You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Liang-Chi Hsieh (JIRA)" <ji...@apache.org> on 2017/08/17 04:10:00 UTC
[jira] [Created] (SPARK-21759) PullupCorrelatedPredicates can
produce unresolved plan
Liang-Chi Hsieh created SPARK-21759:
---------------------------------------
Summary: PullupCorrelatedPredicates can produce unresolved plan
Key: SPARK-21759
URL: https://issues.apache.org/jira/browse/SPARK-21759
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 2.2.0
Reporter: Liang-Chi Hsieh
With the check for structural integrity proposed in SPARK-21726, I found that an optimization rule {{PullupCorrelatedPredicates}} can produce unresolved plans.
For a correlated IN query like:
{code}
Project [a#0]
+- Filter a#0 IN (list#4 [b#1])
: +- Project [c#2]
: +- Filter (outer(b#1) < d#3)
: +- LocalRelation <empty>, [c#2, d#3]
+- LocalRelation <empty>, [a#0, b#1]
{code}
After {{PullupCorrelatedPredicates}}, it produces query plan like:
{code}
'Project [a#0]
+- 'Filter a#0 IN (list#4 [(b#1 < d#3)])
: +- Project [c#2, d#3]
: +- LocalRelation <empty>, [c#2, d#3]
+- LocalRelation <empty>, [a#0, b#1]
{code}
Because the correlated predicate involves another attribute {{d#3}} in subquery, it has been pulled out and added into the {{Project}} on the top of the subquery.
When {{list}} in {{In}} contains just one {{ListQuery}}, {{In.checkInputDataTypes}} checks if the size of {{value}} expressions matches the output size of subquery. In the above example, there is only {{value}} expression and the subquery output has two attributes {{c#2, d#3}}, so it fails the check and {{In.resolved}} returns {{false}}.
We should let {{PullupCorrelatedPredicates}} produce resolved plans to pass the structural integrity check.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org