opensearch-docs-cn/_data-prepper/getting-started.md

---
layout: default
title: Getting started
nav_order: 5
redirect_from:
  - /clients/data-prepper/get-started/
---

# Getting started with 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.

If you are migrating from Open Distro Data Prepper, visit the [Migrating from Open Distro]({{site.url}}{{site.baseurl}}/data-prepper/migrate-open-distro/) page. 

## 1. Installing Data Prepper

There are two ways to install Data Prepper:

1. Run the Docker image.
2. Build from source.

The easiest way to use Data Prepper is by running the Docker image. We suggest that you use this approach if you have [Docker](https://www.docker.com) available.

You can pull the Docker image:

```
docker pull opensearchproject/data-prepper:latest
```

If you have special requirements that require you to build from source, or if you
want to contribute, see the [Developer Guide](https://github.com/opensearch-project/data-prepper/blob/main/docs/developer_guide.md).

## 2. Configuring Data Prepper

You must configure Data Prepper with a pipeline before running it.

You will configure two files:

* `data-prepper-config.yaml`
* `pipelines.yaml`

Depending on your use case, we have a few different guides to configuring Data Prepper.

* [Trace Analytics]({{site.url}}{{site.baseurl}}/data-prepper/common-use-cases/trace-analytics/): Learn how to collect trace data and customize a pipeline that ingests and transforms that data. 
* [Log Analytics]({{site.url}}{{site.baseurl}}/data-prepper/common-use-cases/log-analytics/): Learn how to set up Data Prepper for log observability.
* [Simple Pipeline](https://github.com/opensearch-project/data-prepper/blob/main/docs/simple_pipelines.md): Learn the basics of Data Prepper pipelines with some simple configurations.

## 3. Defining a pipeline

Create a Data Prepper pipeline file, `pipelines.yaml`, with the following configuration:

```yml
simple-sample-pipeline:
  workers: 2
  delay: "5000"
  source:
    random:
  sink:
    - stdout:
```

## 4. Running Data Prepper

Run the following command with your pipeline configuration YAML.

```bash
docker run --name data-prepper \
    -v /full/path/to/pipelines.yaml:/usr/share/data-prepper/pipelines/pipelines.yaml \
    opensearchproject/data-prepper:latest
```

The preceding example pipeline configuration above demonstrates a simple pipeline with a source (`random`) sending data to a sink (`stdout`). For further detailed examples of more advanced pipeline configurations, see [Pipelines]({{site.url}}{{site.baseurl}}/clients/data-prepper/pipelines/).

After starting Data Prepper, you should see log output and some UUIDs after a few seconds:

```yml
2021-09-30T20:19:44,147 [main] INFO  com.amazon.dataprepper.pipeline.server.DataPrepperServer - Data Prepper server running at :4900
2021-09-30T20:19:44,681 [random-source-pool-0] INFO  com.amazon.dataprepper.plugins.source.RandomStringSource - Writing to buffer
2021-09-30T20:19:45,183 [random-source-pool-0] INFO  com.amazon.dataprepper.plugins.source.RandomStringSource - Writing to buffer
2021-09-30T20:19:45,687 [random-source-pool-0] INFO  com.amazon.dataprepper.plugins.source.RandomStringSource - Writing to buffer
2021-09-30T20:19:46,191 [random-source-pool-0] INFO  com.amazon.dataprepper.plugins.source.RandomStringSource - Writing to buffer
2021-09-30T20:19:46,694 [random-source-pool-0] INFO  com.amazon.dataprepper.plugins.source.RandomStringSource - Writing to buffer
2021-09-30T20:19:47,200 [random-source-pool-0] INFO  com.amazon.dataprepper.plugins.source.RandomStringSource - Writing to buffer
2021-09-30T20:19:49,181 [simple-test-pipeline-processor-worker-1-thread-1] INFO  com.amazon.dataprepper.pipeline.ProcessWorker -  simple-test-pipeline Worker: Processing 6 records from buffer
07dc0d37-da2c-447e-a8df-64792095fb72
5ac9b10a-1d21-4306-851a-6fb12f797010
99040c79-e97b-4f1d-a70b-409286f2a671
5319a842-c028-4c17-a613-3ef101bd2bdd
e51e700e-5cab-4f6d-879a-1c3235a77d18
b4ed2d7e-cf9c-4e9d-967c-b18e8af35c90
```
The remainder of this page provides examples for running Data Prepper from the Docker image. If you
built from source, refer to the [Developer Guide](https://github.com/opensearch-project/data-prepper/blob/main/docs/developer_guide.md) for more information.

However you configure your pipeline, you will run Data Prepper the same way. You run the Docker
image and supply both the `pipelines.yaml` and `data-prepper-config.yaml` files.

For Data Prepper 2.0 or later, use this command:

```
docker run --name data-prepper -p 4900:4900 -v ${PWD}/pipelines.yaml:/usr/share/data-prepper/pipelines/pipelines.yaml -v ${PWD}/data-prepper-config.yaml:/usr/share/data-prepper/config/data-prepper-config.yaml opensearchproject/data-prepper:latest
```

For Data Prepper before version 2.0, use this command:

```
docker run --name data-prepper -p 4900:4900 -v ${PWD}/pipelines.yaml:/usr/share/data-prepper/pipelines.yaml -v ${PWD}/data-prepper-config.yaml:/usr/share/data-prepper/data-prepper-config.yaml opensearchproject/data-prepper:1.x
```

Once Data Prepper is running, it will process data until it is shut down. Once you are done, shut it down with the following command:

```
curl -X POST http://localhost:4900/shutdown
```

### Additional configurations

For Data Prepper 2.0 or later, the Log4j 2 configuration file is read from `config/log4j2.properties` in the application's home directory. 
By default, it uses `log4j2-rolling.properties` in the *shared-config* directory.

For Data Prepper 1.5 or earlier, optionally add `"-Dlog4j.configurationFile=config/log4j2.properties"` to the command if you would 
like to pass a custom log4j2 properties file. If no properties file is provided, Data Prepper will default to the log4j2.properties file in the *shared-config* directory.

## Next steps

Trace Analytics is an important Data Prepper use case. If you haven't yet configured it, see the [Trace Analytics]({{site.url}}{{site.baseurl}}/data-prepper/common-use-cases/trace-analytics/).

Log ingestion is also an important Data Prepper use case. To learn more, see [Log analytics]({{site.url}}{{site.baseurl}}/data-prepper/common-use-cases/log-analytics/).

To learn how to run Data Prepper with a Logstash configuration, see [Migrating from Logstash]({{site.url}}{{site.baseurl}}/data-prepper/migrating-from-logstash-data-prepper/).

For information on how to monitor Data Prepper, see the [Monitoring]({{site.url}}{{site.baseurl}}/data-prepper/managing-data-prepper/monitoring/) page.

## Other examples

We have several other Docker [examples](https://github.com/opensearch-project/data-prepper/tree/main/examples/) that allow you to run Data Prepper in different scenarios.