SQL-based ingestion using the multi-stage query task engine is a preview feature available starting in Imply Enterprise and Imply Hybrid 2022.06. It is not available in Polaris yet. Preview features enable early adopters to benefit from new functionality while providing ongoing feedback to help shape and evolve the feature. All functionality documented on this page is subject to change or removal in future releases. Preview features are provided "as is" and are not subject to Imply SLAs.
Apache Druid supports SQL-based ingestion using the bundled
This extension adds a multi-stage query (MSQ) 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 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.
Refer to the ingestion methods table to determine which ingestion method is right for you.
Controller: An indexing service task of type
query_controllerthat manages the execution of a query. There is one controller task per query.
Worker: Indexing service tasks of type
query_workerthat 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.numThreadsof 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.