ffa25b7832
* Benchmarks: New SqlBenchmark, add caching & vectorization to some others. - Introduce a new SqlBenchmark geared towards benchmarking a wide variety of SQL queries. Rename the old SqlBenchmark to SqlVsNativeBenchmark. - Add (optional) caching to SegmentGenerator to enable easier benchmarking of larger segments. - Add vectorization to FilteredAggregatorBenchmark and GroupByBenchmark. * Query vectorization. This patch includes vectorized timeseries and groupBy engines, as well as some analogs of your favorite Druid classes: - VectorCursor is like Cursor. (It comes from StorageAdapter.makeVectorCursor.) - VectorColumnSelectorFactory is like ColumnSelectorFactory, and it has methods to create analogs of the column selectors you know and love. - VectorOffset and ReadableVectorOffset are like Offset and ReadableOffset. - VectorAggregator is like BufferAggregator. - VectorValueMatcher is like ValueMatcher. There are some noticeable differences between vectorized and regular execution: - Unlike regular cursors, vector cursors do not understand time granularity. They expect query engines to handle this on their own, which a new VectorCursorGranularizer class helps with. This is to avoid too much batch-splitting and to respect the fact that vector selectors are somewhat more heavyweight than regular selectors. - Unlike FilteredOffset, FilteredVectorOffset does not leverage indexes for filters that might partially support them (like an OR of one filter that supports indexing and another that doesn't). I'm not sure that this behavior is desirable anyway (it is potentially too eager) but, at any rate, it'd be better to harmonize it between the two classes. Potentially they should both do some different thing that is smarter than what either of them is doing right now. - When vector cursors are created by QueryableIndexCursorSequenceBuilder, they use a morphing binary-then-linear search to find their start and end rows, rather than linear search. Limitations in this patch are: - Only timeseries and groupBy have vectorized engines. - GroupBy doesn't handle multi-value dimensions yet. - Vector cursors cannot handle virtual columns or descending order. - Only some filters have vectorized matchers: "selector", "bound", "in", "like", "regex", "search", "and", "or", and "not". - Only some aggregators have vectorized implementations: "count", "doubleSum", "floatSum", "longSum", "hyperUnique", and "filtered". - Dimension specs other than "default" don't work yet (no extraction functions or filtered dimension specs). Currently, the testing strategy includes adding vectorization-enabled tests to TimeseriesQueryRunnerTest, GroupByQueryRunnerTest, GroupByTimeseriesQueryRunnerTest, CalciteQueryTest, and all of the filtering tests that extend BaseFilterTest. In all of those classes, there are some test cases that don't support vectorization. They are marked by special function calls like "cannotVectorize" or "skipVectorize" that tell the test harness to either expect an exception or to skip the test case. Testing should be expanded in the future -- a project in and of itself. Related to #3011. * WIP * Adjustments for unused things. * Adjust javadocs. * DimensionDictionarySelector adjustments. * Add "clone" to BatchIteratorAdapter. * ValueMatcher javadocs. * Fix benchmark. * Fixups post-merge. * Expect exception on testGroupByWithStringVirtualColumn for IncrementalIndex. * BloomDimFilterSqlTest: Tag two non-vectorizable tests. * Minor adjustments. * Update surefire, bump up Xmx in Travis. * Some more adjustments. * Javadoc adjustments * AggregatorAdapters adjustments. * Additional comments. * Remove switching search. * Only missiles. |
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README.md
Apache Druid (incubating)
Apache Druid (incubating) is a high performance analytics data store for event-driven data.
Disclaimer: Apache Druid is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.
License
More Information
More information about Druid can be found on https://druid.apache.org.
Documentation
You can find the documentation for the latest Druid release on the project website.
If you would like to contribute documentation, please do so under
/docs/content in this repository and submit a pull request.
Getting Started
You can get started with Druid with our quickstart.
Reporting Issues
If you find any bugs, please file a GitHub issue.
Community
Community support is available on the druid-user mailing list(druid-user@googlegroups.com), which is hosted at Google Groups.
Development discussions occur on dev@druid.apache.org, which you can subscribe to by emailing dev-subscribe@druid.apache.org.
We also have a couple people hanging out on IRC in #druid-dev on
irc.freenode.net.
Building From Source
Please note that JDK 8 is required to build Druid.
For instructions on building Druid from source, see docs/content/development/build.md
Contributing
Please follow the guidelines listed here.