Joseph K. Bradley created SPARK-15419:
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Summary: monotonicallyIncreasingId should use less memory with
multiple partitions
Key: SPARK-15419
URL: https://issues.apache.org/jira/browse/SPARK-15419
Project: Spark
Issue Type: Improvement
Components: SQL
Affects Versions: 2.0.0
Environment: branch-2.0, 1 worker
Reporter: Joseph K. Bradley
When monotonicallyIncreasingId is used on a DataFrame with many partitions, it
uses a very large amount of memory.
Consider this code:
{code}
import org.apache.spark.sql.functions._
// JMAP1: run jmap -histo:live [PID]
val numPartitions = 1000
val df = spark.range(0, 1000000, 1, numPartitions).toDF("vtx")
df.cache().count()
// JMAP2: run jmap -histo:live [PID]
val df2 = df.withColumn("id", monotonicallyIncreasingId())
df2.cache().count()
// JMAP3: run jmap -histo:live [PID]
{code}
Here's how memory usage progresses:
* JMAP1: This is just for calibration.
* JMAP2: No significant change from 1.
* JMAP3: Massive jump: 3048895 Longs, 1039638 Objects, 2007427 Integers,
1002000 org.apache.spark.sql.catalyst.expressions.GenericInternalRow
** None of these had significant numbers of instances in JMAP1/2.
When the indexed DataFrame is used repeatedly afterwards, the driver memory
usage keeps increasing and eventually blows up in my application.
Presumably this memory usage could be reduced.
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