airflow-run 0.4.9

Simplified Airflow CLI Tool for Lauching CeleryExecutor Deployment

Airflow Run

Python tool for deploying Airflow Multi-Node Cluster.

Requirements

• Python >=3.6 (tested)

Goal

To provide a quick way to setup Airflow Multi-Node Cluster (a.k.a. Celery Executor Setup).

Steps

1. Generate config yaml file.
2. Run commands to start webserver, scheduler, worker, (rabbitmq, postgres).
3. Add dag files and run initdb.

Generate config file:

afr --generate_config

Running the tool in the same directory as config file:

afr --run postgresql
afr --run initdb
afr --run rabbitmq
afr --run webserver
afr --run scheduler
afr --run worker --queue {queue name}
afr --run flower

Or, running the tool specifying config path:

afr --run postgresql --config /path/config.yaml

Or, use this environment variable to set the config path:

export AIRFLOWRUN_CONFIG_PATH="/some_path/config.yaml"

After running webserver, scheduler and worker (postgres and rabbitmq if needed local instances), Add your dag files in the dags subdirectory in the directory you defined in the config file.

(* note: make sure you have the correct user permission in the dags, logs subdirectories.)

That is it!!

Default Config yaml file:

private_registry: False
registry_url: registry.hub.docker.com
username: ""
password: ""
repository: pkuong/airflow-run
image: airflow-run
tag: latest
local_dir: {local directory where you want to mount /dags and /logs folder}
webserver_port: 8000
flower_port: 5555
custom_mount_volumes: []
env:
  AIRFLOW__CORE__EXECUTOR: CeleryExecutor
  AIRFLOW__CORE__LOAD_EXAMPLES: "False"
  AIRFLOW__CORE__DAGS_FOLDER: /usr/local/airflow/airflow/dags
  AIRFLOW__CORE__LOGS_FOLDER: /usr/local/airflow/airflow/logs
  AIRFLOW_HOME: /usr/local/airflow
  AIRFLOW__CORE__FERNET_KEY: ""
rabbitmq:
  name: rabbitmq
  username: {username}
  password: {password}
  host: {IP}
  virtual_host: /
  image: rabbitmq:3-management
  home: /var/lib/rabbitmq
  ui_port: 15672
  port: 5672
  env:
    RABBITMQ_DEFAULT_USER: {username}
    RABBITMQ_DEFAULT_PASS: {password}
postgresql:
  name: postgresql
  username: {username}
  password: {password}
  host: {host}
  image: postgres
  data: /var/lib/postgresql/data
  port: 5432
  env:
    PGDATA: /var/lib/postgresql/data/pgdata
    POSTGRES_USER: {username}
    POSTGRES_PASSWORD: {password}

Custom mount volumes

You can specify custom mount volumes in the container, for example:

custom_mount_volumes:
  - host_path: /Users/bob/.aws
    container_path: /usr/local/airflow/.aws

Docker image

This tool is using the following public docker image by default.

https://hub.docker.com/repository/docker/pkuong/airflow-run

Building the image:

If you want to build your own image, you can run the following:

afd --build --config_path={absolute path to config.yaml} --dockerfile_path={absolute path to directory which contains Dockerfile}

Contributors

• Paulo Kuong (@pkuong)