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https://issues.apache.org/jira/browse/SPARK-25306?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-25306:
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Assignee: Apache Spark
> Use cache to speed up `createFilter` in ORC
> -------------------------------------------
>
> Key: SPARK-25306
> URL: https://issues.apache.org/jira/browse/SPARK-25306
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: SQL
> Reporter: Dongjoon Hyun
> Assignee: Apache Spark
> Priority: Critical
>
> In ORC data source, `createFilter` function has exponential time complexity
> due to lack of memoization like the following. This issue aims to improve it.
> *REPRODUCE*
> {code}
> // Create and read 1 row table with 1000 columns
> sql("set spark.sql.orc.filterPushdown=true")
> val selectExpr = (1 to 1000).map(i => s"id c$i")
> spark.range(1).selectExpr(selectExpr:
> _*).write.mode("overwrite").orc("/tmp/orc")
> print(s"With 0 filters, ")
> spark.time(spark.read.orc("/tmp/orc").count)
> // Increase the number of filters
> (20 to 30).foreach { width =>
> val whereExpr = (1 to width).map(i => s"c$i is not null").mkString(" and ")
> print(s"With $width filters, ")
> spark.time(spark.read.orc("/tmp/orc").where(whereExpr).count)
> }
> {code}
> *RESULT*
> {code}
> With 0 filters, Time taken: 653 ms
>
> With 20 filters, Time taken: 962 ms
> With 21 filters, Time taken: 1282 ms
> With 22 filters, Time taken: 1982 ms
> With 23 filters, Time taken: 3855 ms
> With 24 filters, Time taken: 6719 ms
> With 25 filters, Time taken: 12669 ms
> With 26 filters, Time taken: 25032 ms
> With 27 filters, Time taken: 49585 ms
> With 28 filters, Time taken: 98980 ms // over 1 min 38 seconds
> With 29 filters, Time taken: 198368 ms // over 3 mins
> With 30 filters, Time taken: 393744 ms // over 6 mins
> {code}
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