* Minor bug fixes in GenericIndexed; Refactor and optimize GenericIndexed; Remove some unnecessary ByteBuffer duplications in some deserialization paths; Add ZeroCopyByteArrayOutputStream
* Fixes
* Move GenericIndexedWriter.writeLongValueToOutputStream() and writeIntValueToOutputStream() to SerializerUtils
* Move constructors
* Add GenericIndexedBenchmark
* Comments
* Typo
* Note in Javadoc that IntermediateLongSupplierSerializer, LongColumnSerializer and LongMetricColumnSerializer are thread-unsafe
* Use primitive collections in IntermediateLongSupplierSerializer instead of BiMap
* Optimize TableLongEncodingWriter
* Add checks to SerializerUtils methods
* Don't restrict byte order in SerializerUtils.writeLongToOutputStream() and writeIntToOutputStream()
* Update GenericIndexedBenchmark
* SerializerUtils.writeIntToOutputStream() and writeLongToOutputStream() separate for big-endian and native-endian
* Add GenericIndexedBenchmark.indexOf()
* More checks in methods in SerializerUtils
* Use helperBuffer.arrayOffset()
* Optimizations in SerializerUtils
* No more singleton. Reduce iterations
* Granularities
* Fix the delay in the test
* Add license header
* Remove unused imports
* Lot more unused imports from all the rearranging
* CR feedback
* Move javadoc to constructor
* Refactor Segment Granularity
* Beginning of one granularity
* Copy the fix for custom periods in segment-grunalrity over here.
* Remove the custom serialization for now.
* Compilation cleanup
* Reformat code
* Fixing unit tests
* Unify to use a single iterable
* Backward compatibility for rolling upgrade
* Minor check style. Cosmetic changes.
* Rename length and millis to duration
* CR feedback
* Minor changes.
* Removing Integer.MAX column size limit.
* On demand creation of headerLong, use v2 instead of v3
* Avoid reusing the same object from a previous test.
* Avoid reusing the same object from a previous test part#2
* code formatting.
* GenericIndexed/Writer code review changes.
* GenericIndexed/writer code review requested changes.
* checkIndex() to static
* native endianess for genericIndexedV2, code review requested changes.
* Formatting
* Hll fix.
* use native endianess during bag size calculation.
* Code review requested changes.
* IOPeon close() changes.
* use different tmp directory path for testing.
* Code review requested changes.
* SQL: Add context and contextual functions to planner.
Added support for context parameters specified as JDBC connection properties
or a JSON object for SQL-over-JSON-over-HTTP.
Also added features that depend on context functionality:
- Added CURRENT_DATE, CURRENT_TIME, CURRENT_TIMESTAMP functions.
- Added support for time zones other than UTC via a "timeZone" context.
- Pass down query context to Druid queries too.
Also some bug fixes:
- Fix DATE handling, it was largely done incorrectly before.
- Fix CAST(__time TO DATE) which should do a floor-to-day.
- Fix non-equality comparisons to FLOOR(__time TO X).
- Fix maxQueryCount property.
* Pass down context to nested queries too.
* Less use of File.deleteOnExit()
* removed deleteOnExit from most of the tests/benchmarks/iopeon
* Made IOpeon closable
* Formatting.
* Revert DeterminePartitionsJobTest, remove cleanup method from IOPeon
* Add filter selectivity estimation for auto search strategy
* Addressed comments
* Lazy bitmap materialization for bitmap sampling and java docs
* Addressed comments.
- Fix wrong non-overlap ratio computation and added unit tests.
- Change Iterable<Integer> to IntIterable
- Remove unnecessary Iterable<Integer>
* Addressed comments
- Split a long ternary operation into if-else blocks
- Add IntListUtils.fromTo()
* Fix test failure and add a test for RangeIntList
* fix code style
* Diabled selectivity estimation for multi-valued dimensions
* Address comment
* SQL: Add resolution parameter to quantile agg, rename to APPROX_QUANTILE.
* Fix bug with re-use of filtered approximate histogram aggregators.
Also add APPROX_QUANTILE tests for filtering and running on complex columns.
Includes some slight refactoring to allow tests to make DruidTables that
include complex columns.
* Remove unused import
* SQL: Ditch CalciteConnection layer and add DruidMeta, extension aggregators.
Switched from CalciteConnection to Planner, bringing benefits:
- CalciteConnection's JDBC interface no longer sits between the SQL server
(HTTP/Avatica) and Druid's query layer. Instead, the SQL servers can use
Druid Sequence objects directly, reducing overhead in the query return path.
- Implemented our own Planner-based Avatica Meta, letting us control
connection timeouts and connection / statement limits. The previous
CalciteConnection-based implementation didn't have any limits or timeouts.
- The Planner interface lets us override the operator table, opening up
SQL language extensions. This patch includes two: APPROX_COUNT_DISTINCT
in core, and a QUANTILE aggregator in the druid-histogram extension.
Also:
- Added INFORMATION_SCHEMA metadata schema.
- Added tests for Unicode literals and escapes.
* Verify statement is actually open before closing it.
* More detailed INFORMATION_SCHEMA docs.
* SQL support for nested groupBys.
