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Posted to issues@spark.apache.org by "DB Tsai (JIRA)" <ji...@apache.org> on 2016/04/08 10:15:25 UTC
[jira] [Updated] (SPARK-9926) Parallelize file listing for
partitioned Hive table
[ https://issues.apache.org/jira/browse/SPARK-9926?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
DB Tsai updated SPARK-9926:
---------------------------
Assignee: Ryan Blue (was: Cheolsoo Park)
> Parallelize file listing for partitioned Hive table
> ---------------------------------------------------
>
> Key: SPARK-9926
> URL: https://issues.apache.org/jira/browse/SPARK-9926
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 1.4.1, 1.5.0
> Reporter: Cheolsoo Park
> Assignee: Ryan Blue
>
> In Spark SQL, short queries like {{select * from table limit 10}} run very slowly against partitioned Hive tables because of file listing. In particular, if a large number of partitions are scanned on storage like S3, the queries run extremely slowly. Here are some example benchmarks in my environment-
> * Parquet-backed Hive table
> * Partitioned by dateint and hour
> * Stored on S3
> ||\# of partitions||\# of files||runtime||query||
> |1|972|30 secs|select * from nccp_log where dateint=20150601 and hour=0 limit 10;|
> |24|13646|6 mins|select * from nccp_log where dateint=20150601 limit 10;|
> |240|136222|1 hour|select * from nccp_log where dateint>=20150601 and dateint<=20150610 limit 10;|
> The problem is that {{TableReader}} constructs a separate HadoopRDD per Hive partition path and group them into a UnionRDD. Then, all the input files are listed sequentially. In other tools such as Hive and Pig, this can be solved by setting [mapreduce.input.fileinputformat.list-status.num-threads|https://hadoop.apache.org/docs/stable/hadoop-mapreduce-client/hadoop-mapreduce-client-core/mapred-default.xml] high. But in Spark, since each HadoopRDD lists only one partition path, setting this property doesn't help.
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