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https://issues.apache.org/jira/browse/SPARK-59174?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Jungtaek Lim updated SPARK-59174:
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Issue Type: Bug (was: Improvement)
> State-store instance metrics are shipped in every task result
> -------------------------------------------------------------
>
> Key: SPARK-59174
> URL: https://issues.apache.org/jira/browse/SPARK-59174
> Project: Spark
> Issue Type: Bug
> Components: SQL, Structured Streaming
> Affects Versions: 4.1.0, 4.2.0, 4.3.0
> Reporter: Diveyam Mishra
> Priority: Minor
> Labels: pull-request-available
>
> Stateful streaming operators (StateStoreWriter) currently create a separate
> SQLMetric accumulator for every (state partition x instance metric x store
> name) combination upfront on the physical plan. On executors, every task
> deserializes and registers all of these accumulators with its TaskContext,
> and returns all of them in the task result payload even though only one
> partition was processed. For queries with high partition counts (e.g., 20,000
> shuffle partitions), this causes inflated task result sizes (~3 MiB/task, ~60
> GiB total across tasks), leading to spark.driver.maxResultSize failures in
> terminal ResultStages (such as RDD checkpointing and file writes) and severe
> driver memory pressure.
> For more details checkout : https://github.com/apache/spark/issues/58394
> *Proposed Solution*
> 1. Replace the upfront O(numPartitions) SQLMetric accumulators with a single
> CollectionAccumulator on StateStoreWriter, allowing each executor task to
> append only its own active partition metrics.
> 2. In StateStoreWriter.getProgress(), the driver aggregates and reports the
> top-K instance metrics directly from this single collection accumulator.
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