Telemetry-Airflow

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

This repository codifies the Airflow cluster that is deployed at workflow.telemetry.mozilla.org (behind SSO) and commonly referred to as "WTMO" or simply "Airflow".

Some links relevant to users and developers of WTMO:

• The dags directory in this repository contains some custom DAG definitions
• Many of the DAGs registered with WTMO don't live in this repository, but are instead generated from ETL task definitions in bigquery-etl
• The Data SRE team maintains a WTMO Developer Guide (behind SSO)

Prerequisites

This app is built and deployed with docker and docker-compose. Dependencies are managed with pip-tools pip-compile.

You'll also need to install MySQL to build the database container.

Installing dependencies locally

⚠ Make sure you use the right Python version. Refer to Dockerfile for current supported Python Version ⚠

You can install the project dependencies locally to run tests with Pytest. We use the official Airflow constraints file to simplify Airflow dependency management. Install dependencies locally using the following command:

make pip-install-local

Updating Python dependencies

Add new Python dependencies into requirements.in and execute make pip-install-local

Build Container

Build Airflow image with

make build

Local Deployment

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

make build
make up

macOS

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

Testing

Adding dummy credentials

Tasks often require credentials to access external credentials. For example, one may choose to store API keys in an Airflow connection or variable. These variables are sure to exist in production but are often not mirrored locally for logistical reasons. Providing a dummy variable is the preferred way to keep the local development environment up to date.

Update the resources/dev_variables.env and resources/dev_connections.env with appropriate strings to prevent broken workflows.

Usage

You can now connect to your local Airflow web console at http://localhost:8080/.

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". You'll likely want to toggle the DAG back to "Off" as soon as your desired task starts running.

Testing GKE Jobs (including BigQuery-etl changes)

See https://go.corp.mozilla.com/wtmodev for more details.

make build && make up
make gke

When done:
make clean-gke

From there, connect to Airflow and enable your job.

Testing Dataproc Jobs

Dataproc jobs run on a self-contained Dataproc cluster, created by Airflow.

To test these, jobs, you'll need a sandbox account and corresponding service account. For information on creating that, see "Testing GKE Jobs". Your service account will need Dataproc and GCS permissions (and BigQuery, if you're connecting to it). Note: Dataproc requires "Dataproc/Dataproc Worker" as well as Compute Admin permissions. You'll need to ensure that the Dataproc API is enabled in your sandbox project.

Ensure that your dataproc job has a configurable project to write to. Set the project in the DAG entry to be configured based on development environment; see the ltv.py job for an example of that.

From there, run the following:

make build && make up
./bin/add_gcp_creds $GOOGLE_APPLICATION_CREDENTIALS google_cloud_airflow_dataproc

You can then connect to Airflow locally. Enable your DAG and see that it runs correctly.

Production Setup

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
• AIRFLOW_DATABASE_URL -- The connection URI for the Airflow database, e.g. mysql://username:password@hostname:port/database
• 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

Also, please set

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

Debugging

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)

# Purge docker volumes (helps with mysql container failing to start)
# Careful as this will purge all local volumes not used by at least one container.
docker volume prune

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
    • 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.