telemetry-airflow

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.

Prerequisites

This app is built and deployed with docker and ansible.

Build Container

An Airflow container can be built with

docker build -t mozdata/telemetry-airflow .

and pushed to Docker hub with

docker push mozdata/telemetry-airflow

Testing

A single task, e.g. spark, of an Airflow dag, e.g. example, can be run with an execution date, e.g. 2016-01-01, in the dev environment with:

AWS_SECRET_ACCESS_KEY=... AWS_ACCESS_KEY_ID=... \
ansible-playbook ansible/deploy_local.yml -e '@ansible/envs/dev.yml' -e "command='test example spark 20160101'"

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 files_scheduler_1

Local Deployment

Assuming you are on OS X and using docker toolbox, first create a docker machine with a sufficient amount of memory with e.g.:

docker-machine create -d virtualbox --virtualbox-memory 4096 default

If you're using OS X and the new Docker for OS X, start the docker service, click the docker icon in the tray, click on preferences and change the available memory to 4GB.

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

ansible-playbook ansible/deploy_local.yml -e '@ansible/envs/dev.yml'
echo "Airflow web console should now be running locally at http://$(docker-machine ip default):8080"

If you get a message saying "Couldn't connect to Docker daemon - you might need to run docker-machine start default.", try the following:

docker-machine start default
eval "$(docker-machine env default)"

You can now connect to your local Airflow web console with a URL like http://192.168.99.100:8080 (see above for how to identify the exact IP address).

Remote Deployment

In order to deploy Airflow to e.g. the stage environment, an ECS cluster has to be created first with at least one container instance:

ansible-playbook ansible/provision_aws.yml -e '@ansible/envs/stage.yml'

Once the ECS cluster is up and running, Airflow can be (re)deployed with:

ansible-playbook ansible/deploy_aws.yml -e '@ansible/envs/stage.yml'

Debugging

If you do a remote deploy and the changes are not reflected in the live site, you may have to remove ECS task definitions from the AWS Console and try again. To do this, go to the ECS page, click Task Definitions, click ecscompose-telemetry-airflow, then remove all the listed task definitions. After that, re-deploy and things should work as expected.

If you get an error about ecs-cli missing, follow these steps to install it.

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)

Triggering a task to re-run within the Airflow UI

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 2016-07-14 00:00:00
  - Start Date: Some date between the execution date and "now", for example 2016-07-20 00:00:05
  - End Date: Leave it blank
  - State: success
  - Run Id: scheduled__2016-07-14T00:00:00
  - 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.

Triggering backfill tasks using the CLI

SSH in to 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 webserver instance is a good choice.
Run the desired backfill command, something like $ airflow backfill main_summary -s 2016-06-20 -e 2016-06-26

whd / telemetry-airflow