Prefer Unions over Or in Spark Joins

A common anti-pattern in Spark workloads is the use of an or operator as part of a join . An example of this goes as follows:

val resultDF = dataframe . join ( anotherDF , $ "cID" === $ "customerID" || $ "cID" === $ "contactID" , "left" )

This looks straight-forward. The use of an or within the join makes its semantics easy to understand. However, we should be aware of the pitfalls of such an approach.

The declarative SQL above is resolved within Spark into a physical plan which determines how this particular query gets executed. To view the query plan for the computation, we could do:

resultDF . explain () /* pass true if you are interested in the logical plan of the query as well */ resultDF . explain ( true )

For the purpose of our discussion we will stick to just the physical plan. For a more detailed understanding of query plans within Spark, I would recommend reading: Deep Dive into Spark SQL’s Catalyst Optimizer.

In the physical plan of a join operation, Spark identifies the strategy it will use to perform the join. The most common types of join strategies are (more can be found here):

I have listed the four strategies above in the order of decreasing performance. In all cases, you do not want your joins to be resolved into a BroadcastNestedLoopJoin because it is just a fancy name for using nested for-loops to join your data-frames.

We now have enough background to understand the drawback of an or in a join clause. We will assume the data-frames in our example are of considerable size (:big-data:). Analyzing its physical plan, you will see something similar to this:

== Physical Plan == BroadcastNestedLoopJoin BuildRight, LeftOuter, ((cID#8 = customerID#23) || (cID#8 = contactID#24)) :- *(1) Project [_1#4 AS cID#8, _2#5 AS c2#9, _3#6 AS c3#10] : +- *(1) SerializeFromObject [assertnotnull(input[0, scala.Tuple3, true])._1 AS _1#4, assertnotnull(input[0, scala.Tuple3, true])._2 AS _2#5, assertnotnull(input[0, scala.Tuple3, true])._3 AS _3#6] : +- Scan[obj#3] +- BroadcastExchange IdentityBroadcastMode +- *(2) Project [_1#18 AS c1#22, _2#19 AS customerID#23, _3#20 AS contactID#24] +- *(2) SerializeFromObject [assertnotnull(input[0, scala.Tuple3, true])._1 AS _1#18, assertnotnull(input[0, scala.Tuple3, true])._2 AS _2#19, assertnotnull(input[0, scala.Tuple3, true])._3 AS _3#20] +- Scan[obj#17]

For many large workloads, a query plan involving a BroadcastNestedLoopJoin will result in an run-time exception similar to : SparkException: Cannot broadcast the table that is larger than 8GB: 10 GB

So, how do we work-around this? High-school boolean algebra to the rescue! Remember an or over two sets result in their union set. We can rewrite our example as follows:

val resultPart1 = dataframe . join ( anotherDF , $ "cID" === $ "customerID" , "left" ) val resultPart2 = dataframe . join ( anotherDF , $ "cID" === $ "contactID" , "left" ) val resultDF = resultPart1 . unionByName ( resultPart2 )

This produces the following physical plan:

== Physical Plan == Union :- SortMergeJoin [cID#8], [customerID#23], LeftOuter : :- *(2) Sort [cID#8 ASC NULLS FIRST], false, 0 : : +- Exchange hashpartitioning(cID#8, 200) : : +- *(1) Project [_1#4 AS cID#8, _2#5 AS c2#9, _3#6 AS c3#10] : : +- *(1) SerializeFromObject [assertnotnull(input[0, scala.Tuple3, true])._1 AS _1#4, assertnotnull(input[0, scala.Tuple3, true])._2 AS _2#5, assertnotnull(input[0, scala.Tuple3, true])._3 AS _3#6] : : +- Scan[obj#3] : +- *(4) Sort [customerID#23 ASC NULLS FIRST], false, 0 : +- Exchange hashpartitioning(customerID#23, 200) : +- *(3) Project [_1#18 AS c1#22, _2#19 AS customerID#23, _3#20 AS contactID#24] : +- *(3) SerializeFromObject [assertnotnull(input[0, scala.Tuple3, true])._1 AS _1#18, assertnotnull(input[0, scala.Tuple3, true])._2 AS _2#19, assertnotnull(input[0, scala.Tuple3, true])._3 AS _3#20] : +- Scan[obj#17] +- SortMergeJoin [cID#8], [contactID#24], LeftOuter :- *(6) Sort [cID#8 ASC NULLS FIRST], false, 0 : +- ReusedExchange [cID#8, c2#9, c3#10], Exchange hashpartitioning(cID#8, 200) +- *(8) Sort [contactID#24 ASC NULLS FIRST], false, 0 +- Exchange hashpartitioning(contactID#24, 200) +- *(7) Project [_1#18 AS c1#22, _2#19 AS customerID#23, _3#20 AS contactID#24] +- *(7) SerializeFromObject [assertnotnull(input[0, scala.Tuple3, true])._1 AS _1#18, assertnotnull(input[0, scala.Tuple3, true])._2 AS _2#19, assertnotnull(input[0, scala.Tuple3, true])._3 AS _3#20] +- Scan[obj#17]

As we see, the dreaded BroadcastNestedLoopJoin has been replaced by two SortMergeJoins , which has much better performance guarantees.

Also, it is important to understand why union is an efficient operation to be embraced every time we can use it. Union causes zero shuffling of data across executors; it is just a bookkeeping change for Spark.

I will leave you with a complete reproducible example so you can try this out in your notebook: