updating data prepper documentation for 1.2 release
Signed-off-by: Christopher Manning <cmanning09@users.noreply.github.com>
This commit is contained in:
Christopher Manning
Manning
parent
05b1682f11
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2c2d7ffc67
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@ -1,14 +1,13 @@
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---
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layout: default
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title: Configuration reference
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parent: Trace analytics
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nav_order: 25
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parent: Data Prepper
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nav_order: 3
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---
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# Data Prepper configuration reference
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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}}/observability-plugins/trace/data-prepper/).
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This page lists all supported Data Prepper server, sources, buffers, preppers, and sinks, along with their associated options. For example configuration files, see [Data Prepper]({{site.url}}{{site.baseurl}}/observability-plugins/data-prepper/pipelines/).
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## Data Prepper server options
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@ -19,7 +18,7 @@ keyStoreFilePath | No | String, path to a .jks or .p12 keystore file. Required i
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keyStorePassword | No | String, password for keystore. Optional, defaults to empty string.
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privateKeyPassword | No | String, password for private key within keystore. Optional, defaults to empty string.
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serverPort | No | Integer, port number to use for server APIs. Defaults to 4900
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metricRegistries | No | List, metrics registries for publishing the generated metrics. Defaults to Prometheus; Prometheus and CloudWatch are currently supported.
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## General pipeline options
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@ -53,7 +52,20 @@ sslKeyFile | Conditionally | String, file-system path or AWS S3 path to the secu
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useAcmCertForSSL | No | Boolean, enables TLS/SSL using certificate and private key from AWS Certificate Manager (ACM). Default is `false`.
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acmCertificateArn | Conditionally | String, represents the ACM certificate ARN. ACM certificate take preference over S3 or local file system certificate. Required if `useAcmCertForSSL` is set to `true`.
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awsRegion | Conditionally | String, represents the AWS region to use ACM or S3. Required if `useAcmCertForSSL` is set to `true` or `sslKeyCertChainFile` and `sslKeyFile` are AWS S3 paths.
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authentication | No | An authentication configuration. By default, this runs an unauthenticated server. This uses pluggable authentication for HTTPS. To provide customer authentication use or create a plugin which implements: [GrpcAuthenticationProvider](https://github.com/opensearch-project/data-prepper/blob/main/data-prepper-plugins/armeria-common/src/main/java/com/amazon/dataprepper/armeria/authentication/GrpcAuthenticationProvider.java)
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### http_source
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This is a source plugin that supports HTTP protocol. Currently ONLY support Json UTF-8 codec for incoming request, e.g. `[{"key1": "value1"}, {"key2": "value2"}]`.
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Option | Required | Description
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:--- | :--- | :---
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port | No | Integer, the port the source is running on. Default is `2021`. Valid options are between `0` and `65535`.
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request_timeout | No | Integer, the request timeout in millis. Default is `10_000`.
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thread_count | No | Integer, the number of threads to keep in the ScheduledThreadPool. Default is `200`.
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max_connection_count | No | Integer, the maximum allowed number of open connections. Default is `500`.
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max_pending_requests | No | Ingeger, the maximum number of allowed tasks in ScheduledThreadPool work queue. Default is `1024.
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authentication | No | An authentication configuration. By default, this runs an unauthenticated server. This uses pluggable authentication for HTTPS. To provide customer authentication use or create a plugin which implements: [ArmeriaHttpAuthenticationProvider](https://github.com/opensearch-project/data-prepper/blob/main/data-prepper-plugins/armeria-common/src/main/java/com/amazon/dataprepper/armeria/authentication/ArmeriaHttpAuthenticationProvider.java)
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### file
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@ -62,7 +74,8 @@ Source for flat file input.
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Option | Required | Description
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:--- | :--- | :---
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path | Yes | String, path to the input file (e.g. `logs/my-log.log`).
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format | No | String, format of each line in the file. Valid options are `json` or `plain`. Default is `plain`.
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record_type | No | String, the record type that will be stored. Valid options are `string` or `event`. Default is `string`. If you would like to use the file source for log analytics use cases like grok, change this to `event`.
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### pipeline
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@ -144,6 +157,22 @@ Option | Required | Description
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:--- | :--- | :---
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upper_case | No | Boolean, whether to convert to uppercase (`true`) or lowercase (`false`).
