feat: capture write-time materialization metrics (local engine)#371
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Manisha4 wants to merge 2 commits into
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feat: capture write-time materialization metrics (local engine)#371Manisha4 wants to merge 2 commits into
Manisha4 wants to merge 2 commits into
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Add a write-time metrics capture layer for materialization (EAPC-22385), local compute engine + Cassandra first. Off by default; gated behind the ENABLE_MATERIALIZATION_METRICS env var. - MaterializationMetricsAggregator (feast/_materialization_metrics.py): accumulates rows_read_offline, rows_written_online, drop_reasons, fields_written, field_null_counts, and max_event_timestamp/lag_seconds. Invariant: rows_read - rows_written == rows_dropped == sum(drop_reasons). - ExecutionContext carries an optional metrics_collector; instantiated in ComputeEngine.get_execution_context only for a MaterializationTask when the env gate is on. - Local nodes populate it: LocalSourceReadNode (rows_read), LocalFilterNode (filter drops), LocalDedupNode (dedup drops), LocalOutputNode (rows_written, fields, null counts, freshness). All no-ops when the collector is absent. - The online store reaches the aggregator via a ContextVar the output node binds around the write (collecting()), so online_write_batch's signature is unchanged. Cassandra records ttl_expired / ttl_exceeds_max at its TTL skip points, best-effort. - Unit tests for the aggregator and the node hooks, incl. the contextvar store-drop seam. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…olume metrics (EAPC-22385) Extend the write-time materialization metrics collector to the Spark compute engine and add the remaining Layer-1 volume metrics. - Spark path: MaterializationStatsAccumulatorParam merges per-executor stats (via the pure, commutative merge_stats) back to the driver; map_in_arrow builds a per-partition aggregator bound through collecting() so the online store's TTL-skip drops are captured on the executor. - merge_from_dict folds the accumulator result into the driver-side collector. - Volume metrics: bytes_written (Arrow nbytes, both engines) and distinct_entity_keys (exact via pyarrow group_by on the local engine; left unset on Spark to avoid a second DAG-re-executing pass -- HLL follow-up). - Layer-1 -> job bridge (record_run_result / drain_run_results) so the materialization job can drain write-time stats after store.materialize(). - Env-gated (ENABLE_MATERIALIZATION_METRICS), no-op when disabled. Tests: collector merge/volume/bridge + Spark accumulator path (pyspark-guarded). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Add a write-time metrics capture layer for materialization (EAPC-22385), local compute engine + Cassandra first. Off by default; gated behind the ENABLE_MATERIALIZATION_METRICS env var.
What this PR does / why we need it:
Which issue(s) this PR fixes:
Misc