Table of Contents


Druid has limited support for joins through query-time lookups. The common use case of query-time lookups is to replace one dimension value (e.g. a String ID) with another value (e.g. a human-readable String value). This is similar to a star-schema join.

Druid does not yet have full support for joins. Although Druid’s storage format would allow for the implementation of joins (there is no loss of fidelity for columns included as dimensions), full support for joins have not yet been implemented yet for the following reasons:

  1. Scaling join queries has been, in our professional experience, a constant bottleneck of working with distributed databases.
  2. The incremental gains in functionality are perceived to be of less value than the anticipated problems with managing highly concurrent, join-heavy workloads.

A join query is essentially the merging of two or more streams of data based on a shared set of keys. The primary
high-level strategies for join queries we are aware of are a hash-based strategy or a sorted-merge strategy. The hash-based strategy requires that all but one data set be available as something that looks like a hash table, a lookup operation is then performed on this hash table for every row in the “primary” stream. The sorted-merge strategy assumes that each stream is sorted by the join key and thus allows for the incremental joining of the streams. Each of these strategies, however, requires the materialization of some number of the streams either in sorted order or in a hash table form.

When all sides of the join are significantly large tables (> 1 billion records), materializing the pre-join streams requires complex distributed memory management. The complexity of the memory management is only amplified by the fact that we are targeting highly concurrent, multi-tenant workloads.