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[SPARK-53241][CORE] Support createArray in SparkCollectionUtils #51968

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[SPARK-53241][CORE] Support createArray in SparkCollectionUtils #51968

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@dongjoon-hyun dongjoon-hyun commented Aug 11, 2025 •
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What changes were proposed in this pull request?

This PR aims to support createArray in SparkCollectionUtils to take advantage of java.util.Arrays.fill() method which is faster than Scala Array.fill's operation.

Apache Spark uses Array.fill() many times.

$ git grep Array.fill | wc -l
     530

Like the following example, new method is much faster.

spark/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcQuerySuite.scala

Lines 870 to 873 in d2dc055

val df = spark.range(1, 1024, 1, 1).map { _ =>
val byteData = Array.fill[Byte](5 * 1024 * 1024)('X')
byteData
}.toDF() 

scala> spark.time((1 until 1024).map(Array.fill[Byte](5 * 1024 * 1024)('X')).size)
Time taken: 15 ms
val res0: Int = 1023

scala> spark.time((1 until 1024).map(org.apache.spark.util.SparkCollectionUtils.createArray[Byte](5 * 1024 * 1024, 'X')).size)
Time taken: 0 ms
val res1: Int = 1023

Why are the changes needed?

To support a better implementation.

$ bin/spark-shell --driver-memory 12G
...
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 4.1.0-SNAPSHOT
      /_/

Using Scala version 2.13.16 (OpenJDK 64-Bit Server VM, Java 21.0.8)
...
scala> spark.time(Array.fill[Byte](2_000_000_000)(7))
Time taken: 387 ms

scala> spark.time(org.apache.spark.util.SparkCollectionUtils.createArray[Byte](2_000_000_000, 7))
Time taken: 190 ms

Does this PR introduce any user-facing change?

No, this is a new utility method.

How was this patch tested?

Pass the CIs.

Was this patch authored or co-authored using generative AI tooling?

No.

@dongjoon-hyun dongjoon-hyun marked this pull request as ready for review August 11, 2025 05:23
peter-toth
peter-toth approved these changes Aug 11, 2025
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Looks good.

@dongjoon-hyun
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dongjoon-hyun commented Aug 11, 2025

Thank you for review and approval, @peter-toth !

Merged to master for Apache Spark 4.1.0.

@dongjoon-hyun dongjoon-hyun deleted the SPARK-53241 branch August 11, 2025 13:56
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