AWS Glue & Dagster Pipes#

This tutorial gives a short overview on how to use Dagster Pipes with AWS Glue.

The dagster-aws integration library provides the PipesGlueClient resource which can be used to launch AWS Glue jobs from Dagster assets and ops. Dagster can receive regular events like logs, asset checks, or asset materializations from jobs launched with this client. Using it requires minimal code changes on the job side.

Prerequisites#

In the orchestration environment, you'll need to:
- Install the following packages:
```
pip install dagster dagster-webserver dagster-aws
```
  Refer to the Dagster installation guide for more info.
- AWS authentication credentials configured. If you don't have this set up already, refer to the boto3 quickstart.
In AWS:
- An existing AWS account
- An AWS Glue job with a Python 3.8+ runtime environment

Step 1: Provide the dagster-pipes module#

Provide the dagster-pipes module to the AWS Glue job either by installing it in the Glue job environment or packaging it along with the job script.

Step 2: Add dagster-pipes to the Glue job#

Call open_dagster_pipes in the Glue job script to create a context that can be used to send messages to Dagster:


import boto3
from dagster_pipes import (
    PipesCliArgsParamsLoader,
    PipesS3ContextLoader,
    open_dagster_pipes,
)

client = boto3.client("s3")
context_loader = PipesS3ContextLoader(client)
params_loader = PipesCliArgsParamsLoader()


def main():
    with open_dagster_pipes(
        context_loader=context_loader,
        params_loader=params_loader,
    ) as pipes:
        pipes.log.info("Hello from AWS Glue job!")
        pipes.report_asset_materialization(
            metadata={"some_metric": {"raw_value": 0, "type": "int"}},
            data_version="alpha",
        )


if __name__ == "__main__":
    main()

Step 3: Add the PipesGlueClient to Dagster code#

In the Dagster asset/op code, use the PipesGlueClient resource to launch the job:


import os

import boto3
from dagster_aws.pipes import PipesGlueClient

from dagster import AssetExecutionContext, asset


@asset
def glue_pipes_asset(
    context: AssetExecutionContext, pipes_glue_client: PipesGlueClient
):
    return pipes_glue_client.run(
        context=context,
        start_job_run_params={
            "JobName": "Example Job",
            "Arguments": {"some_parameter": "some_value"},
        },
    ).get_materialize_result()

This will launch the AWS Glue job and monitor its status until it either fails or succeeds. A job failure will also cause the Dagster run to fail with an exception.

Step 4: Create Dagster definitions#

Next, add the PipesGlueClient resource to your project's Definitions object:


from dagster import Definitions  # noqa
from dagster_aws.pipes import PipesS3ContextInjector, PipesCloudWatchMessageReader


bucket = os.environ["DAGSTER_GLUE_S3_CONTEXT_BUCKET"]


defs = Definitions(
    assets=[glue_pipes_asset],
    resources={
        "pipes_glue_client": PipesGlueClient(
            client=boto3.client("glue"),
            context_injector=PipesS3ContextInjector(
                client=boto3.client("s3"),
                bucket=bucket,
            ),
            message_reader=PipesCloudWatchMessageReader(client=boto3.client("logs")),
        )
    },
)

Dagster will now be able to launch the AWS Glue job from the glue_pipes_asset asset.

By default, the client uses the CloudWatch log stream (.../output/<job-run-id>) created by the Glue job to receive Dagster events. The client will also forward the stream to stdout.