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Asset-triggered Dag runs cause scheduler memory spikes

apache/airflow#69848·46930·Python·69 天未动·0 条评论·上游最近活跃 ·池内状态:可认领
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### Under which category would you file this issue? Ariflow Core ### Apache Airflow version `main` ### What happened and how to reproduce it? When the scheduler creates an asset-triggered Dag run, it selects every `AssetEvent` in the event window as a full ORM object. This materializes each event's JSON metadata and relationship state in the scheduler process before writing rows to `dagrun_asset_event`. The scheduler's peak memory therefore grows linearly with the number of events. A production-path benchmark using a 512-byte JSON payload per event produced the following baseline results: | Database | Events | Duration | Python peak | RSS increase | |---|---:|---:|---:|---:| | SQLite | 1,000 | 0.537 s | 6.23 MB | 0.17 MB | | SQLite | 10,000 | 1.785 s | 54.24 MB | 79.98 MB | | SQLite | 50,000 | 12.545 s | 274.21 MB | 465.98 MB | | PostgreSQL | 1,000 | 0.339 s | 7.39 MB | 1.88 MB | | PostgreSQL | 10,000 | 2.510 s | 62.34 MB | 108.78 MB | | PostgreSQL | 50,000 | 10.046 s | 289.72 MB | 483.77 MB | | MySQL | 1,000 | 2.622 s | 6.76 MB | 0.75 MB | | MySQL | 10,000 | 4.478 s | 54.54 MB | 98.21 MB | | MySQL | 50,000 | 21.502 s | 272.63 MB | 475.28 MB | Reproduction outline: 1. Create an asset-scheduled Dag with `catchup=False`. 2. Insert 1,000, 10,000, or 50,000 matching `AssetEvent` rows containing a 512-byte JSON value. 3. Add the corresponding `AssetDagRunQueue` row. 4. Invoke `SchedulerJobRunner._create_dag_runs_asset_triggered`. 5. Measure elapsed time, Python allocations, and scheduler RSS during Dag run creation. Fixture creation is outside the measured interval. Every run creates the expected number of `dagrun_asset_event` associations. ### What you think should happen instead? Dag run creation should preserve exact consumed-event membership without materializing complete `AssetEvent` objects in scheduler memory. The association can be written as a set-based `INSERT ... SELECT` in the existing transaction, using the same direct-asset, alias, and event-window predicat…
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