val threshold = spark.conf.get("spark.sql.autoBroadcastJoinThreshold").toInt
scala> threshold / 1024 / 1024
res0: Int = 10
val q = spark.range(100).as("a").join(spark.range(100).as("b")).where($"a.id" === $"b.id")
scala> println(q.queryExecution.logical.numberedTreeString)
00 'Filter ('a.id = 'b.id)
01 +- Join Inner
02 :- SubqueryAlias a
03 : +- Range (0, 100, step=1, splits=Some(8))
04 +- SubqueryAlias b
05 +- Range (0, 100, step=1, splits=Some(8))
scala> println(q.queryExecution.sparkPlan.numberedTreeString)
00 BroadcastHashJoin [id#0L], [id#4L], Inner, BuildRight
01 :- Range (0, 100, step=1, splits=8)
02 +- Range (0, 100, step=1, splits=8)
scala> q.explain
== Physical Plan ==
*BroadcastHashJoin [id#0L], [id#4L], Inner, BuildRight
:- *Range (0, 100, step=1, splits=8)
+- BroadcastExchange HashedRelationBroadcastMode(List(input[0, bigint, false]))
+- *Range (0, 100, step=1, splits=8)
spark.conf.set("spark.sql.autoBroadcastJoinThreshold", -1)
scala> spark.conf.get("spark.sql.autoBroadcastJoinThreshold")
res1: String = -1
scala> q.explain
== Physical Plan ==
*SortMergeJoin [id#0L], [id#4L], Inner
:- *Sort [id#0L ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(id#0L, 200)
: +- *Range (0, 100, step=1, splits=8)
+- *Sort [id#4L ASC NULLS FIRST], false, 0
+- ReusedExchange [id#4L], Exchange hashpartitioning(id#0L, 200)
// Force BroadcastHashJoin with broadcast hint (as function)
val qBroadcast = spark.range(100).as("a").join(broadcast(spark.range(100)).as("b")).where($"a.id" === $"b.id")
scala> qBroadcast.explain
== Physical Plan ==
*BroadcastHashJoin [id#14L], [id#18L], Inner, BuildRight
:- *Range (0, 100, step=1, splits=8)
+- BroadcastExchange HashedRelationBroadcastMode(List(input[0, bigint, false]))
+- *Range (0, 100, step=1, splits=8)
// Force BroadcastHashJoin using SQL's BROADCAST hint
// Supported hints: BROADCAST, BROADCASTJOIN or MAPJOIN
val qBroadcastLeft = """
SELECT /*+ BROADCAST (lf) */ *
FROM range(100) lf, range(1000) rt
WHERE lf.id = rt.id
"""
scala> sql(qBroadcastLeft).explain
== Physical Plan ==
*BroadcastHashJoin [id#34L], [id#35L], Inner, BuildRight
:- *Range (0, 100, step=1, splits=8)
+- BroadcastExchange HashedRelationBroadcastMode(List(input[0, bigint, false]))
+- *Range (0, 1000, step=1, splits=8)
val qBroadcastRight = """
SELECT /*+ MAPJOIN (rt) */ *
FROM range(100) lf, range(1000) rt
WHERE lf.id = rt.id
"""
scala> sql(qBroadcastRight).explain
== Physical Plan ==
*BroadcastHashJoin [id#42L], [id#43L], Inner, BuildRight
:- *Range (0, 100, step=1, splits=8)
+- BroadcastExchange HashedRelationBroadcastMode(List(input[0, bigint, false]))
+- *Range (0, 1000, step=1, splits=8)Broadcast Joins (aka Map-Side Joins)
Spark SQL uses broadcast join (aka broadcast hash join) instead of hash join to optimize join queries when the size of one side data is below spark.sql.autoBroadcastJoinThreshold.
Broadcast join can be very efficient for joins between a large table (fact) with relatively small tables (dimensions) that could then be used to perform a star-schema join. It can avoid sending all data of the large table over the network.
You can use broadcast function or SQL’s broadcast hints to mark a dataset to be broadcast when used in a join query.
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Note
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According to the article Map-Side Join in Spark, broadcast join is also called a replicated join (in the distributed system community) or a map-side join (in the Hadoop community). |
CanBroadcast object matches a LogicalPlan with output small enough for broadcast join.
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Note
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Currently statistics are only supported for Hive Metastore tables where the command ANALYZE TABLE [tableName] COMPUTE STATISTICS noscan has been run.
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JoinSelection execution planning strategy uses spark.sql.autoBroadcastJoinThreshold property (default: 10M) to control the size of a dataset before broadcasting it to all worker nodes when performing a join.