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https://issues.apache.org/jira/browse/SPARK-60395?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Dongjoon Hyun resolved SPARK-60395.
-----------------------------------
Fix Version/s: 4.4.0
Resolution: Fixed
Issue resolved by pull request 59786
[https://github.com/apache/spark/pull/59786]
> Fix the batch size computation and limit check of maxBytesPerOutputBatch in
> sliced Arrow output
> -----------------------------------------------------------------------------------------------
>
> Key: SPARK-60395
> URL: https://issues.apache.org/jira/browse/SPARK-60395
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 4.2.0, 4.3.0, 4.1.3, 4.4.0
> Reporter: L. C. Hsieh
> Assignee: L. C. Hsieh
> Priority: Major
> Labels: pull-request-available
> Fix For: 4.4.0
>
>
> SliceBytesArrowOutputProcessorImpl, used when the internal config
> spark.sql.execution.arrow.maxBytesPerOutputBatch is set, has two problems in
> how it measures and compares the size of an output batch:
> 1. getBatchBytes sums FieldVector.getBufferSize of the top-level vectors into
> a Long, but for a struct vector the size is already truncated to an int
> inside Arrow: NonNullableStructVector.getBufferSize sums its children in a
> long and returns (int) buffer, and StructVector adds its validity size as an
> int. The outputs of mapInArrow, mapInPandas, applyInPandas and Python UDTFs
> are wrapped in one top-level struct vector, so a batch over 2 GiB can be
> measured as a negative size. For example, a mapInArrow UDF that yields one
> batch of three int64 columns with 2^27 rows (about 3 GiB) is measured as
> -1,006,632,960 bytes, so with maxBytesPerOutputBatch=256m the whole batch is
> output without slicing. The check on each slice can accept an oversized slice
> the same way. The size should be computed by summing the buffer sizes of the
> leaf vectors (and the validity buffers of the nested vectors) as Long.
> 2. The doc of spark.sql.execution.arrow.maxBytesPerOutputBatch says Spark
> will try to create batches with the size equal or less than the value, but
> both checks in SliceBytesArrowOutputProcessorImpl use <, so a batch whose
> size equals the limit exactly is sliced. The records processor uses <=. The
> checks or the doc should be aligned.
> 3. The shrink search in SliceBytesArrowOutputProcessorImpl.getRoot restarts
> from all remaining rows for every output batch, and each probe that starts at
> a non-zero row allocates and refills its offset buffers, so splitting one
> batch of R rows into chunks of c rows copies O(R^2/c) offsets instead of
> O(R). The first probe, slice(0, rowCount), also always fails since it
> measures the same as the root that was just checked. The search should start
> from the last row count that fit.
> 4. In the same loop, the loop ends only through return or with
> sliceBatchCount == 0, so the following if (sliceBatchCount == 0) is always
> true and the initial value of rootSlice is never read; the fallback can
> simply be root.slice(currentRowIdx, 1).
> 5. The checkValue of spark.sql.execution.arrow.maxBytesPerOutputBatch bounds
> only the upper side, while PythonArrowOutput narrows the value with .toInt,
> so a negative value below Int.MinValue, which the doc calls no limit, wraps
> to a positive limit. For example, -3g parses to -3221225472 and becomes a 1
> GiB limit, and -4294967295 becomes a 1-byte limit; if
> maxRecordsPerOutputBatch is also set, the check that both limits are not set
> at once then throws although the bytes limit is off. The value should be
> clamped before narrowing (e.g. math.max(value, 0L).toInt), or the lower side
> bounded in checkValue.
> 6. In BaseSliceArrowOutputProcessor.loadBatch, the rowCount > 0 && clause
> never changes the result, since currentRowIdx < rowCount already implies
> rowCount >= 1, and can be dropped.
> Found while reviewing SPARK-60367
> (https://github.com/apache/spark/pull/59616), which this builds on.
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