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Apache Druid has two features related to joining of data:

  1. Join operators. These are available using a join datasource in native queries, or using the JOIN operator in Druid SQL. Refer to the join datasource documentation for information about how joins work in Druid native queries, or the multi-stage query join documentation for information about how joins work in multi-stage query tasks.
  2. Query-time lookups, simple key-to-value mappings. These are preloaded on all servers that are involved in queries and can be accessed with or without an explicit join operator. Refer to the lookups documentation for more details.

Whenever possible, for best performance it is good to avoid joins at query time. Often this can be accomplished by joining data before it is loaded into Druid. However, there are situations where joins or lookups are the best solution available despite the performance overhead, including:

  • The fact-to-dimension (star and snowflake schema) case: you need to change dimension values after initial ingestion, and aren't able to reingest to do this. In this case, you can use lookups for your dimension tables.
  • Your workload requires joins or filters on subqueries.