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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