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SQL-based ingestion


This page describes SQL-based batch ingestion using the druid-multi-stage-query extension, new in Druid 24.0. Refer to the ingestion methods table to determine which ingestion method is right for you.

Apache Druid supports SQL-based ingestion using the bundled druid-multi-stage-query extension, which is enabled by default. This extension adds a multi-stage query task engine for SQL that allows running SQL INSERT and REPLACE statements as batch tasks. As an experimental feature, the task engine also supports running SELECT queries as batch tasks.

Nearly all SELECT capabilities are available in the multi-stage query (MSQ) task engine, with certain exceptions listed on the Known issues page. This allows great flexibility to apply transformations, filters, JOINs, aggregations, and so on as part of INSERT ... SELECT and REPLACE ... SELECT statements. This also allows in-database transformation: creating new tables based on queries of other tables.

To use EXTERN, you need READ permission on the resource named "EXTERNAL" of the resource type "EXTERNAL". If you encounter a 403 error when trying to use EXTERN, verify that you have the correct permissions. The same is true of any of the input-source specific table function such as S3 or LOCALFILES.


  • Controller: An indexing service task of type query_controller that manages the execution of a query. There is one controller task per query.

  • Worker: Indexing service tasks of type query_worker that execute a query. There can be multiple worker tasks per query. Internally, the tasks process items in parallel using their processing pools (up to druid.processing.numThreads of execution parallelism within a worker task).

  • Stage: A stage of query execution that is parallelized across worker tasks. Workers exchange data with each other between stages.

  • Partition: A slice of data output by worker tasks. In INSERT or REPLACE queries, the partitions of the final stage become Druid segments.

  • Shuffle: Workers exchange data between themselves on a per-partition basis in a process called shuffling. During a shuffle, each output partition is sorted by a clustering key.

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