Source code for airflow.example_dags.example_asset_state

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"""
Example Dag that demonstrates using AIP-103 asset state to track a watermark across DAG runs.
The producer reads the last watermark, processes only new records, then
advances the watermark. The consumer is triggered by the asset event and
reads asset state to understand what the producer just loaded.

Asset state persists on the asset across runs — unlike task state which is
scoped to a single task instance. This replaces the common pattern of
storing watermarks in Airflow Variables, which have no asset-level scoping.
"""

from __future__ import annotations

import json
import random
from datetime import datetime, timezone

from airflow.sdk import DAG, Asset, task


[docs]
ORDERS = Asset(name="orders/daily", uri="s3://warehouse/orders/daily")



def _fetch_records(since: str) -> list[dict]:
    """Simulate fetching records newer than `since`."""
    return [{"id": i} for i in range(random.randint(100, 5_000))]


with DAG(
    dag_id="example_asset_state_producer",
    schedule=None,
    start_date=datetime(2026, 1, 1),
    catchup=False,
    tags=["example", "asset-state"],
    doc_md=__doc__,
):

    @task(inlets=[ORDERS], outlets=[ORDERS])

[docs]
    def load(asset_state=None):
        state = asset_state[ORDERS]

        # First run: watermark is None — fall back to epoch start.
        watermark = state.get("watermark") or "2026-01-01T00:00:00+00:00"
        records = _fetch_records(since=watermark)
        row_count = len(records)

        now = datetime.now(tz=timezone.utc).isoformat()
        state.set("watermark", now)
        state.set("total_runs", (state.get("total_runs") or 0) + 1)
        state.set(
            "last_run_summary",
            {
                "rows_loaded": row_count,
                "prev_watermark": watermark,
                "completed_at": now,
            },
        )

        print(f"Loaded {row_count} records. Watermark advanced to {now}.")
        return row_count


    load()


with DAG(
    dag_id="example_asset_state_consumer",
    schedule=[ORDERS],
    start_date=datetime(2026, 1, 1),
    catchup=False,
    tags=["example", "asset-state"],
):

    @task(inlets=[ORDERS])

[docs]
    def consume(asset_state=None):
        state = asset_state[ORDERS]
        summary = json.loads(state.get("last_run_summary") or "{}")
        print(
            f"Processing {summary.get('rows_loaded', '?')} rows "
            f"up to watermark {state.get('watermark')}. "
            f"Total runs so far: {state.get('total_runs')}."
        )


    consume()