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https://issues.apache.org/jira/browse/SPARK-59824?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Yicong Huang reassigned SPARK-59824:
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Assignee: Tengfei Huang
> Guard against oversized rows during pickle Python UDF input serialization
> -------------------------------------------------------------------------
>
> Key: SPARK-59824
> URL: https://issues.apache.org/jira/browse/SPARK-59824
> Project: Spark
> Issue Type: Task
> Components: PySpark
> Affects Versions: 4.2.0
> Reporter: Tengfei Huang
> Assignee: Tengfei Huang
> Priority: Major
> Labels: pull-request-available
>
> h3. Problem
> For non-Arrow Python UDFs, Spark evaluates the UDF input expressions and
> serializes the projected row using Java conversion followed by pickle
> serialization.
> A single row can contain a very large string or binary value. Because
> batching cannot split an individual row, converting and pickling it may
> create several large in-memory copies and cause an executor OOM before the
> row reaches the Python worker.
> Existing batch-size limits do not protect against this case.
> h3. Proposed change
> Add an optional row-size guard for pickle-based Python UDFs.
> The guard should:
> * Run after UDF input expressions are projected, so computed values such as
> {{{}cast{}}}, {{{}concat{}}}, and {{substring}} are measured.
> * Run before Java conversion and pickle serialization.
> * Estimate the payload size of supported top-level variable-width arguments,
> initially strings and binary values.
> * Fail with a structured error when the estimated size exceeds a
> configurable threshold based on executor heap.
> * Introduce no per-row overhead when disabled or when the projected schema
> has no supported variable-width arguments.
> Nested arrays, maps, and structs are intentionally excluded initially to
> avoid a recursive traversal of every input value.
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