Allows, for example, doing exact count distinct by writing:
SELECT COUNT(*) FROM (SELECT DISTINCT col FROM druid.foo)
Contrast with approximate count distinct, which is:
SELECT COUNT(DISTINCT col) FROM druid.foo
* Add deeply-nested groupBy docs, tests, and maxQueryCount config.
* Extract magic constants into statics.
* Rework rules to put preconditions in the "matches" method.
* Add an option to SearchQuery to choose a search query execution strategy.
Supported strategies are
1) Index-only query execution
2) Cursor-based scan
3) Auto: choose an efficient strategy for a given query
* Add SearchStrategy and SearchQueryExecutor
* Address comments
* Rename strategies and set UseIndexesStrategy as the default strategy
* Add a cost-based planner for auto strategy
* Add document
* Fix code style
* apply code style
* apply comments
* Enable parallel test
* Remove unnecessary NotThreadSafe annocation
* Randomize the start port when finding available ports
* Fix test failure
* Change to handle all negatives
* GroupByBenchmark: Add serde, spilling, all-gran benchmarks.
Also use more iterations.
* groupBy v2: Ignore timestamp completely when granularity = all, except for the final merge.
Specifically:
- Remove timestamp from RowBasedKey when not needed
- Set timestamp to null in MapBasedRows that are not part of the final merge.
* Add "like" filter.
* Addressed some PR comments.
* Slight simplifications to LikeFilter.
* Additional simplifications.
* Fix comment in LikeFilter.
* Clarify comment in LikeFilter.
* Simplify LikeMatcher a bit.
* No use going through the optimized path if prefix is empty.
* Add more tests.
With the old code, all on-disk segments were the same. Now they're different.
This will end up altering benchmark results for queryMultiQueryableIndex,
likely making them slower (since values won't group as well as they used to).
The memory changes will help test with larger/more segments, since we won't
have to hold them all in memory at once.
* ability to not rollup at index time, make pre aggregation an option
* rename getRowIndexForRollup to getPriorIndex
* fix doc misspelling
* test query using no-rollup indexes
* fix benchmark fail due to jmh bug
* Add numeric StringComparator
* Only use direct long comparison for numeric ordering in BoundFilter, add time filtering benchmark query
* Address PR comments, add multithreaded BoundDimFilter test
* Add comment on strlen tie handling
* Add timeseries interval filter benchmark
* Adjust docs
* Use jackson for StringComparator, address PR comments
* Add new TopNMetricSpec and SearchSortSpec with tests (WIP)
* More TopNMetricSpec and SearchSortSpec tests
* Fix NewSearchSortSpec serde
* Update docs for new DimensionTopNMetricSpec
* Delete NumericDimensionTopNMetricSpec
* Delete old SearchSortSpec
* Rename NewSearchSortSpec to SearchSortSpec
* Add TopN numeric comparator benchmark, address PR comments
* Refactor OrderByColumnSpec
* Add null checks to NumericComparator and String->BigDecimal conversion function
* Add more OrderByColumnSpec serde tests
* Support filtering on __time column
* Rename DruidPredicate
* Add docs for ValueMatcherFactory, add comment on getColumnCapabilities
* Combine ValueMatcherFactory predicate methods to accept DruidCompositePredicate
* Address PR comments (support filter on all long columns)
* Use predicate factory instead of composite predicate
* Address PR comments
* Lazily initialize long handling in selector/in filter
* Move long value parsing from InFilter to InDimFilter, make long value parsing thread-safe
* Add multithreaded selector/in filter test
* Fix non-final lock object in SelectorDimFilter
Because timestamps at the end instant are not actually part of the interval. This
affected benchmark numbers, since it meant some data points would not be queried
(the interval for the query was based on getDataInterval) and also the
TimestampCheckingOffsets could not use the allWithinThreshold optimization.
This patch introduces a GroupByStrategy concept and two strategies: "v1"
is the current groupBy strategy and "v2" is a new one. It also introduces
a merge buffers concept in DruidProcessingModule, to try to better
manage memory used for merging.
Both of these are described in more detail in #2987.
There are two goals of this patch:
1. Make it possible for historical/realtime nodes to return larger groupBy
result sets, faster, with better memory management.
2. Make it possible for brokers to merge streams when there are no order-by
columns, avoiding materialization.
This patch does not do anything to help with memory management on the broker
when there are order-by columns or when there are nested queries. That could
potentially be done in a future patch.
* new interval based cost function
Addresses issues with balancing of segments in the existing cost function
- `gapPenalty` led to clusters of segments ~30 days apart
- `recencyPenalty` caused imbalance among recent segments
- size-based cost could be skewed by compression
New cost function is purely based on segment intervals:
- assumes each time-slice of a partition is a constant cost
- cost is additive, i.e. cost(A, B union C) = cost(A, B) + cost(A, C)
- cost decays exponentially based on distance between time-slices
* comments and formatting
* add more comments to explain the calculation
Roman Leventov
Jonathan Wei
praveev
Jihoon Son
Akash Dwivedi
Gian Merlino
DaimonPl
Himanshu
Navis Ryu
Dave Li
kaijianding
Hyukjin Kwon
Charles Allen
Xavier Léauté
John Wang