Druid 0.9.2 Release

by Gian Merlino · December 1, 2016

The Druid community is pleased to announce our next major release, 0.9.2. We’ve added hundreds of performance improvements, stability improvements, and bug fixes.

You can find the full list of changes here and documentation for this release here.

Major Highlights

New groupBy engine

Druid now includes a new groupBy engine, rewritten from the ground up for better performance and memory management. Benchmarks show a 2–5x performance boost on our test datasets. The new engine also supports strict limits on memory usage and the option to spill to disk when memory is exhausted, avoiding result set row count limitations and potential OOMEs generated by the previous engine.

The new engine is off by default, but you can enable it through configuration or query context parameters. We intend to enable it by default in a future version of Druid.

You can find additional “implementation details” on http://druid.io/docs/0.9.2/querying/groupbyquery.html#implementation-details for documentation and configuration.

Ability to disable rollup

Since its inception, Druid has had a concept of “dimensions” and “metrics” that applied both at ingestion time and at query time. Druid is unique in that it is one of the only databases that supports aggregation at data loading time, which we call “rollup”. But, for some use cases, ingestion-time rollup is not desired, and it’s better to load the original data as-is. With rollup disabled, one row in Druid will be created for each input row.

Query-time aggregation is, of course, still supported through the groupBy, topN, and timeseries queries.

For additional information, see the “rollup” flag on http://druid.io/docs/0.9.2/ingestion/index.html for documentation. By default, rollup remains enabled.

Ability to filter on longs

Druid now supports sophisticated filtering on integer-typed columns, including long metrics and the special __time column. This opens up a number of new capabilities:

  • Filtered aggregations on time, useful for time comparison queries using two filtered aggregators and a post-aggregator. This can also be used for retention analysis with theta sketches. You can find examples here.
  • Filtering on integer-typed columns, which is especially useful when rollup is disabled using the new rollup-disabling flag. Druid does not yet support grouping on longs. We intend to add this capability in a future releases.

New long encodings

Until now, all integer-typed columns in Druid, including long metrics and the special __time column, were stored as 64-bit longs optionally compressed in blocks with LZ4. Druid 0.9.2 adds new encoding options which, in many cases, can reduce file sizes and improve performance:

  • Long encoding option “auto”, which potentially uses table or delta encoding to use fewer than 64 bits per row. The “longs” encoding option is the default behavior, which always uses 64 bits.
  • Compression option “none”, which is like the old “uncompressed” option, except it offers a speedup by bypassing block copying.

The default remains “longs” encoding + “lz4” compression. In our testing, two options that often yield useful benefits are “auto” + “lz4” (generally smaller than longs + lz4) and “auto” + “none” (generally faster than longs + lz4, file size impact varies). See the PR for full test results. See “metricCompression” and “longEncoding” on http://druid.io/docs/0.9.2/ingestion/batch-ingestion.html for documentation.

We’re creating an additional blog post for this work. Stay tuned to our blog for more information.

Release Notes

In addition to these major highlights, Druid 0.9.2 contains a number of other improvements. For more information about these changes, bug fixes, and more, check out the full release notes here.


The Druid community’s efforts made all the great improvements in this release possible. Thanks to all the members of the community who contributed to this release.

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