Airflow is a platform to programmatically author, schedule and monitor workflows.

When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.

Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.

This app is built and deployed with docker and docker-compose.

An Airflow container can be built with

make build

You should then run the database migrations to complete the container initialization with

make migrate

A single task, e.g. spark, of an Airflow dag, e.g. example, can be run with an execution date, e.g. 2018-01-01, in the dev environment with:

export AWS_SECRET_ACCESS_KEY=...
export AWS_ACCESS_KEY_ID=...
make run COMMAND="test example spark 20180101"

The container will run the desired task to completion (or failure). Note that if the container is stopped during the execution of a task, the task will be aborted. In the example's case, the Spark job will be terminated.

The logs of the task can be inspected in real-time with:

docker logs -f telemetryairflow_scheduler_1

You can task logs and see cluster status on the EMR console

By default, the results will end up in the telemetry-test-bucket in S3. If your desired task depends on other views, it will expect to be able to find those results in telemetry-test-bucket too. It's your responsibility to run the tasks in correct order of their dependencies.

main_summary can be a good test case for any large changes to telemetry-batch-view, but you'll likely want to make some modifications before launching a test to keep runtime reasonable.

Edit the EMRSparkOperator definition for the main_summary task in dags/main_summary.py to add some additional parameters to the tbv_envvar dictionary:

        "channel": "nightly",   # run on smaller nightly data rather than release
        "read-mode": "aligned", # more efficient RDD splitting for small datasets

Then launch in dev as:

export AWS_SECRET_ACCESS_KEY=...
export AWS_ACCESS_KEY_ID=...
make run COMMAND="test main_summary main_summary 20180523"

To run the full main_summary DAG via local Airflow, you'll need to remove the main_summary_glue task definition and all references to it, since your local instance doesn't have the proper credentials set up to access AWS Glue. See the next section for info on how to configure a full DAG run, though this should only be needed to significant changes affecting many view definitions.

Assuming you're using macOS and Docker for macOS, start the docker service, click the docker icon in the menu bar, click on preferences and change the available memory to 4GB.

To deploy the Airflow container on the docker engine, with its required dependencies, run:

make up

You can now connect to your local Airflow web console at http://localhost:8000/.

All DAGs are paused by default for local instances and our staging instance of Airflow. In order to submit a DAG via the UI, you'll need to toggle the DAG from "Off" to "On", but be very careful to check what DAG runs are generated (Browse > DAG Runs), since it may start generating backfill runs based on the DAG's configured start date, which could get very expensive (set schedule_interval=None in your DAG definition to prevent these scheduled runs). You'll likely want to toggle the DAG back to "Off" as soon as your desired task starts running.

Users on Linux distributions will encounter permission issues with docker-compose. This is because the local application folder is mounted as a volume into the running container. The Airflow user and group in the container is set to 10001.

To work around this, replace all instances of 10001 in Dockerfile.dev with the host user id.

sed -i "s/10001/$(id -u)/g" Dockerfile.dev

Note: This only works for telemetry-batch-view and telemetry-streaming jobs

A dev changes can be run by simply changing the DEPLOY_TAG environment variable to whichever upstream branch you've pushed your local changes to.

Afterwards, you're going to need to rebuild: make build

From there, you can either set the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY in the Dockerfile and run make up to get a local UI and run from there, or you can follow the testing instructions above and use make run.

Note: the canonical reference for production environment variables lives in a private repository.

When deploying to production make sure to set up the following environment variables:

