forked from docs/doc-exports
Yang, Tong
6182f91ba8
Reviewed-by: Hasko, Vladimir <vladimir.hasko@t-systems.com> Co-authored-by: Yang, Tong <yangtong2@huawei.com> Co-committed-by: Yang, Tong <yangtong2@huawei.com>
3.3 KiB
3.3 KiB
Optimizing the INSERT...SELECT Operation
Scenario
The INSERT...SELECT operation needs to be optimized if any of the following conditions is true:
- Many small files need to be queried.
- A few large files need to be queried.
- The INSERT...SELECT operation is performed by a non-spark user in Beeline/JDBCServer mode.
Procedure
Optimize the INSERT...SELECT operation as follows:
- If the table to be created is the Hive table, set the storage type to Parquet. This enables INSERT...SELECT statements to be run faster.
- Perform the INSERT...SELECT operation as a spark-sql user or spark user (if in Beeline/JDBCServer mode). In this way, it is no longer necessary to change the file owner repeatedly, accelerating the execution of INSERT...SELECT statements.
In Beeline/JDBCServer mode, the executor user is the same as the driver user. The driver user is a spark user because the driver is a part of JDBCServer service and started by a spark user. If the Beeline user is not a spark user, the file owner must be changed to the Beeline user (actual user) because the executor is unaware of the Beeline user.
- If many small files need to be queried, set spark.sql.files.maxPartitionBytes and spark.files.openCostInBytes to set the maximum size in bytes of partition and combine multiple small files in a partition to reduce file amount. This accelerates file renaming, ultimately enabling INSERT...SELECT statements to be run faster.
The preceding optimizations are not a one-size-fits-all solution. In the following scenario, it still takes long to perform the INSERT...SELECT operation:
The dynamic partitioned table contains many partitions.
Parent topic: Spark SQL and DataFrame Tuning