Fix OpenLineage jobDependencies run ID after upstream task is cleared

[FrankYang0529]

Why

• Asset-triggered Dag runs emit a jobDependencies facet. For each asset event that triggered the Dag run, the facet lists the task and the OpenLineage run ID of the try that emitted the asset event. The run ID is built from the try number.
• _extract_ol_info_from_asset_event takes the try number from AssetEvent.source_task_instance. That relationship looks up the task_instance row by Dag, run, task and map index. The row only holds the latest try, and the asset event does not record which try emitted it.
• The facet is rebuilt for the START, COMPLETE and FAIL events of the Dag run. For example, try 1 of a task emits an asset event, and that asset event triggers a Dag run. Before that Dag run finishes, the task is cleared and scheduled again as try 2. From then on, every event of that Dag run points at try 2, even though try 1 emitted the asset event. If the Dag run is already running when the task is cleared, its START event points at try 1 and its COMPLETE event points at try 2.

How

• DagRun.schedule_tis sets try_number and scheduled_dttm in the same update.
• If the source task instance was scheduled at or before the event, its current try emitted the event. In that case the run ID is unchanged and no extra query runs.
• If the source task instance was scheduled after the event, a new helper looks up the highest try in TaskInstanceHistory that was scheduled at or before the event.

Verification

• Unit test: TZ=UTC uv run --frozen --project providers/openlineage pytest providers/openlineage/tests/unit
• Integration test:

1. Setup

uv sync --frozen --no-dev --package apache-airflow-providers-openlineage --package apache-airflow-providers-standard
npx -y pnpm@10.28.1 -C airflow-core/src/airflow/ui install --frozen-lockfile
npx -y pnpm@10.28.1 -C airflow-core/src/airflow/ui build
mkdir -p /tmp/ol-job-dependencies-demo/dags
cat > /tmp/ol-job-dependencies-demo/dags/orders.py <<'EOF'
import time
from pathlib import Path

import pendulum

from airflow.sdk import DAG, Asset, task

orders = Asset("demo://warehouse/orders")

with DAG(
    dag_id="orders_producer",
    schedule=None,
    start_date=pendulum.datetime(2025, 1, 1, tz="UTC"),
    is_paused_upon_creation=False,
):

    @task(outlets=[orders])
    def build_orders():
        return "orders table rebuilt"

    build_orders()

with DAG(
    dag_id="orders_report",
    schedule=[orders],
    start_date=pendulum.datetime(2025, 1, 1, tz="UTC"),
    is_paused_upon_creation=False,
):

    @task
    def make_report():
        while not Path("/tmp/ol-job-dependencies-demo/finish").exists():
            time.sleep(1)

    make_report()
EOF

cat > /tmp/ol-job-dependencies-demo/airflow.env <<'EOF'
AIRFLOW_HOME=/tmp/ol-job-dependencies-demo/airflow-home
AIRFLOW__CORE__DAGS_FOLDER=/tmp/ol-job-dependencies-demo/dags
AIRFLOW__CORE__LOAD_EXAMPLES=False
AIRFLOW__CORE__SIMPLE_AUTH_MANAGER_ALL_ADMINS=True
AIRFLOW__OPENLINEAGE__TRANSPORT='{"type": "file", "log_file_path": "/tmp/ol-job-dependencies-demo/airflow-home/ol_events.jsonl", "append": true}'
EOF

2. Start Airflow

set -a; . /tmp/ol-job-dependencies-demo/airflow.env; PATH="$PWD/.venv/bin:$PATH"; exec airflow standalone

3. Run the Dag

Trigger the Dag:

curl -s -X POST "http://localhost:8080/api/v2/dags/orders_producer/dagRuns" -H 'Content-Type: application/json' -d '{"logical_date": null}' | jq -c '{dag_run_id, state}'

Check the state of build_orders:

curl -s "http://localhost:8080/api/v2/dags/orders_producer/dagRuns/~/taskInstances" | jq -c '.task_instances[] | {task_id, try_number, state}'

Clear build_orders once it is success:

curl -s -X POST "http://localhost:8080/api/v2/dags/orders_producer/clearTaskInstances" -H 'Content-Type: application/json' -d '{"dry_run": false, "only_failed": false, "task_ids": ["build_orders"]}' | jq -c '.task_instances[] | {task_id, state}'

Let orders_report finish:

touch /tmp/ol-job-dependencies-demo/finish

Check both orders_report runs are success:

curl -s "http://localhost:8080/api/v2/dags/orders_report/dagRuns" | jq -c '.dag_runs[] | {dag_run_id, state}'

4. Check result

jq -r 'select(.job.name == "orders_producer.build_orders" and .eventType == "COMPLETE") | "build_orders try \(.run.facets.airflow.taskInstance.try_number): run \(.run.runId)"' /tmp/ol-job-dependencies-demo/airflow-home/ol_events.jsonl
jq -r 'select(.job.name == "orders_report" and .eventType == "COMPLETE") | .run.facets.jobDependencies.upstream[] | "asset event \(.airflow.asset_events[0].asset_event_id): upstream run \(.run.runId)"' /tmp/ol-job-dependencies-demo/airflow-home/ol_events.jsonl | sort

On main, asset event 1 shows the run ID of try 2:

build_orders try 1: run 01a0aa0b-f137-7d33-b737-24e810559276
build_orders try 2: run 01a0aa0b-f137-7f1d-975b-87e1216ea322

asset event 1: upstream run 01a0aa0b-f137-7f1d-975b-87e1216ea322
asset event 2: upstream run 01a0aa0b-f137-7f1d-975b-87e1216ea322

On this branch, asset event 1 shows the run ID of try 1:

build_orders try 1: run 01a0a9d4-61ca-7dc3-b577-be0c3728e983
build_orders try 2: run 01a0a9d4-61ca-7716-92a2-1a0f5372c4d4

asset event 1: upstream run 01a0a9d4-61ca-7dc3-b577-be0c3728e983
asset event 2: upstream run 01a0a9d4-61ca-7716-92a2-1a0f5372c4d4

---

Was generative AI tooling used to co-author this PR?

• Yes - Claude Code

---

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