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### grok_prepper
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Takes unstructured data and utilizes pattern matching to structure and extract important keys and make data more structured and queryable.
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Option | Required | Description
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:--- | :--- | :---
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match | No | Map, specifies which keys to match specific patterns against. Default is `{}`.
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keep_empty_captures | No | Boolean, enables preserving `null` captures. Defatul value is `false`.
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named_captures_only | No | Boolean, enables whether to keep only named captures. Default vaule is `true`.
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break_on_match | No | Boolean, specifies wether to match all patterns or stop once the first successful match is found. Default is `true`.
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keys_to_overwrite | No | List, specifies which existing keys are to be overwritten if there is a capture with the same key value. Default is `[]`.
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pattern_definitions | No | Map, that allows for custom pattern use inline. Default value is `{}`.
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patterns_directories | No | List, specifies the path of directories that contain customer pattern files. Default value is `[]`.
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pattern_files_glob | No | String, specifies which pattern files to use from the directories specified for `pattern_directories`. Default is `*`.
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target_key | No | String, specifies a parent level key to store all captures. Default value is `null`.
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timeout_millis | Integer, the maximum amount of time that matching will be performed. Setting to `0` will disable the timeout. Default value is `30,000`.
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## Sinks
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@ -0,0 +1,58 @@
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---
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layout: default
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title: Get Started
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parent: Data Prepper
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nav_order: 1
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---
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# Get started with Data Prepper
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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.
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## Install Data Prepper
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To use the Docker image, pull it like any other image:
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```bash
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docker pull opensearchproject/data-prepper:latest
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```
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## Define a pipeline
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Build a pipeline by creating a pipeline configuration YAML using any of the built-in Data Prepper plugins.
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For more examples and details on pipeline configurations see [Pipelines]({{site.url}}{{site.baseurl}}/observability-plugins/data-prepper/pipelines) guide.
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## Start Data Prepper
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Run the following command with your pipeline configuration YAML.
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```bash
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docker run --name data-prepper -v /full/path/to/pipelines.yaml:/usr/share/data-prepper/pipelines.yaml opensearchproject/opensearch-data-prepper:latest
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```
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### Migrating from Logstash
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Data Prepper supports Logstash configuration files for a limited set of plugins. Simply use the logstash config to run Data Prepper.
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```bash
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docker run --name data-prepper -v /full/path/to/logstash.conf:/usr/share/data-prepper/pipelines.conf opensearchproject/opensearch-data-prepper:latest
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```
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This feature is limited by feature parity of Data Prepper. As of Data Prepper 1.2 release, the following plugins from the Logstash configuration are supported:
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- HTTP Input plugin
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- Grok Filter plugin
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- Elasticsearch Output plugin
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- Amazon Elasticsearch Output plugin
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### Configure the Data Prepper server
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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 has a TLS configuration and is specified by a separate YAML file. Data Prepper docker images secures these endpoints by default. We strongly recommend providing your own configuration file for securing production environments. Example:
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```yml
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ssl: true
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keyStoreFilePath: "/usr/share/data-prepper/keystore.jks"
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keyStorePassword: "password"
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privateKeyPassword: "other_password"
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serverPort: 1234
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```
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@ -0,0 +1,15 @@
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---
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layout: default
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title: Data Prepper
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nav_order: 80
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has_children: true
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has_toc: false
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---
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# Data Prepper
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Data Prepper is a server side data collector with abilities to filter, enrich, transform, normalize and aggregate data for downstream analytics and visualization.
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Data Prepper allows users to build custom pipelines to improve their operational view of their own applications. Two common uses for Data Prepper are trace and log analytics. [Trace Anaytics]({{site.url}}{{site.baseurl}}/observability-plugins/trace/index/) can help you visualize this flow of events and identify performance problems. [Log Anayltics]({{site.url}}{{site.baseurl}}/observability-plugins/log-analytics/index) can improve searching, analyzing and provide insights into your application.
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To get started building your own custom pipelines with Data Prepper, see the [Get Started]({{site.url}}{{site.baseurl}}/observability-plugins/data-prepper/get-started/) guide.
