[jira] [Assigned] (SPARK-27359) Joins on some array functions can be optimized

["Apache Spark (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-27359?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-27359:
------------------------------------

    Assignee: Apache Spark

> Joins on some array functions can be optimized
> ----------------------------------------------
>
>                 Key: SPARK-27359
>                 URL: https://issues.apache.org/jira/browse/SPARK-27359
>             Project: Spark
>          Issue Type: Improvement
>          Components: Optimizer, SQL
>    Affects Versions: 3.0.0
>            Reporter: Nikolas Vanderhoof
>            Assignee: Apache Spark
>            Priority: Minor
>
> I encounter these cases frequently, and implemented the optimization manually (as shown here). If others experience this as well, perhaps it would be good to add appropriate tree transformations into catalyst. 
> h1. Case 1
> A join like this:
> {code:scala}
> left.join(
>   right,
>   arrays_overlap(left("a"), right("b"))     // Creates a cartesian product in the logical plan
> )
> {code}
> will produce the same results as:
> {code:scala}
> {
>   val leftPrime = left.withColumn("exploded_a", explode(col("a")))
>   val rightPrime = right.withColumn("exploded_b", explode(col("b")))
>   leftPrime.join(
>     rightPrime,
>     leftPrime("exploded_a") === rightPrime("exploded_b")
>       // Equijoin doesn't produce cartesian product
>   ).drop("exploded_a", "exploded_b").distinct
> }
> {code}
> h1. Case 2
> A join like this:
> {code:scala}
> left.join(
>   right,
>   array_contains(left("arr"), right("value")) // Cartesian product in logical plan
> )
> {code}
> will produce the same results as:
> {code:scala}
> {
>   val leftPrime = left.withColumn("exploded_arr", explode(col("arr")))
>   leftPrime.join(
>     right,
>     leftPrime("exploded_arr") === right("value") // Fast equijoin
>   ).drop("exploded_arr").distinct
> }
> {code}
> h1. Case 3
> A join like this:
> {code:scala}
> left.join(
>   right,
>   array_contains(right("arr"), left("value")) // Cartesian product in logical plan
> )
> {code}
> will produce the same results as:
> {code:scala}
> {
>   val rightPrime = right.withColumn("exploded_arr", explode(col("arr")))
>   left.join(
>     rightPrime,
>     left("value") === rightPrime("exploded_arr") // Fast equijoin
>   ).drop("exploded_arr").distinct
> }
> {code}



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