> ## Documentation Index
> Fetch the complete documentation index at: https://docs.flare-db.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Run a Beam pipeline

> Add the FlareDB runner SDK to an Apache Beam project and submit a pipeline to a FlareDB instance.

FlareDB runs Apache Beam pipelines. You keep building your pipeline with the Beam SDK, and point it at `FlareRunner`, which submits the pipeline to a running FlareDB instance as a job.

## Prerequisites

* A running FlareDB instance. See the [Quickstart](/quickstart).
* JDK 17.
* An Apache Beam project. The examples in this guide use Gradle and Beam 2.76.0; Maven is supported as well.

## 1. Add the runner dependency

Add the `flaredb-runner` artifact to your Beam project.

<Tabs>
  <Tab title="Gradle">
    ```groovy build.gradle theme={null}
    dependencies {
        implementation 'com.flare-db:flaredb-runner:0.1.0'
    }
    ```
  </Tab>

  <Tab title="Maven">
    ```xml pom.xml theme={null}
    <dependency>
      <groupId>com.flare-db</groupId>
      <artifactId>flaredb-runner</artifactId>
      <version>0.1.0</version>
    </dependency>
    ```
  </Tab>
</Tabs>

## 2. Configure the pipeline

Set `FlareRunner` as the runner and configure the FlareDB instance and applicatio JAR:

```java WordCount.java theme={null}
WordCountPipelineOptions options =
    PipelineOptionsFactory.fromArgs(args).as(WordCountPipelineOptions.class);

options.setRunner(FlareRunner.class);
options.setJobEndpoint("127.0.0.1:8099");
options.setUberJar("build/libs/wordcount-0.1.0-all.jar");

Pipeline pipeline = Pipeline.create(options);
```

The JAR is staged to FlareDB so your pipeline code is available when the job runs. Point setUberJar to the fat JAR produced by your build.

## 3. Build the pipeline

Build a self-contained (uber) JAR containing your pipeline and its dependencies.

```bash theme={null}
./gradlew shadowJar
```

Use the build output's JAR path in `setUberJar`.

## 4. Run the pipeline

With FlareDB instance running, submit the pipeline:

```bash theme={null}
./gradlew run
```

## Pipeline options

Define your options by extending `FlarePipelineOptions`, For Example:

```java WordCountPipelineOptions.java theme={null}
public interface WordCountPipelineOptions extends FlarePipelineOptions {}
```

| Option       | Usage                                | Description                                                   |
| ------------ | ------------------------------------ | ------------------------------------------------------------- |
| Runner       | `setRunner(FlareRunner.class)`       | Pipeline runner.                                              |
| Job endpoint | `setJobEndpoint("host:port")`        | URL of the FlareDB job service. Defaults to `127.0.0.1:8099`. |
| Uber JAR     | `setUberJar("/path/to/app-all.jar")` | Path to the fat JAR staged to workers.                        |
| Job name     | `setJobName("my-job")`               | Name of the submitted job.                                    |

These options can also be provided as command-line arguments:

```bash theme={null}
./gradlew run --args="--jobEndpoint=127.0.0.1:8099 --uberJar=/path/to/app-all.jar"
```

## Pipeline logs:

// ToDo

`flare up` prints the instance ID. The `<job-id>` is generated when a job is submitted.

## Next steps

* [FlareDB I/O](/io) — read from and write to FlareDB tables from a pipeline.
* [SQL shell](/sql) — query the data your pipeline produces.