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https://issues.apache.org/jira/browse/SPARK-57024?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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L. C. Hsieh resolved SPARK-57024.
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Fix Version/s: 4.3.0
Resolution: Fixed
Issue resolved by pull request 56072
[https://github.com/apache/spark/pull/56072]
> Use bulk fill APIs to materialize RLE runs in Parquet vectorized reader
> -----------------------------------------------------------------------
>
> Key: SPARK-57024
> URL: https://issues.apache.org/jira/browse/SPARK-57024
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 4.3.0
> Reporter: L. C. Hsieh
> Assignee: L. C. Hsieh
> Priority: Minor
> Labels: pull-request-available
> Fix For: 4.3.0
>
>
> `VectorizedRleValuesReader` materializes RLE runs of nulls and definition
> levels with degenerate per-element loops:
> // VectorizedRleValuesReader.java
> for (int k = 0; k < runLen; k++) {
> nulls.putNull(valueOff + k);
> }
> for (int k = 0; k < runLen; k++) {
> defLevels.putInt(levelIdx + k, runValue);
> }
> `WritableColumnVector` already exposes the bulk equivalents
> `putNulls(rowId, count)` and `putInts(rowId, count, value)`. Switching the
> callers to these bulk APIs lets the implementations use intrinsics — but
> the bulk implementations themselves were also degenerate byte-by-byte
> loops, so the win only materializes once both sides are fixed:
> - `OnHeapColumnVector.putNulls` -> `Arrays.fill(byte[], ..., (byte) 1)`
> - `OnHeapColumnVector.putInts(rowId, count, value)`
> -> `Arrays.fill(int[], ..., value)`
> - `OffHeapColumnVector.putNulls` -> `Platform.setMemory(addr, (byte) 1,
> count)`
> `Arrays.fill` is a JIT intrinsic backed by `_jbyte_fill` / `_jint_fill`
> stubs, and `Unsafe.setMemory` lowers to a native memset; both are
> significantly faster than the unrolled-by-JIT byte/int loops they replace
> for run lengths above a handful of elements.
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