deploying-airflow

SKILL.md

Deploying Airflow

This skill covers deploying Airflow DAGs and projects to production, whether using Astro (Astronomer's managed platform) or open-source Airflow on Docker Compose or Kubernetes.

Choosing a path: Astro is a good fit for managed operations and faster CI/CD. For open-source, use Docker Compose for dev and the Helm chart for production.

Astro (Astronomer)

Astro provides CLI commands and GitHub integration for deploying Airflow projects.

Deploy Commands

Command
What It Does

astro deploy
Full project deploy — builds Docker image and deploys DAGs

astro deploy --dags
DAG-only deploy — pushes only DAG files (fast, no image build)

astro deploy --image
Image-only deploy — pushes only the Docker image (for multi-repo CI/CD)

astro deploy --dbt
dbt project deploy — deploys a dbt project to run alongside Airflow

Full Project Deploy

Builds a Docker image from your Astro project and deploys everything (DAGs, plugins, requirements, packages):

astro deploy

Use this when you've changed requirements.txt, Dockerfile, packages.txt, plugins, or any non-DAG file.

DAG-Only Deploy

Pushes only files in the dags/ directory without rebuilding the Docker image:

astro deploy --dags

This is significantly faster than a full deploy since it skips the image build. Use this when you've only changed DAG files and haven't modified dependencies or configuration.

Image-Only Deploy

Pushes only the Docker image without updating DAGs:

astro deploy --image

This is useful in multi-repo setups where DAGs are deployed separately from the image, or in CI/CD pipelines that manage image and DAG deploys independently.

dbt Project Deploy

Deploys a dbt project to run with Cosmos on an Astro deployment:

astro deploy --dbt

GitHub Integration

Astro supports branch-to-deployment mapping for automated deploys:

Map branches to specific deployments (e.g., main -> production, develop -> staging)

Pushes to mapped branches trigger automatic deploys

Supports DAG-only deploys on merge for faster iteration

Configure this in the Astro UI under Deployment Settings > CI/CD.

CI/CD Patterns

Common CI/CD strategies on Astro:

DAG-only on feature branches: Use astro deploy --dags for fast iteration during development

Full deploy on main: Use astro deploy on merge to main for production releases

Separate image and DAG pipelines: Use --image and --dags in separate CI jobs for independent release cycles

Deploy Queue

When multiple deploys are triggered in quick succession, Astro processes them sequentially in a deploy queue. Each deploy completes before the next one starts.

Reference

Astro Deploy Documentation

Open-Source: Docker Compose

Deploy Airflow using the official Docker Compose setup. This is recommended for learning and exploration — for production, use Kubernetes with the Helm chart (see below).

Prerequisites

Docker and Docker Compose v2.14.0+

The official apache/airflow Docker image

Quick Start

Download the official Airflow 3 Docker Compose file:

curl -LfO 'https://airflow.apache.org/docs/apache-airflow/stable/docker-compose.yaml'

This sets up the full Airflow 3 architecture:

Service
Purpose

airflow-apiserver
REST API and UI (port 8080)

airflow-scheduler
Schedules DAG runs

airflow-dag-processor
Parses and processes DAG files

airflow-worker
Executes tasks (CeleryExecutor)

airflow-triggerer
Handles deferrable/async tasks

postgres
Metadata database

redis
Celery message broker

Minimal Setup

For a simpler setup with LocalExecutor (no Celery/Redis), create a docker-compose.yaml:

x-airflow-common: &airflow-common
  image: apache/airflow:3  # Use the latest Airflow 3.x release
  environment: &airflow-common-env
    AIRFLOW__CORE__EXECUTOR: LocalExecutor
    AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow
    AIRFLOW__CORE__LOAD_EXAMPLES: 'false'
    AIRFLOW__CORE__DAGS_FOLDER: /opt/airflow/dags
  volumes:
    - ./dags:/opt/airflow/dags
    - ./logs:/opt/airflow/logs
    - ./plugins:/opt/airflow/plugins
  depends_on:
    postgres:
      condition: service_healthy

services:
  postgres:
    image: postgres:16
    environment:
      POSTGRES_USER: airflow
      POSTGRES_PASSWORD: airflow
      POSTGRES_DB: airflow
    volumes:
      - postgres-db-volume:/var/lib/postgresql/data
    healthcheck:
      test: ["CMD", "pg_isready", "-U", "airflow"]
      interval: 10s
      retries: 5
      start_period: 5s

  airflow-init:
    <<: *airflow-common
    entrypoint: /bin/bash
    command:
      - -c
      - |
        airflow db migrate
        airflow users create \
          --username admin \
          --firstname Admin \
          --lastname User \
          --role Admin \
          --email admin@example.com \
          --password admin
    depends_on:
      postgres:
        condition: service_healthy

  airflow-apiserver:
    <<: *airflow-common
    command: airflow api-server
    ports:
      - "8080:8080"
    healthcheck:
      test: ["CMD", "curl", "--fail", "http://localhost:8080/health"]
      interval: 30s
      timeout: 10s
      retries: 5
      start_period: 30s

  airflow-scheduler:
    <<: *airflow-common
    command: airflow scheduler

  airflow-dag-processor:
    <<: *airflow-common
    command: airflow dag-processor

  airflow-triggerer:
    <<: *airflow-common
    command: airflow triggerer

volumes:
  postgres-db-volume:

Airflow 3 architecture note: The webserver has been replaced by the API server (airflow api-server), and the DAG processor now runs as a standalone process separate from the scheduler.

