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https://issues.apache.org/jira/browse/SPARK-23309?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16349553#comment-16349553
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Thomas Graves commented on SPARK-23309:
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[~dongjoon] is there any native way with the native hive to control the # of
partitions? (like hive.exec.orc.split.strategy). Or do you have to do the
coalesce?
> Spark 2.3 cached query performance 20-30% worse then spark 2.2
> --------------------------------------------------------------
>
> Key: SPARK-23309
> URL: https://issues.apache.org/jira/browse/SPARK-23309
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.3.0
> Reporter: Thomas Graves
> Priority: Blocker
>
> I was testing spark 2.3 rc2 and I am seeing a performance regression in sql
> queries on cached data.
> The size of the data: 10.4GB input from hive orc files /18.8 GB cached/5592
> partitions
> Here is the example query:
> val dailycached = spark.sql("select something from table where dt =
> '20170301' AND something IS NOT NULL")
> dailycached.createOrReplaceTempView("dailycached")
> spark.catalog.cacheTable("dailyCached")
> spark.sql("SELECT COUNT(DISTINCT(something)) from dailycached").show()
>
> On spark 2.2 I see queries times average 13 seconds
> On the same nodes I see spark 2.3 queries times average 17 seconds
> Note these are times of queries after the initial caching. so just running
> the last line again:
> spark.sql("SELECT COUNT(DISTINCT(something)) from dailycached").show()
> multiple times.
>
> I also ran a query over more data (335GB input/587.5 GB cached) and saw a
> similar discrepancy in the performance of querying cached data between spark
> 2.3 and spark 2.2, where 2.2 was better by like 20%.
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