You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Dongjoon Hyun (Jira)" <ji...@apache.org> on 2020/12/19 22:12:00 UTC

[jira] [Resolved] (SPARK-33850) EXPLAIN FORMATTED doesn't show the plan for subqueries if AQE is enabled

     [ https://issues.apache.org/jira/browse/SPARK-33850?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Dongjoon Hyun resolved SPARK-33850.
-----------------------------------
    Fix Version/s: 3.1.0
       Resolution: Fixed

Issue resolved by pull request 30855
[https://github.com/apache/spark/pull/30855]

> EXPLAIN FORMATTED doesn't show the plan for subqueries if AQE is enabled
> ------------------------------------------------------------------------
>
>                 Key: SPARK-33850
>                 URL: https://issues.apache.org/jira/browse/SPARK-33850
>             Project: Spark
>          Issue Type: Sub-task
>          Components: SQL
>    Affects Versions: 3.0.0, 3.0.1, 3.1.0, 3.2.0
>            Reporter: Kousuke Saruta
>            Assignee: Kousuke Saruta
>            Priority: Major
>             Fix For: 3.1.0
>
>
> When AQE is enabled, EXPLAIN FORMATTED doesn't show the plan for subqueries.
> {code}
> spark.conf.set("spark.sql.adaptive.enabled", "true")
> val df = spark.range(1, 100)
> df.createTempView("df")
> spark.sql("SELECT (SELECT min(id) AS v FROM df)").explain("FORMATTED")
> == Physical Plan ==
> AdaptiveSparkPlan (3)
> +- Project (2)
>  +- Scan OneRowRelation (1)
> (1) Scan OneRowRelation
> Output: []
> Arguments: ParallelCollectionRDD[0] at explain at <console>:24, OneRowRelation, UnknownPartitioning(0)
> (2) Project
> Output [1]: [Subquery subquery#3, [id=#20] AS scalarsubquery()#5L]
> Input: []
> (3) AdaptiveSparkPlan
> Output [1]: [scalarsubquery()#5L]
> Arguments: isFinalPlan=false
> {code}



--
This message was sent by Atlassian Jira
(v8.3.4#803005)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org