spark shuffle hash join

If spark.sql.ansi.enabled is set to true, it throws ArrayIndexOutOfBoundsException for invalid indices. As you can see, each branch of the join contains an Exchange operator that represents the shuffle (notice that Spark will not always use sort-merge join for joining two tables — to see more details about the logic that Spark is using for choosing a joining algorithm, see my other article About Joins in Spark 3.0 where we discuss it in detail). element_at(map, key) - Returns value for given key. Also, this is only supported for ‘=’ join. The function returns NULL if the key is not contained in the map and spark.sql.ansi.enabled is set to false. If spark.sql.ansi.enabled is set to true, it throws NoSuchElementException instead. ShuffleManager随着Spark的发展有两种实现的方式，分别为HashShuffleManager和SortShuffleManager，因此spark的Shuffle有Hash Shuffle和Sort Shuffle两种. As the name suggests, Hash Join is performed by first creating a Hash Table based on join_key of smaller relation and then looping over larger relation to match the hashed join_key values. In spark, Hash Join plays a role at per node level and the strategy is used to join partitions available on the node. Spark uses this limit to broadcast a relation to all the nodes in case of a join operation. The join strategy hints, namely BROADCAST, MERGE, SHUFFLE_HASH and SHUFFLE_REPLICATE_NL, instruct Spark to use the hinted strategy on each specified relation when joining them with another relation.For example, when the BROADCAST hint is used on table ‘t1’, broadcast join (either broadcast hash join or broadcast nested loop join depending … Join Strategy Hints for SQL Queries. 在Spark的版本的发展，ShuffleManager在不断迭代，变得越来越先进。 在Spark 1.2以前，默认的shuffle计 … Spark Shuffle发展史. Image by author. Hash Join.

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