Xiao Li commented on SPARK-23309:

Based on my understanding about what [~tgraves]said above, the number of 
partitions is different between our ORC reader and Hive-serde reader because we 
do not respect Hive confs. 

Now the performance regression is observed when we read cached data. This 
should not be related to Hive. This 
https://issues.apache.org/jira/browse/SPARK-23312 has been merged. Thus, maybe 
[~tgraves]can try that patch and see whether the performance regression is gone 
after setting {{spark.sql.inMemoryColumnarStorage.enableVectorizedReader}} to 

> 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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