Common Operations

# Start all services
docker compose up -d

# Stop all services
docker compose down

# View logs
docker compose logs -f airflow-scheduler

# Restart after requirements change
docker compose down && docker compose up -d --build

# Run a one-off Airflow CLI command
docker compose exec airflow-apiserver airflow dags list

Installing Python Packages

Add packages to requirements.txt and rebuild:

# Add to requirements.txt, then:
docker compose down
docker compose up -d --build

Or use a custom Dockerfile:

FROM apache/airflow:3  # Pin to a specific version (e.g., 3.1.7) for reproducibility
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

Update docker-compose.yaml to build from the Dockerfile:

x-airflow-common: &airflow-common
  build:
    context: .
    dockerfile: Dockerfile
  # ... rest of config

Environment Variables

Configure Airflow settings via environment variables in docker-compose.yaml:

environment:
  # Core settings
  AIRFLOW__CORE__EXECUTOR: LocalExecutor
  AIRFLOW__CORE__PARALLELISM: 32
  AIRFLOW__CORE__MAX_ACTIVE_TASKS_PER_DAG: 16

  # Email
  AIRFLOW__EMAIL__EMAIL_BACKEND: airflow.utils.email.send_email_smtp
  AIRFLOW__SMTP__SMTP_HOST: smtp.example.com

  # Connections (as URI)
  AIRFLOW_CONN_MY_DB: postgresql://user:pass@host:5432/db

Open-Source: Kubernetes (Helm Chart)

Deploy Airflow on Kubernetes using the official Apache Airflow Helm chart.

Prerequisites

A Kubernetes cluster

kubectl configured

helm installed

Installation

# Add the Airflow Helm repo
helm repo add apache-airflow https://airflow.apache.org
helm repo update

# Install with default values
helm install airflow apache-airflow/airflow \
  --namespace airflow \
  --create-namespace

# Install with custom values
helm install airflow apache-airflow/airflow \
  --namespace airflow \
  --create-namespace \
  -f values.yaml

Key values.yaml Configuration

# Executor type
executor: KubernetesExecutor  # or CeleryExecutor, LocalExecutor

# Airflow image (pin to your desired version)
defaultAirflowRepository: apache/airflow
defaultAirflowTag: "3"  # Or pin: "3.1.7"

# Git-sync for DAGs (recommended for production)
dags:
  gitSync:
    enabled: true
    repo: https://github.com/your-org/your-dags.git
    branch: main
    subPath: dags
    wait: 60  # seconds between syncs

# API server (replaces webserver in Airflow 3)
apiServer:
  resources:
    requests:
      cpu: "250m"
      memory: "512Mi"
    limits:
      cpu: "500m"
      memory: "1Gi"
  replicas: 1

# Scheduler
scheduler:
  resources:
    requests:
      cpu: "500m"
      memory: "1Gi"
    limits:
      cpu: "1000m"
      memory: "2Gi"

# Standalone DAG processor
dagProcessor:
  enabled: true
  resources:
    requests:
      cpu: "250m"
      memory: "512Mi"
    limits:
      cpu: "500m"
      memory: "1Gi"

# Triggerer (for deferrable tasks)
triggerer:
  resources:
    requests:
      cpu: "250m"
      memory: "512Mi"
    limits:
      cpu: "500m"
      memory: "1Gi"

# Worker resources (CeleryExecutor only)
workers:
  resources:
    requests:
      cpu: "500m"
      memory: "1Gi"
    limits:
      cpu: "2000m"
      memory: "4Gi"
  replicas: 2

# Log persistence
logs:
  persistence:
    enabled: true
    size: 10Gi

# PostgreSQL (built-in)
postgresql:
  enabled: true

# Or use an external database
# postgresql:
#   enabled: false
# data:
#   metadataConnection:
#     user: airflow
#     pass: airflow
#     host: your-rds-host.amazonaws.com
#     port: 5432
#     db: airflow

Upgrading

# Upgrade with new values
helm upgrade airflow apache-airflow/airflow \
  --namespace airflow \
  -f values.yaml

# Upgrade to a new Airflow version
helm upgrade airflow apache-airflow/airflow \
  --namespace airflow \
  --set defaultAirflowTag="<version>"

DAG Deployment Strategies on Kubernetes

Git-sync (recommended): DAGs are synced from a Git repository automatically

Persistent Volume: Mount a shared PV containing DAGs

Baked into image: Include DAGs in a custom Docker image

Useful Commands

# Check pod status
kubectl get pods -n airflow

# View scheduler logs
kubectl logs -f deployment/airflow-scheduler -n airflow

# Port-forward the API server
kubectl port-forward svc/airflow-apiserver 8080:8080 -n airflow

# Run a one-off CLI command
kubectl exec -it deployment/airflow-scheduler -n airflow -- airflow dags list

Related Skills

setting-up-astro-project: For initializing a new Astro project

managing-astro-local-env: For local development with astro dev

authoring-dags: For writing DAGs before deployment

testing-dags: For testing DAGs before deployment