Optimizing joins techniques in Apache Spark

Hi Readers,

In this post you will be learning the various optimizing joins techniques that can be used in Apache Spark.

Three types of joins in Spark are

Shuffle hash join (default):

  • It is a map-reduce type join.
  • Based on output key it shuffles the datasets.
  • During reduce phase, it joins the datasets for same output key.

Broadcast hash join:

  • Use this when one dataset is small enough to fit in memory.

Cartesian join:

  • Use this when every row of one table is joined with every row of the other table.

example:
The below code snippet uses the .join() with orderDF and orderItemDF to get a joinedOrderDataDF.

scala> var joinedOrderDataDF = orderDF.join(orderItemDF,orderDF(“order_id”)===orderItemDF(“order_item_order_id”))
joinedOrderDataDF: org.apache.spark.sql.DataFrame = [order_id: int, order_date: bigint … 8 more fields]

check which execution strategy of the join has been used in joinedOrderDataDF using
scala>joinedOrderDataDF.explain —> to get which join type has been used.
scala>joinedOrderDataDF.queryExecution.executedPlan —> gives information on how the dataframe has been executed.

march2018_sparkscala-sqljoin

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