Help Center/ MapReduce Service/ Component Operation Guide (LTS) (Ankara Region)/ Using Spark/ Spark Performance Tuning/ Spark SQL and DataFrame Tuning/ Optimizing the INSERT...SELECT Operation
Updated on 2024-11-29 GMT+08:00
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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.