` operations to retrieve data from the protected index. Additionally, cross-cluster searches are limited with this mode.
Adaptive | `adaptive-level` | The default setting that allows OpenSearch to automatically choose the mode. | DLS queries without TLQs are executed in Lucene-level mode, while DLS queries that contain TLQ are executed in filter- level mode.

BIN
images/cluster.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 24 KiB

After

Width:  |  Height:  |  Size: 26 KiB