[jira] [Created] (SPARK-17972) Query planning slows down dramatically for large query plans even when sub-trees are cached

["Cheng Lian (JIRA)" <ji...@apache.org>]

Cheng Lian created SPARK-17972:
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             Summary: Query planning slows down dramatically for large query plans even when sub-trees are cached
                 Key: SPARK-17972
                 URL: https://issues.apache.org/jira/browse/SPARK-17972
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 2.0.1, 1.6.2
            Reporter: Cheng Lian
            Assignee: Cheng Lian


The following Spark shell snippet creates a series of query plans that grow exponentially. The {{i}}-th plan is created using 4 *cached* copies of the {{i - 1}}-th plan.

{code}
(0 until 6).foldLeft(Seq(1, 2, 3).toDS) { (plan, iteration) =>
  val start = System.currentTimeMillis()
  val result = plan.join(plan, "value").join(plan, "value").join(plan, "value").join(plan, "value")
  result.cache()
  System.out.println(s"Iteration $iteration takes time ${System.currentTimeMillis() - start} ms")
  result.as[Int]
}
{code}

We can see that although all plans are cached, the query planning time still grows exponentially and quickly becomes unbearable.

{noformat}
Iteration 0 takes time 9 ms
Iteration 1 takes time 19 ms
Iteration 2 takes time 61 ms
Iteration 3 takes time 219 ms
Iteration 4 takes time 830 ms
Iteration 5 takes time 4080 ms
{noformat}

Similar scenarios can be found in iterative ML code and significantly affects usability.



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