Cheolsoo Park created SPARK-9926:
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Summary: 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
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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