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https://issues.apache.org/jira/browse/SPARK-40045?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-40045:
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Assignee: (was: Apache Spark)
> The order of filtering predicates is not reasonable
> ---------------------------------------------------
>
> Key: SPARK-40045
> URL: https://issues.apache.org/jira/browse/SPARK-40045
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.1.2, 3.2.0, 3.3.0
> Reporter: caican
> Priority: Major
>
> {code:java}
> select id, data FROM testcat.ns1.ns2.table
> where id =2
> and md5(data) = '8cde774d6f7333752ed72cacddb05126'
> and trim(data) = 'a' {code}
> Based on the SQL, we currently get the filters in the following order:
> {code:java}
> // `(md5(cast(data#23 as binary)) = 8cde774d6f7333752ed72cacddb05126)) AND
> (trim(data#23, None) = a))` comes before `(id#22L = 2)`
> == Physical Plan == *(1) Project [id#22L, data#23]
> +- *(1) Filter ((((isnotnull(data#23) AND isnotnull(id#22L)) AND
> (md5(cast(data#23 as binary)) = 8cde774d6f7333752ed72cacddb05126)) AND
> (trim(data#23, None) = a)) AND (id#22L = 2))
> +- BatchScan[id#22L, data#23] class
> org.apache.spark.sql.connector.InMemoryTable$InMemoryBatchScan{code}
> In this predicate order, all data needs to participate in the evaluation,
> even if some data does not meet the later filtering criteria and it may
> causes spark tasks to execute slowly.
>
> So i think that filtering predicates that need to be evaluated should
> automatically be placed to the far right to avoid data that does not meet the
> criteria being evaluated.
>
> As shown below:
> {noformat}
> // `(id#22L = 2)` comes before `(md5(cast(data#23 as binary)) =
> 8cde774d6f7333752ed72cacddb05126)) AND (trim(data#23, None) = a))`
> == Physical Plan == *(1) Project [id#22L, data#23]
> +- *(1) Filter ((((isnotnull(data#23) AND isnotnull(id#22L)) AND
> (md5(cast(data#23 as binary)) = 8cde774d6f7333752ed72cacddb05126)) AND
> (trim(data#23, None) = a)) AND (id#22L = 2))
> +- BatchScan[id#22L, data#23] class
> org.apache.spark.sql.connector.InMemoryTable$InMemoryBatchScan{noformat}
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