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@ -1,27 +1,11 @@
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---
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layout: default
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title: Data Prepper
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parent: Trace analytics
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nav_order: 20
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title: Pipelines
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parent: Data Prepper
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nav_order: 2
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---
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# Data Prepper
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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.
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## Install Data Prepper
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To use the Docker image, pull it like any other image:
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```bash
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docker pull opensearchproject/data-prepper:latest
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```
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Otherwise, [download](https://opensearch.org/downloads.html) the appropriate archive for your operating system and unzip it.
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## Configure pipelines
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# Pipelines
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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:
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@ -61,7 +45,39 @@ sample-pipeline:
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- Sinks define where your data goes. In this case, the sink is an OpenSearch cluster.
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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:
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## Examples
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This section provides some pipeline examples that you can use to start creating your own pipelines. For more information, see [Data Prepper configuration reference]({{site.url}}{{site.baseurl}}/observability-plugins/data-prepper/data-prepper-reference/) guide.
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The Data Prepper repository has several [sample applications](https://github.com/opensearch-project/data-prepper/tree/main/examples) to help you get started.
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### Log ingestion pipeline
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The following example demonstrates how to use HTTP source and Grok prepper plugins to process unstructured log data.
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```yaml
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log-pipeline:
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source:
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http:
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ssl: false
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processor:
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- grok:
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match:
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log: [ "%{COMMONAPACHELOG}" ]
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sink:
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- opensearch:
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hosts: [ "https://opensearch:9200" ]
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insecure: true
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username: admin
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password: admin
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index: apache_logs
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```
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Note: This example uses weak security. We strongly recommend securing all plugins which open external ports in production environments.
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### Trace Analytics pipeline
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The following example demonstrates how to build a pipeline that supports the [Trace Analytics OpenSearch Dashboards plugin]({{site.url}}{{site.baseurl}}/observability-plugins/trace/ta-dashboards/). This pipeline takes data from the OpenTelemetry Collector and uses two other pipelines as sinks. These two separate pipelines index trace and the service map documents for the dashboard plugin.
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```yml
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entry-pipeline:
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@ -104,32 +120,3 @@ service-map-pipeline:
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password: "ta-password"
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trace_analytics_service_map: true
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```
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To learn more, see the [Data Prepper configuration reference]({{site.url}}{{site.baseurl}}/observability-plugins/trace/data-prepper-reference/).
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## Configure the Data Prepper server
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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:
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```yml
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ssl: true
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keyStoreFilePath: "/usr/share/data-prepper/keystore.jks"
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keyStorePassword: "password"
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privateKeyPassword: "other_password"
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serverPort: 1234
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```
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## Start Data Prepper
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**Docker**
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```bash
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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
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```
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**macOS and Linux**
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```bash
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./data-prepper-tar-install.sh config/pipelines.yaml config/data-prepper-config.yaml
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```
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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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@ -0,0 +1,95 @@
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---
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layout: default
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title: Log analytics
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nav_order: 70
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---
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# Log Ingestion
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Log ingestion provides a way to transform unstructured log data into a structure data and ingestion into OpenSearch. This data can help improve your log collection in distributed applications.
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Structured log data allows for improved queries and filtering based on the data format when you are searching logs for an event.
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## Get started with log ingestion
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OpenSearch Log Ingestion consists of three components---[Data Prepper]({{site.url}}{{site.baseurl}}/observability-plugins/data-prepper/index/), [OpenSearch]({{site.url}}{{site.baseurl}}/) and [OpenSearch Dashboards]({{site.url}}{{site.baseurl}}/)---that fit into the OpenSearch ecosystem. The Data Prepper repository has several [sample applications](https://github.com/opensearch-project/data-prepper/tree/main/examples) to help you get started.
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### Basic flow of data
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1. Log Ingestion relies on you adding log collection to your application's environment to gather and send log data.
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(In the [example](#example) below, [FluentBit](https://docs.fluentbit.io/manual/) is used as a log collector that collects log data from a file and sends the log data to Data Prepper).
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2. [Data Prepper]({{site.url}}{{site.baseurl}}/observability-plugins/data-prepper/index/) receives the log data, transforms the data into a structure format, and indexes it on an OpenSearch cluster.
