Lantao Jin created SPARK-30114:
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Summary: Optimize LIMIT only query by partial listing files
Key: SPARK-30114
URL: https://issues.apache.org/jira/browse/SPARK-30114
Project: Spark
Issue Type: Improvement
Components: SQL
Affects Versions: 3.0.0
Reporter: Lantao Jin
We use Spark as ad-hoc query engine. Most of users' SELECT queries with LIMIT
operation. When we execute some queries like
1) SELECT * FROM TABLE_A LIMIT N
2) SELECT colA FROM TABLE_A LIMIT N
3) CREATE TAB_B as SELECT * FROM TABLE_A LIMIT N
If the TABLE_A is a large table (a RDD with thousands and thousands of
partitions), the execution time would be very big since it has to list all
files to build a RDD before execution. But almost time, the N is just like 10,
100, 1000, not very big. We don't need to scan all files. This optimization
will create a *SinglePartitionReadRDD* to address it.
In our production result, this optimization benefits a lot. The duration time
of simple query with LIMIT could reduce 5~10 times. For example, before this
optimization, a query on a table which has about one hundred thousands files
would run over 30 seconds, after applying this optimization, the time decreased
to 5 seconds.
Should support both Spark DataSource Table and Hive Table which can be
converted to DataSource table.
Should support bucket table, partition table, normal table.
Should support different file formats like parquet, orc.
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