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Posted to issues@spark.apache.org by "colin fang (JIRA)" <ji...@apache.org> on 2019/04/30 17:31:00 UTC
[jira] [Commented] (SPARK-17859) persist should not impede with
spark's ability to perform a broadcast join.
[ https://issues.apache.org/jira/browse/SPARK-17859?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16830504#comment-16830504 ]
colin fang commented on SPARK-17859:
------------------------------------
The above case works for me in v2.4
{code:java}
spark.conf.set("spark.sql.autoBroadcastJoinThreshold", 0)
df_large = spark.range(1e6)
df_small = F.broadcast(spark.range(10).coalesce(1)).cache()
df_large.join(df_small, "id").explain()
== Physical Plan ==
*(2) Project [id#0L]
+- *(2) BroadcastHashJoin [id#0L], [id#2L], Inner, BuildRight
:- *(2) Range (0, 1000000, step=1, splits=4)
+- BroadcastExchange HashedRelationBroadcastMode(List(input[0, bigint, false]))
+- *(1) InMemoryTableScan [id#2L]
+- InMemoryRelation [id#2L], StorageLevel(disk, memory, deserialized, 1 replicas)
+- Coalesce 1
+- *(1) Range (0, 10, step=1, splits=4)
{code}
However, I have definitely seen cases where `F.broadcast` is ignored for cached dataframe. (I am unable to find a minimal example though.)
> persist should not impede with spark's ability to perform a broadcast join.
> ---------------------------------------------------------------------------
>
> Key: SPARK-17859
> URL: https://issues.apache.org/jira/browse/SPARK-17859
> Project: Spark
> Issue Type: Bug
> Components: Optimizer
> Affects Versions: 2.0.0
> Environment: spark 2.0.0 , Linux RedHat
> Reporter: Franck Tago
> Priority: Major
>
> I am using Spark 2.0.0
> My investigation leads me to conclude that calling persist could prevent broadcast join from happening .
> Example
> Case1: No persist call
> var df1 =spark.range(1000000).select($"id".as("id1"))
> df1: org.apache.spark.sql.DataFrame = [id1: bigint]
> var df2 =spark.range(1000).select($"id".as("id2"))
> df2: org.apache.spark.sql.DataFrame = [id2: bigint]
> df1.join(df2 , $"id1" === $"id2" ).explain
> == Physical Plan ==
> *BroadcastHashJoin [id1#117L], [id2#123L], Inner, BuildRight
> :- *Project [id#114L AS id1#117L]
> : +- *Range (0, 1000000, splits=2)
> +- BroadcastExchange HashedRelationBroadcastMode(List(input[0, bigint, false]))
> +- *Project [id#120L AS id2#123L]
> +- *Range (0, 1000, splits=2)
> Case 2: persist call
> df1.persist.join(df2 , $"id1" === $"id2" ).explain
> 16/10/10 15:50:21 WARN CacheManager: Asked to cache already cached data.
> == Physical Plan ==
> *SortMergeJoin [id1#3L], [id2#9L], Inner
> :- *Sort [id1#3L ASC], false, 0
> : +- Exchange hashpartitioning(id1#3L, 10)
> : +- InMemoryTableScan [id1#3L]
> : : +- InMemoryRelation [id1#3L], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas)
> : : : +- *Project [id#0L AS id1#3L]
> : : : +- *Range (0, 1000000, splits=2)
> +- *Sort [id2#9L ASC], false, 0
> +- Exchange hashpartitioning(id2#9L, 10)
> +- InMemoryTableScan [id2#9L]
> : +- InMemoryRelation [id2#9L], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas)
> : : +- *Project [id#6L AS id2#9L]
> : : +- *Range (0, 1000, splits=2)
> Why does the persist call prevent the broadcast join .
> My opinion is that it should not .
> I was made aware that the persist call is lazy and that might have something to do with it , but I still contend that it should not .
> Losing broadcast joins is really costly.
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