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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2018/11/06 16:10:00 UTC
[jira] [Assigned] (SPARK-25951) Redundant shuffle if column is
renamed
[ https://issues.apache.org/jira/browse/SPARK-25951?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-25951:
------------------------------------
Assignee: (was: Apache Spark)
> Redundant shuffle if column is renamed
> --------------------------------------
>
> Key: SPARK-25951
> URL: https://issues.apache.org/jira/browse/SPARK-25951
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.3.0
> Reporter: Ohad Raviv
> Priority: Minor
>
> we've noticed that sometimes a column rename causes extra shuffle:
> {code:java}
> val N = 1 << 12
> spark.sql("set spark.sql.autoBroadcastJoinThreshold=0")
> val t1 = spark.range(N).selectExpr("floor(id/4) as key1")
> val t2 = spark.range(N).selectExpr("floor(id/4) as key2")
> import org.apache.spark.sql.functions._
> t1.groupBy("key1").agg(count(lit("1")).as("cnt1"))
> .join(t2.groupBy("key2").agg(count(lit("1")).as("cnt2")).withColumnRenamed("key2", "key3"),
> col("key1")===col("key3"))
> .explain()
> {code}
> results in:
> {noformat}
> == Physical Plan ==
> *(6) SortMergeJoin [key1#6L], [key3#22L], Inner
> :- *(2) Sort [key1#6L ASC NULLS FIRST], false, 0
> : +- *(2) HashAggregate(keys=[key1#6L], functions=[count(1)], output=[key1#6L, cnt1#14L])
> : +- Exchange hashpartitioning(key1#6L, 2)
> : +- *(1) HashAggregate(keys=[key1#6L], functions=[partial_count(1)], output=[key1#6L, count#39L])
> : +- *(1) Project [FLOOR((cast(id#4L as double) / 4.0)) AS key1#6L]
> : +- *(1) Filter isnotnull(FLOOR((cast(id#4L as double) / 4.0)))
> : +- *(1) Range (0, 4096, step=1, splits=1)
> +- *(5) Sort [key3#22L ASC NULLS FIRST], false, 0
> +- Exchange hashpartitioning(key3#22L, 2)
> +- *(4) HashAggregate(keys=[key2#10L], functions=[count(1)], output=[key3#22L, cnt2#19L])
> +- Exchange hashpartitioning(key2#10L, 2)
> +- *(3) HashAggregate(keys=[key2#10L], functions=[partial_count(1)], output=[key2#10L, count#41L])
> +- *(3) Project [FLOOR((cast(id#8L as double) / 4.0)) AS key2#10L]
> +- *(3) Filter isnotnull(FLOOR((cast(id#8L as double) / 4.0)))
> +- *(3) Range (0, 4096, step=1, splits=1)
> {noformat}
> I was able to track it down to this code in class HashPartitioning:
> {code:java}
> case h: HashClusteredDistribution =>
> expressions.length == h.expressions.length && expressions.zip(h.expressions).forall {
> case (l, r) => l.semanticEquals(r)
> }
> {code}
> the semanticEquals returns false as it compares key2 and key3 eventhough key3 is just a rename of key2
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