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packages/dataflow

The purpose of this package is to simplify the development of Dataflow workflows.

See the modules in common for some generally useful abstractions.

You'll notice that workflows are included in this package. Workflows are located here to simplify job execution, as all non-standard and non-beam modules must be packaged together for job execution.

See Scheduling a Dataflow workflow for more information on automating a workflow you'd like to run regularly.

It's possible that you may only care about running your pipeline locally. In that case, you can simply import the common modules.

Note that Dataflow supports continuous pipelines. Chrome Operations hasn't experimented with these yet, but they are worth exploring!

References

Beam Docs

Workflow Testing

There are a couple requirements to testing your Dataflow workflow.

First, you must activate the infra Python environment. Assuming you have that set up already, run source ENV/bin/activate from the root of your infra checkout. If you need to set up or update your environment, see bootstrap/README for more info.

Next, you must have Google Storage buckets to pass with the --staging_location and --temp_location options. The name is not important, but for example you could use gs://my-dataflow-job/staging. Create these if you don't have them already.

Next, you must have permission within the project to schedule a Dataflow job, and be authenticated to do so.

To check your authentication status, ensure that you have the Cloud SDK installed, then run:

gcloud info

If you don't see the correct project id, reach out to an editor of that project to request access.

Finally, run the command below to test your workflow. Note: Job names should match the regular expression [a-z]([-a-z0-9]{0,38}[a-z0-9]).

python <path-to-dataflow-job> --job_name <pick-a-job-name> \
--project <project> --runner DataflowRunner \
--setup_file <infra-checkout-path>/packages/dataflow/setup.py \
--staging_location <staging bucket> \
--temp_location <temp bucket> --save_main_session

Navigate to the Dataflow console in your browser and you should see your job running. Wait until it succeeds.

Running the test will leave behind a directory, packages/dataflow/dataflow.egg-info, that you must manually clean up.

Updating the package

Jobs scheduled with the remote_execute_dataflow_workflow recipe use the version of the job at HEAD but the version of the package pinned in bootstrap/deps.pyl. So, if you make a breaking change to the package, submit the update first (which will automatically be picked up by the package mirror), then submit the change to the job along with the ref update in deps.pyl together in one commit. Be sure to follow the instructions in bootstrap/README.md to build and upload the new wheel before submitting the change to deps.pyl.

Limits

Please see the Dataflow docs for the most up to date information on quotas and limits.

At the time of writing, there are limits on Dataflow requests per minute, number of GCE instances (--numWorkers), number of concurrent jobs, monitoring requests, job creation request size, and number of side input shards.

Some of these limits are per user, others are per project, others are per organization. In general, users and project owners are responsible for ensuring they do not hit user and project limits. Currently these limits are, in general, not restrictive for us, and we are not concerned about hitting them.

Interaction with other cloud services

If you run a job on GCE, GCE limits apply. If you use BigQuery as a source or sink, BigQuery limits apply. Project owners should monitor usage and be mindful of these limits.

BigQuery

The relevant limits are query and insert limits. Query and insert limits can be found in the Dataflow documentation. As query and insert rates are highly project-specific, project owners should be responsible for monitoring this limit.