Airflow Cheat Sheet

A DAG with TaskFlow

from datetime import datetime, timedelta
from airflow.sdk import dag, task          # Airflow 2.x: from airflow.decorators import dag, task

@dag(
    schedule="@daily",
    start_date=datetime(2026, 1, 1),
    catchup=False,
    default_args={"retries": 3, "retry_delay": timedelta(minutes=5)},
    tags=["orders"],
)
def orders_daily():
    @task
    def extract(ds=None):                  # ds = logical date as YYYY-MM-DD
        return f"s3://bucket/raw/dt={ds}/"

    @task
    def load(path: str):
        print("loading", path)

    load(extract())

orders_daily()

Schedules

Value	Meaning
"@daily", "@hourly"	Presets
"0 6 * * *"	Cron
None	Manual or externally triggered only

A run for an interval starts after the interval ends. Use data_interval_start, data_interval_end or ds to select data.

Retries

retries, retry_delay, retry_exponential_backoff, max_retry_delay. Only safe with idempotent tasks.

Trigger rules

all_success (default), all_done (cleanup), one_failed (alerting), none_failed.

CLI (Airflow 3)

airflow dags list
airflow dags test orders_daily 2026-10-01           # run a whole DAG locally for one date
airflow tasks test orders_daily extract 2026-10-01  # run one task, no state recorded
airflow backfill create --dag-id orders_daily --from-date 2026-09-01 --to-date 2026-09-30

In Airflow 2.x, backfills used airflow dags backfill -s START -e END dag_id.

Defaults that changed in Airflow 3

Item	Airflow 2.x	Airflow 3
TaskFlow imports	airflow.decorators	airflow.sdk
catchup default	True	False
Backfill CLI	airflow dags backfill	airflow backfill create

Rules of thumb

• Orchestrate, do not process: submit heavy work to Spark or the warehouse.
• Pass file paths or table names between tasks, not data.
• Make tasks idempotent and parameterised by the data interval.
• Set catchup explicitly.