apache-airflow-dag-orchestration

Apache Airflow DAG Orchestration

Comprehensive architectural guide for authoring resilient, idempotent, and scalable data pipelines using Apache Airflow 2.x+ with TaskFlow API, custom operators, and Kubernetes/Celery executors.

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1. Core Architecture & Execution Model

1.1 Airflow Components

• Scheduler: Parses DAG files continuously, evaluates scheduling rules, monitors task state, and enqueues commands to the executor.
• Webserver: Flask/React UI displaying DAG topology, execution history, metrics, and manual triggers.
• Worker: Executes actual task instances (in CeleryWorker pods, K8s Pods, or LocalExecutor threads).
• Triggerer: Runs an asynchronous event loop (asyncio) handling Deferrable Operators (Sensors/Async tasks) to save worker slots.
• Metadata Database: PostgreSQL/MySQL database storing DAG definitions, run history, variables, connection strings, and XComs.

┌───────────────────────────────────────────────────────────────┐
│                   Airflow Scheduler                           │
└──────────────────────────────┬────────────────────────────────┘
                               │ Reads DAGs & Enqueues Tasks
                               ▼
 ┌───────────────────┐  ┌─────────────┐  ┌────────────────────┐
 │ Metadata Database │  │ Triggerer   │  │  Executor / Worker │
 └───────────────────┘  └─────────────┘  └────────────────────┘

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2. Pipeline Idempotency & Backfill Guarantees

2.1 Deterministic Execution Logic

Every DAG run MUST be deterministic for its target logical_date (data_interval_start). Never rely on system time (datetime.now()) inside execution logic.

# Idempotent SQL execution pattern using logical_date variables
SQL_IDEMPOTENT_WRITE = """
DELETE FROM analytics.daily_revenue 
WHERE metric_date = '{{ ds }}';

INSERT INTO analytics.daily_revenue (metric_date, total_revenue)
SELECT 
    DATE(transaction_time) AS metric_date,
    SUM(amount) AS total_revenue
FROM raw.transactions
WHERE transaction_time >= '{{ data_interval_start }}'
  AND transaction_time < '{{ data_interval_end }}'
GROUP BY 1;
"""