[jira] [Created] (SPARK-28761) spark.driver.maxResultSize only applies to compressed data

["David Vogelbacher (JIRA)" <ji...@apache.org>]

David Vogelbacher created SPARK-28761:
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             Summary: spark.driver.maxResultSize only applies to compressed data
                 Key: SPARK-28761
                 URL: https://issues.apache.org/jira/browse/SPARK-28761
             Project: Spark
          Issue Type: Improvement
          Components: Spark Core
    Affects Versions: 3.0.0
            Reporter: David Vogelbacher


Spark has a setting `spark.driver.maxResultSize`, see https://spark.apache.org/docs/latest/configuration.html#application-properties :
{noformat}
Limit of total size of serialized results of all partitions for each Spark action (e.g. collect) in bytes. Should be at least 1M, or 0 for unlimited. Jobs will be aborted if the total size is above this limit. Having a high limit may cause out-of-memory errors in driver (depends on spark.driver.memory and memory overhead of objects in JVM). Setting a proper limit can protect the driver from out-of-memory errors.
{noformat}
This setting can be very useful in constraining the memory that the spark driver needs for a specific spark action. However, this limit is checked before decompressing data in https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala#L662

Even if the compressed data is below the limit the uncompressed data can still be far above. In order to protect the driver we should also impose a limit on the uncompressed data. We could do this in https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala#L344
I propose adding a new config option {{spark.driver.maxUncompressedResultSize}}.

A simple repro of this with spark shell:
{noformat}
> printf 'a%.0s' {1..100000} > test.csv # create a 100 MB file
> ./bin/spark-shell --conf "spark.driver.maxResultSize=10000"
scala> val df = spark.read.format("csv").load("/Users/dvogelbacher/test.csv")
df: org.apache.spark.sql.DataFrame = [_c0: string]

scala> val results = df.collect()
results: Array[org.apache.spark.sql.Row] = Array([aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa...

scala> results(0).getString(0).size
res0: Int = 100000
{noformat}

Even though we set maxResultSize to 10 MB, we collect a result that is 100MB uncompressed.



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