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3. The data can then be explored through OpenSearch search queries or the **Discover** page in OpenSearch Dashboards.
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### Example
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This example mimics the writing of log entries to a log file that are then processed by Data Prepper and stored in OpenSearch.
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Download or clone the [Data Prepper repository](https://github.com/opensearch-project/data-prepper). Then navigate to `examples/log-ingestion/` and open `docker-compose.yml` in a text editor. This file contains a container for:
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- [Fluent Bit](https://docs.fluentbit.io/manual/) (`fluent-bit`)
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- Data Prepper (`data-prepper`)
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- A single-node OpenSearch cluster (`opensearch`)
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- OpenSearch Dashboards (`opensearch-dashboards`).
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Close the file and run `docker-compose up --build` to start the containers.
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After the containers start, your ingestion pipeline is set up and ready to ingest log data. The `fluent-bit` container is configured to read log data from `test.log`. Run the following command to generate log data to send to the log ingestion pipeline.
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```
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echo '63.173.168.120 - - [04/Nov/2021:15:07:25 -0500] "GET /search/tag/list HTTP/1.0" 200 5003' >> test.log
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```
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Fluent-Bit will collect the log data and send it to Data Prepper:
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```angular2html
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[2021/12/02 15:35:41] [ info] [output:http:http.0] data-prepper:2021, HTTP status=200
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200 OK
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```
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Data Prepper will process the log and index it:
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```
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2021-12-02T15:35:44,499 [log-pipeline-processor-worker-1-thread-1] INFO com.amazon.dataprepper.pipeline.ProcessWorker - log-pipeline Worker: Processing 1 records from buffer
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```
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This should result in a single document being written to the OpenSearch cluster in the `apache-logs` index as defined in the `log_pipeline.yaml` file.
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Run the following command to see one of the raw documents in the OpenSearch cluster:
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```bash
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curl -X GET -u 'admin:admin' -k 'https://localhost:9200/apache_logs/_search?pretty&size=1'
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```
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The response should show the parsed log data:
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```
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"hits" : [
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{
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"_index" : "apache_logs",
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"_type" : "_doc",
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"_id" : "yGrJe30BgI2EWNKtDZ1g",
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"_score" : 1.0,
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"_source" : {
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"date" : 1.638459307042312E9,
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"log" : "63.173.168.120 - - [04/Nov/2021:15:07:25 -0500] \"GET /search/tag/list HTTP/1.0\" 200 5003",
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"request" : "/search/tag/list",
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"auth" : "-",
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"ident" : "-",
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"response" : "200",
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"bytes" : "5003",
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"clientip" : "63.173.168.120",
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"verb" : "GET",
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"httpversion" : "1.0",
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"timestamp" : "04/Nov/2021:15:07:25 -0500"
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}
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}
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]
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```
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The same data can be viewed in OpenSearch Dashboards by visiting the **Discover** page and searching the `apache_logs` index. Remember, you must create the index in OpensSearch Dashboards if this is your first time searching for the index.
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@ -9,7 +9,6 @@ nav_order: 1
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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.
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## Basic flow of data
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@ -20,11 +19,10 @@ OpenSearch Trace Analytics consists of two components---Data Prepper and the Tra
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1. The [OpenTelemetry Collector](https://opentelemetry.io/docs/collector/getting-started/) receives data from the application and formats it into OpenTelemetry data.
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||||
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1. [Data Prepper]({{site.url}}{{site.baseurl}}/observability-plugins/trace/data-prepper/) processes the OpenTelemetry data, transforms it for use in OpenSearch, and indexes it on an OpenSearch cluster.
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||||
1. [Data Prepper]({{site.url}}{{site.baseurl}}/observability-plugins/data-prepper/index/) processes the OpenTelemetry data, transforms it for use in OpenSearch, and indexes it on an OpenSearch cluster.
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||||
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||||
1. The [Trace Analytics OpenSearch Dashboards plugin]({{site.url}}{{site.baseurl}}/observability-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.
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||||
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## Jaeger HotROD
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||||
One Trace Analytics sample application is the Jaeger HotROD demo, which mimics the flow of data through a distributed application.
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@ -0,0 +1 @@
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Reference in New Issue