• AWS_ACCESS_KEY_ID -- The AWS access key ID to spin up the Spark clusters
• AWS_SECRET_ACCESS_KEY -- The AWS secret access key
• SPARK_BUCKET -- The AWS S3 bucket where Spark related files are stored, e.g. telemetry-spark-emr-2
• AIRFLOW_BUCKET -- The AWS S3 bucket where airflow specific files are stored, e.g. telemetry-airflow
• PUBLIC_OUTPUT_BUCKET -- The AWS S3 bucket where public job results are stored in, e.g. telemetry-public-analysis-2
• PRIVATE_OUTPUT_BUCKET -- The AWS S3 bucket where private job results are stored in, e.g. telemetry-parquet
• AIRFLOW_DATABASE_URL -- The connection URI for the Airflow database, e.g. postgres://username:password@hostname:port/password
• AIRFLOW_BROKER_URL -- The connection URI for the Airflow worker queue, e.g. redis://hostname:6379/0
• AIRFLOW_BROKER_URL -- The connection URI for the Airflow result backend, e.g. redis://hostname:6379/1
• AIRFLOW_GOOGLE_CLIENT_ID -- The Google Auth client id used for authentication.
• AIRFLOW_GOOGLE_CLIENT_SECRET -- The Google Auth client secret used for authentication.
• AIRFLOW_GOOGLE_APPS_DOMAIN -- The domain(s) to restrict Google Auth login to e.g. mozilla.com
• AIRFLOW_SMTP_HOST -- The SMTP server to use to send emails e.g. email-smtp.us-west-2.amazonaws.com
• AIRFLOW_SMTP_USER -- The SMTP user name
• AIRFLOW_SMTP_PASSWORD -- The SMTP password
• AIRFLOW_SMTP_FROM -- The email address to send emails from e.g. telemetry-alerts@workflow.telemetry.mozilla.org
• URL -- The base URL of the website e.g. https://workflow.telemetry.mozilla.org
• DEPLOY_ENVIRONMENT -- The environment currently running, e.g. stage or prod
• DEPLOY_TAG -- The tag or branch to retrieve the JAR from, e.g. master or tags. You can specify the tag or travis build exactly as well, e.g. master/42.1 or tags/v2.2.1. Not specifying the exact tag or build will use the latest from that branch, or the latest tag.
• ARTIFACTS_BUCKET -- The s3 bucket where the build artifacts can be found, e.g. net-mozaws-data-us-west-2-ops-ci-artifacts

Also, please set

• AIRFLOW_SECRET_KEY -- A secret key for Airflow's Flask based webserver
• AIRFLOW_FERNET_KEY -- A secret key to saving connection passwords in the DB

Both values should be set by using the cryptography module's fernet tool that we've wrapped in a docker-compose call:

make secret

Run this for each key config variable, and don't use the same for both!

Some useful docker tricks for development and debugging:

# Stop all docker containers:
docker stop $(docker ps -aq)

# Remove any leftover docker volumes:
docker volume rm $(docker volume ls -qf dangling=true)

• Check if the task / run you want to re-run is visible in the DAG's Tree View UI
  • For example, the main_summary DAG tree view.
  • Hover over the little squares to find the scheduled dag run you're looking for.
• If the dag run is not showing in the Dag Tree View UI (maybe deleted)
  • Browse -> Dag Runs
  • Create (you can look at another dag run of the same dag for example values too)
    • Dag Id: the name of the dag, for example, main_summary or crash_aggregates
    • Execution Date: The date the dag should have run, for example, 2018-05-14 00:00:00
    • Start Date: Some date between the execution date and "now", for example, 2018-05-20 00:00:05
    • End Date: Leave it blank
    • State: success
    • Run Id: scheduled__2018-05-14T00:00:00
    • External Trigger: unchecked
  • Click Save
  • Click on the Graph view for the dag in question. From the main DAGs view, click the name of the DAG
  • Select the "Run Id" you just entered from the drop-down list
  • Click "Go"
  • Click each element of the DAG and "Mark Success"
  • The tasks should now show in the Tree View UI
• If the dag run is showing in the DAG's Tree View UI
  • Click on the small square for the task you want to re-run
  • Uncheck the "Downstream" toggle
  • Click the "Clear" button
  • Confirm that you want to clear it
  • The task should be scheduled to run again straight away.

• SSH into the ECS container instance
• List docker containers using docker ps
• Log in to one of the docker containers using docker exec -it <container_id> bash. The web server instance is a good choice.
• Run the desired backfill command, something like $ airflow backfill main_summary -s 2018-05-20 -e 2018-05-26

• Commits to forked repo PRs will trigger CircleCI builds that build the docker container and test python dag compilation. This should pass prior to merging.
• Every commit to master or tag will trigger a CircleCI build that will build and push the container to dockerhub