Airflow Quick Start With docker-compose on AWS EC2

Airflow Quick Start With docker-compose on AWS EC2


4 min read


For quick set up and to start learning Apache Airflow, we will deploy airflow using docker-compose and running on AWS EC2

Table Of Contents

๐Ÿš€ Introduction

The docker-compose.yaml contains several service definitions: - airflow-scheduler - The scheduler monitors all tasks and DAGs, then triggers the task instances once their dependencies are complete.

  • airflow-webserver - The webserver is available at http://localhost:8080.

  • airflow-worker - The worker that executes the tasks given by the scheduler. -

  • airflow-init - The initialization service. - flower - The flower app for monitoring the environment. It is available at http://localhost:5555.

  • postgres - The database.

  • redis - The redis - broker that forwards messages from scheduler to worker.

Some directories in the container are mounted, which means that their contents are synchronized between the services and persistent.

  • ./dags - you can put your DAG files here.

  • ./logs - contains logs from task execution and scheduler.

  • ./plugins - you can put your custom plugins here.

๐Ÿš€ Additional PIP requirements

  • airflow image contains almost enough PIP packages for operating, but we still need to install extra packages such as clickhouse-driver, pandahouse and apache-airflow-providers-slack.

  • Airflow from 2.1.1 supports ENV PIPADDITIONAL_REQUIREMENTS to add additional requirements when starting all containers

_PIP_ADDITIONAL_REQUIREMENTS: 'pandahouse==0.2.7 clickhouse-driver==0.2.1 apache-airflow-providers-slack'
  • It's not recommended to use this way on production, we should build our own image which contains all necessary pip packages then push to AWS ECR

๐Ÿš€ How to build customised airflow image

  • Build your own image.

  • requirements.txt

  • Dockerfile
FROM apache/airflow:2.1.2-python3.9
COPY requirements.txt .
RUN pip install -r requirements.txt
  • docker build -t my-airlfow .

๐Ÿš€ Persistent airflow log, dags, and plugins

Not only persistent the folders but also share these between scheduler, worker and web-server

    - /mnt/airflow/dags:/opt/airflow/dags
    - /mnt/airflow/logs:/opt/airflow/logs
    - /mnt/airflow/plugins:/opt/airflow/plugins
    - /mnt/airflow/data:/opt/airflow/data

๐Ÿš€ Using git-sync to up-to-date DAGs

  • The git-sync service will poll the registered project each 10s to clone the new commit to /dags

  • We use HTTP method and Access key token to provide permission for the container

    container_name: af-gitsync
      - GIT_SYNC_DEPTH=1
      - GIT_SYNC_USERNAME=airflow
      - GIT_KNOWN_HOSTS=false
      - GIT_SYNC_DEST=repo
      - GIT_SYNC_REPO=
      - GIT_SYNC_WAIT=60
      - GIT_SYNC_TIMEOUT=120
      - GIT_SYNC_ADD_USER=true
      - GIT_SYNC_ROOT=/dags
      - GIT_SYNC_BRANCH=master
      - /mnt/airflow/dags:/dags

๐Ÿš€ How to run

  1. Initializing Environment
mkdir ./dags ./logs ./plugins
echo -e "AIRFLOW_UID=$(id -u)\nAIRFLOW_GID=0" > .env
  1. Prepare docker-compose.yaml



  1. Run airflow-init to setup airflow database
docker-compose up airflow-init
  • Running airflow - Up
docker-compose up -d

๐Ÿš€ Add airflow connectors

  • Add slack connection
airflow connections add 'airflow-slack' \
    --conn-type 'http'
    --conn-password '/T00000000/B00000000/XXXXXXXXXXXXXXXXXXXXXXXX' \
    --conn-host ''
  • We can add a connector in UI

Airflow Quick Start With docker-compose on AWS EC2

  • We can add connection through env file eg. .env in docker-compose
AIRFLOW_CONN_MY_POSTGRES_CONN: 'postgresql://airflow:airflow@postgres-pg:5432/airflow'

๐Ÿš€ Understand airflow parameters in airflow.models

  • airflow.models

  • Context

    • on_failure_callback (TaskStateChangeCallback) โ€“ a function to be called when a task instance of this task fails. a context dictionary is passed as a single parameter to this function. Context contains references to related objects to the task instance and is documented under the macros section of the API.

    • on_execute_callback (TaskStateChangeCallback) โ€“ much like the on_failure_callback except that it is executed right before the task is executed.

    • on_retry_callback (TaskStateChangeCallback) โ€“ much like the on_failure_callback except that it is executed when retries occur.

    • on_success_callback (TaskStateChangeCallback) โ€“ much like the on_failure_callback except that it is executed when the task succeeds.

    • trigger_rule (str) โ€“ defines the rule by which dependencies are applied for the task to get triggered. Options are: { all_success | all_failed | all_done | one_success | one_failed | none_failed | none_failed_or_skipped | none_skipped | dummy} default is all_success. Options can be set as string or using the constants defined in the static class airflow.utils.TriggerRule