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Posted to issues@spark.apache.org by "Hyukjin Kwon (Jira)" <ji...@apache.org> on 2019/10/08 05:42:21 UTC
[jira] [Resolved] (SPARK-23797) SparkSQL performance on small TPCDS
tables is very low when compared to Drill or Presto
[ https://issues.apache.org/jira/browse/SPARK-23797?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-23797.
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Resolution: Incomplete
> SparkSQL performance on small TPCDS tables is very low when compared to Drill or Presto
> ---------------------------------------------------------------------------------------
>
> Key: SPARK-23797
> URL: https://issues.apache.org/jira/browse/SPARK-23797
> Project: Spark
> Issue Type: Bug
> Components: Optimizer, Spark Submit, SQL
> Affects Versions: 2.3.0
> Reporter: Tin Vu
> Priority: Major
> Labels: bulk-closed
>
> I am executing a benchmark to compare performance of SparkSQL, Apache Drill and Presto. My experimental setup:
> * TPCDS dataset with scale factor 100 (size 100GB).
> * Spark, Drill, Presto have a same number of workers: 12.
> * Each worked has same allocated amount of memory: 4GB.
> * Data is stored by Hive with ORC format.
> I executed a very simple SQL query: "SELECT * from table_name"
> The issue is that for some small size tables (even table with few dozen of records), SparkSQL still required about 7-8 seconds to finish, while Drill and Presto only needed less than 1 second.
> For other large tables with billions records, SparkSQL performance was reasonable when it required 20-30 seconds to scan the whole table.
> Do you have any idea or reasonable explanation for this issue?
> Thanks,
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