* Fix resource leak in `io.druid.segment.IndexIO.DefaultIndexIOHandler#validateTwoSegments(java.io.File, java.io.File)`
* Un-deprecate `close()` in `QueryableIndex` and make it inherit `Closeable`
* Fix resource leaks in various unit tests
* Add `CloserRule` for closing out resources
it fails when we issue the SegmentMetadataQuery by setting {"bySegment" : true} in context with exception -
java.lang.ClassCastException: io.druid.query.Result cannot be cast to io.druid.query.metadata.metadata.SegmentAnalysis
at io.druid.query.metadata.SegmentMetadataQueryQueryToolChest$4.compare(SegmentMetadataQueryQueryToolChest.java:222) ~[druid-processing-0.7.3-SNAPSHOT.jar:0.7.3-SNAPSHOT]
at com.google.common.collect.NullsFirstOrdering.compare(NullsFirstOrdering.java:44) ~[guava-16.0.1.jar:?]
at com.metamx.common.guava.MergeIterator$1.compare(MergeIterator.java:46) ~[java-util-0.27.0.jar:?]
at com.metamx.common.guava.MergeIterator$1.compare(MergeIterator.java:42) ~[java-util-0.27.0.jar:?]
at java.util.PriorityQueue.siftUpUsingComparator(PriorityQueue.java:649) ~[?:1.7.0_80]
which caused following regression
it fails when we issue the SegmentMetadataQuery by setting {"bySegment" : true} in context with exception -
java.lang.ClassCastException: io.druid.query.Result cannot be cast to io.druid.query.metadata.metadata.SegmentAnalysis
at io.druid.query.metadata.SegmentMetadataQueryQueryToolChest$4.compare(SegmentMetadataQueryQueryToolChest.java:222) ~[druid-processing-0.7.3-SNAPSHOT.jar:0.7.3-SNAPSHOT]
at com.google.common.collect.NullsFirstOrdering.compare(NullsFirstOrdering.java:44) ~[guava-16.0.1.jar:?]
at com.metamx.common.guava.MergeIterator$1.compare(MergeIterator.java:46) ~[java-util-0.27.0.jar:?]
at com.metamx.common.guava.MergeIterator$1.compare(MergeIterator.java:42) ~[java-util-0.27.0.jar:?]
at java.util.PriorityQueue.siftUpUsingComparator(PriorityQueue.java:649) ~[?:1.7.0_80]
* Inverted function arguments to compose postProcFn for GroupBy queries
with havingspec + limitspec.
* Replaced query.getLimitSpec() with null in GroupByQueryToolChest's
mergeGroupByResults
* Added unittest to verify functionality
that is if complex agg did not implement inputSizeFn() so
that segment metadata query shows atleast some information.
also instead of COMPLEX, return type of data stored.
renamed all [Max/Min]*.java to [DoubleMax/DoubleMin]*.java and created [Max/Min]AggregatorFactory.java which can be removed when we dont need the min/max aggregator type backward compatibility
fixes#1211
fix value matcher
more improvements
more fixes for partial null column
fix handling of dimension having only null values
fixes#1211
fix value matcher
more improvements
more fixes for partial null column
review comment
IndexMaker speedups
* About 15% speedup
Conflicts:
processing/src/main/java/io/druid/segment/IndexMaker.java
fix handling of dimension having only null values
fixes#1211
fix value matcher
more improvements
more fixes for partial null column
fix handling of dimension having only null values
fixes#1211
fix value matcher
more improvements
more fixes for partial null column
review comment
review comments
review comment
fix failing tests
review comment
fix compilation
fixes#1330,
Avoid creating Period instance as creating a Period from Long.MAX_VALUE
throws arithmetic exception.
After this query metric will emit duration in seconds instead of
minutes.
* Add some unit tests
* Add io.druid.segment.IndexMerger.reprocess for quick re-indexing of data
* Add dim-value validation to validation checker (instead of ONLY index #)
* General code refactoring to make things a little easier to read
on postAggregations which are removed in the forwarded query.
Add a unit test to replicate the issue.
Add a query that can replicate this issue into integration test.
- compression for single-value dimensions using CompressedVSizeIntsIndexedSupplier
- makes dimension compression configurable via IndexSpec
- IndexSpec also enables configuring bitmap and metric compression
Also, remove some comments that did not have enough context to actually make sense to anyone but the original author (at least, I hope they make sense to the author, I definitely don't know what was being said).
* Requires https://github.com/druid-io/druid-api/pull/37
* Requires https://github.com/metamx/java-util/pull/22
* Moves the puller logic to use a more standard workflow going through java-util helpers instead of re-writing the handlers for each impl
* General workflow goes like this: 1) LoadSpec makes sure the correct Puller is called with the correct parameters. 2) The Puller sets up general information like how to make an InputStream, how to find a file name (for .gz files for example), and when to retry. 3) CompressionUtils does most of the heavy lifting when it can
- Moves DimExtractionFn under a more generic ExtractionFn interface to
support extracting dimension values other than strings
- pushes down extractionFn to the storage adapter from query engine
- 'dimExtractionFn' parameter has been deprecated in favor of 'extractionFn'
- adds a TimeFormatExtractionFn, allowing to project the '__time' dimension
- JavascriptDimExtractionFn renamed to JavascriptExtractionFn, adding
support for any dimension value types that map directly to Javascript
- update documentation for time column extraction and related changes
* add test for same-name groupBy hyperUniques post-agg
* add test for same-name post-agg in groupby with approx histogram
* Fixes https://github.com/druid-io/druid/issues/1045
* Throws an error if post aggs and aggs do not have unique names
* Add more groupBy tests for Having filters
- Switch to using Druid parent POM
- Add required fields for Sonatype
- Common plugin versions and settings have been moved to the parent pom
- Cleanup artifacts and POMs for consistent formatting
- Remove org.hyperic.sigar dependency and update docs to reflect necessary jars to add at runtime when sigar is needed
This commit also includes
1) the addition of a context parameter on timeseries queries that allows it to ignore empty buckets instead of generating results for them
2) A cleanup of an unused method on an interface
Default behavior is as before.
Added documentation for how to enable synchronous logging for select chatty classes:
* io.druid.client.ServerInventoryView
* io.druid.client.BatchServerInventoryView
* io.druid.curator.inventory.CuratorInventoryManager
* com.metamx.http.client.pool.ChannelResourceFactory
* Was encountering weird errors when fast writes were coming in while queries were happening.
* Added unit tests which tend to cause concurrency query problems
* Add type strictness to CachingClusteredClient.
* Add background caching to CachingClusteredClient. Gives between 0% and 5% query speed increase.
* Add @BackgroundCaching annotation for injected ExecutorService items
* Add `numBackgroundThreads' configuration options to CacheConfig (default 0 aka same thread legacy behavior)
* Add unit tests for CacheConfig
* Add an abstract caching query runner class, currently it doesn't do anything exceppt simply make the two caching queries distinct.
* Add caching to CachingQueryRunner. Gives up to a WHOPPING 40% reduction in query time on HLL queries
* Updated docs with more info on cache settings.
- For now uses a hardcoded ratio of aggregator to timeanddim buffer sizes
- canAppendRow is a workaround for realtime index since the
Firehose currently does not have a way of rolling back the last event in
case of error
- canAppendRow needs a fudge factor; there is a race between checking
if we can add a row and actually adding a row, because of the way MapDB
reports its size.
Change serializer / deserializer for HyperLogLog
* Changed DirectDruidClient's InputStream handling. Is now ~10% faster for data heavy queries, and has lower variance in execution speed.
* Changed HLL Collector's toByteStream() method to be better optimized for small values. Is notably faster for small result quantities which fall into the sparse HLL bucket codepath.
* No change for dense HLL which just uses a direct bytestream of the underlying byte data.
