mirror of https://github.com/apache/druid.git
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
This module provides Druid aggregators based on https://datasketches.github.io/.
Credits: This module is a result of feedback and work done by following people.
https://github.com/cheddar https://github.com/himanshug https://github.com/leerho https://github.com/will-lauer