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https://issues.apache.org/jira/browse/SPARK-40622?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Mridul Muralidharan resolved SPARK-40622.
-----------------------------------------
Fix Version/s: 3.4.0
Resolution: Fixed
> Result of a single task in collect() must fit in 2GB
> ----------------------------------------------------
>
> Key: SPARK-40622
> URL: https://issues.apache.org/jira/browse/SPARK-40622
> Project: Spark
> Issue Type: Bug
> Components: Spark Core, SQL
> Affects Versions: 3.3.0
> Reporter: Ziqi Liu
> Assignee: Ziqi Liu
> Priority: Major
> Fix For: 3.4.0
>
>
> when collecting results, data from single partition/task is serialized
> through byte array or ByteBuffer(which is backed by byte array as well),
> therefore it's subject to java array max size limit(in terms of byte array,
> it's 2GB).
>
> Construct a single partition larger than 2GB and collect it can easily
> reproduce the issue
> {code:java}
> // create data of size ~3GB in single partition, which exceeds the byte array
> limit
> // random gen to make sure it's poorly compressed
> val df = spark.range(0, 3000, 1, 1).selectExpr("id", s"genData(id, 1000000)
> as data")
> withSQLConf("spark.databricks.driver.localMaxResultSize" -> "4g") {
> withSQLConf("spark.sql.useChunkedBuffer" -> "true") {
> df.queryExecution.executedPlan.executeCollect()
> }
> } {code}
> will get a OOM error from
> [https://github.com/AdoptOpenJDK/openjdk-jdk11/blob/master/src/java.base/share/classes/java/io/ByteArrayOutputStream.java#L125]
>
> Consider using ChunkedByteBuffer to replace byte array in order to bypassing
> this limit
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