TopNNumericResultBuilder semi-aggressive loop unrolling for metricVals
Benchmark for HLL for sparse packing (small HLL bucket population):
HyperLogLogSerdeBenchmarkTest.benchmarkToByteBuffer[0]: [measured 100000 out of 100100 rounds, threads: 1 (sequential)]
round: 0.00 [+- 0.00], round.block: 0.00 [+- 0.00], round.gc: 0.00 [+- 0.00], GC.calls: 216, GC.time: 0.42, time.total: 15.96, time.warmup: 0.22, time.bench: 15.74
HyperLogLogSerdeBenchmarkTest.benchmarkToByteBuffer[1]: [measured 100000 out of 100100 rounds, threads: 1 (sequential)]
round: 0.00 [+- 0.00], round.block: 0.00 [+- 0.00], round.gc: 0.00 [+- 0.00], GC.calls: 217, GC.time: 0.45, time.total: 13.87, time.warmup: 0.02, time.bench: 13.85
HyperLogLogSerdeBenchmarkTest.benchmarkToByteBuffer[2]: [measured 100000 out of 100100 rounds, threads: 1 (sequential)]
round: 0.00 [+- 0.00], round.block: 0.00 [+- 0.00], round.gc: 0.00 [+- 0.00], GC.calls: 55, GC.time: 0.16, time.total: 4.13, time.warmup: 0.00, time.bench: 4.12
HyperLogLogSerdeBenchmarkTest.benchmarkToByteBuffer[3]: [measured 100000 out of 100100 rounds, threads: 1 (sequential)]
round: 0.00 [+- 0.00], round.block: 0.00 [+- 0.00], round.gc: 0.00 [+- 0.00], GC.calls: 55, GC.time: 0.16, time.total: 4.30, time.warmup: 0.00, time.bench: 4.30
HyperLogLogSerdeBenchmarkTest.benchmarkToByteBuffer[4]: [measured 100000 out of 100100 rounds, threads: 1 (sequential)]
round: 0.00 [+- 0.00], round.block: 0.00 [+- 0.00], round.gc: 0.00 [+- 0.00], GC.calls: 8, GC.time: 0.03, time.total: 1.10, time.warmup: 0.00, time.bench: 1.09
HyperLogLogSerdeBenchmarkTest.benchmarkToByteBuffer[5]: [measured 100000 out of 100100 rounds, threads: 1 (sequential)]
round: 0.00 [+- 0.00], round.block: 0.00 [+- 0.00], round.gc: 0.00 [+- 0.00], GC.calls: 8, GC.time: 0.03, time.total: 0.72, time.warmup: 0.00, time.bench: 0.72
HyperLogLogSerdeBenchmarkTest.benchmarkToByteBuffer[6]: [measured 100000 out of 100100 rounds, threads: 1 (sequential)]
round: 0.00 [+- 0.00], round.block: 0.00 [+- 0.00], round.gc: 0.00 [+- 0.00], GC.calls: 1, GC.time: 0.00, time.total: 0.60, time.warmup: 0.00, time.bench: 0.60
HyperLogLogSerdeBenchmarkTest.benchmarkToByteBuffer[7]: [measured 100000 out of 100100 rounds, threads: 1 (sequential)]
round: 0.00 [+- 0.00], round.block: 0.00 [+- 0.00], round.gc: 0.00 [+- 0.00], GC.calls: 2, GC.time: 0.01, time.total: 0.26, time.warmup: 0.00, time.bench: 0.25
Updates to HyperLogLogCollector toByteBuffer() based on code review
Removed changes from DirectDruidClient from this branch and put it in another branch.
Changed HyperLogLogCollector to have protected getters and setters
Remove unused ByteOrder from HyperLogLogCollector
Copyright header on HyperLogLogSerdeBenchmarkTest
Now with less ass!
Reformat in TopNNumericResultsBuilder. No code change
Removed unused import in HyperLogLogCollector
Replace AppendableByteArrayInputStream in DirectDruidClient
* Replace with SequenceInputStream fueled by an enumeration of ChannelBufferInputStream which directly wrap the response context ChannelBuffer
Modify TopNQueryQueryToolChest to use Arrays instead of Lists
Modify TopNQueryQueryToolChest to use Arrays instead of Lists
Revert accidental changes to DirectDruidClient
They should be in another merge request:
https://github.com/metamx/druid/pull/893
Fixes from code review
* Extracting names from AggregatorFactory classes now done with TopNQueryQueryToolChest.extractFactoryName
* Renamed variable in TopNNumericResultBuilder
* Added more unit tests
* Now properly uses safe / fast decompressor for LZ4
* Now chooses fastest lz4 instance instead of only looking at Java implmentations
* Encapsulate ResourceHolder in try-with-resources to make sure they close correctly
* Modify SmileFactory to set the delegate to text option.
* This option only occurs when a Reader type object is passed in to the deserialization stuff
* This is needed by the X-Druid-Response-Context header return value, which is JSON
* Changed topN queries to use joda Interval instead of string values
* topN by segment now implements BySegmentResultValue<Result<TopNResultValue>> instead of BySegmentResultValue<TopNResultValue>
* Added a unit test which failed uner the prior implementation.
Charles Allen
Bingkun Guo
Xavier Léauté
Xavier Léauté
nishant
fjy
Himanshu
Parag Jain
Maarten Rijke
Neo
vkavuluri
Eric Tschetter
Slim Bouguerra
msprunck
Prajwal Tuladhar
Himanshu Gupta
Joshua Schumacher
James Estes
Gian Merlino
Maarten Rijke
Vikram Ramakrishnan