Temporal vs Airflow: Choosing the Right Self-Hosted Orchestration Engine

Temporal vs Airflow: Which Should You Self-Host?

Temporal and Apache Airflow are multi-container orchestration platforms requiring at least 4GB of RAM each for self-hosting. While Airflow excels at scheduling batch data pipelines via Python DAGs, Temporal provides durable execution for stateful application logic that must survive infrastructure failures.

Why This Matters

Engineering teams often conflate data orchestration with application workflow management, leading to significant performance bottlenecks and architectural friction. Using a batch-oriented scheduler like Airflow for real-time application logic introduces unacceptable latency due to periodic DAG parsing, while building data pipelines in Temporal sacrifices access to the 70+ pre-built connectors available in the mature Airflow provider ecosystem. Understanding the distinction between durable execution and batch scheduling is critical for maintaining sub-second latency in distributed systems versus managing complex ETL backfills.

Key Insights

• Temporal provides sub-second task dispatch latency and supports 10,000+ workflow starts per second, whereas Airflow is limited to hundreds of DAG runs per minute.
• Airflow manages 10-year-old industry-standard DAGs with native support for date-range backfills and historical data reprocessing via 70+ provider packages.
• Temporal ensures durability by persisting workflow state through failures, allowing code to run for months or years using deterministic replay and workflow versioning.
• Self-hosting Temporal requires a server, database, UI, and custom-built worker processes that contain the specific workflow logic in Go, Java, Python, or TypeScript.
• Airflow’s self-hosted architecture is significantly complex, typically involving 5+ services including an API server, scheduler, DAG processor, and Celery workers plus Redis and PostgreSQL.

Working Examples

Temporal development setup using Docker Compose with PostgreSQL backend.

services:
  temporal:
    image: temporalio/auto-setup:1.29.3
    ports:
      - "7233:7233"
    depends_on:
      - postgresql
    environment:
      - DB=postgresql
      - DB_PORT=5432
      - POSTGRES_USER=temporal
      - POSTGRES_PWD=temporal
      - POSTGRES_SEEDS=postgresql
      - DYNAMIC_CONFIG_FILE_PATH=config/dynamicconfig/development-sql.yaml
    restart: unless-stopped
  temporal-ui:
    image: temporalio/ui:2.36.2
    ports:
      - "8080:8080"
    environment:
      - TEMPORAL_ADDRESS=temporal:7233
      - TEMPORAL_CORS_ORIGINS=http://localhost:3000
    depends_on:
      - temporal
    restart: unless-stopped
  postgresql:
    image: postgres:16-alpine
    environment:
      POSTGRES_USER: temporal
      POSTGRES_PASSWORD: temporal
    volumes:
      - temporal_db:/var/lib/postgresql/data
    restart: unless-stopped

Practical Applications

• Use Case: Implementing saga patterns for distributed transactions in Temporal to handle compensation logic across microservices like payment processing and order fulfillment.
• Pitfall: Attempting human-in-the-loop interactions in Airflow via sensors, which results in resource-intensive and clunky polling mechanisms compared to Temporal’s native signals.
• Use Case: Orchestrating daily ETL/ELT pipelines with Airflow to leverage its mature ecosystem of Spark, dbt, Snowflake, and BigQuery providers.
• Pitfall: Deploying long-running user onboarding flows in Airflow, which is an anti-pattern as DAG runs are expected to complete promptly and do not persist state across failures.

References:

• https://dev.to/selfhostingsh/temporal-vs-airflow-which-should-you-self-host-31c7