* Address security vulnerabilities CVSS >= 7
Update dependencies to address security vulnerabilities with CVSS scores
of 7 or higher. A new Travis CI job is added to prevent new
high/critical security vulnerabilities from being added.
Updated dependencies:
- api-util 1.0.0 -> 1.0.3
- jackson 2.9.10 -> 2.10.1
- kafka 2.1.0 -> 2.1.1
- libthrift 0.10.0 -> 0.13.0
- protobuf 3.2.0 -> 3.11.0
The following high/critical security vulnerabilities are currently
suppressed (so that the new Travis CI job can be added now) and are left
as future work to fix:
- hibernate-validator:5.2.5
- jackson-mapper-asl:1.9.13
- libthrift:0.6.1
- netty:3.10.6
- nimbus-jose-jwt:4.41.1
* Rename EDL1 license file
* Fix inspection errors
* Add FileUtils.createTempDir() and enforce its usage.
The purpose of this is to improve error messages. Previously, the error
message on a nonexistent or unwritable temp directory would be
"Failed to create directory within 10,000 attempts".
* Further updates.
* Another update.
* Remove commons-io from benchmark.
* Fix tests.
* sketch of broker parallel merges done in small batches on fork join pool
* fix non-terminating sequences, auto compute parallelism
* adjust benches
* adjust benchmarks
* now hella more faster, fixed dumb
* fix
* remove comments
* log.info for debug
* javadoc
* safer block for sequence to yielder conversion
* refactor LifecycleForkJoinPool into LifecycleForkJoinPoolProvider which wraps a ForkJoinPool
* smooth yield rate adjustment, more logs to help tune
* cleanup, less logs
* error handling, bug fixes, on by default, more parallel, more tests
* remove unused var
* comments
* timeboundary mergeFn
* simplify, more javadoc
* formatting
* pushdown config
* use nanos consistently, move logs back to debug level, bit more javadoc
* static terminal result batch
* javadoc for nullability of createMergeFn
* cleanup
* oops
* fix race, add docs
* spelling, remove todo, add unhandled exception log
* cleanup, revert unintended change
* another unintended change
* review stuff
* add ParallelMergeCombiningSequenceBenchmark, fixes
* hyper-threading is the enemy
* fix initial start delay, lol
* parallelism computer now balances partition sizes to partition counts using sqrt of sequence count instead of sequence count by 2
* fix those important style issues with the benchmarks code
* lazy sequence creation for benchmarks
* more benchmark comments
* stable sequence generation time
* update defaults to use 100ms target time, 4096 batch size, 16384 initial yield, also update user docs
* add jmh thread based benchmarks, cleanup some stuff
* oops
* style
* add spread to jmh thread benchmark start range, more comments to benchmarks parameters and purpose
* retool benchmark to allow modeling more typical heterogenous heavy workloads
* spelling
* fix
* refactor benchmarks
* formatting
* docs
* add maxThreadStartDelay parameter to threaded benchmark
* why does catch need to be on its own line but else doesnt
* IndexerSQLMetadataStorageCoordinator.getTimelineForIntervalsWithHandle() don't fetch abutting intervals; simplify getUsedSegmentsForIntervals()
* Add VersionedIntervalTimeline.findNonOvershadowedObjectsInInterval() method; Propagate the decision about whether only visible segmetns or visible and overshadowed segments should be returned from IndexerMetadataStorageCoordinator's methods to the user logic; Rename SegmentListUsedAction to RetrieveUsedSegmentsAction, SegmetnListUnusedAction to RetrieveUnusedSegmentsAction, and UsedSegmentLister to UsedSegmentsRetriever
* Fix tests
* More fixes
* Add javadoc notes about returning Collection instead of Set. Add JacksonUtils.readValue() to reduce boilerplate code
* Fix KinesisIndexTaskTest, factor out common parts from KinesisIndexTaskTest and KafkaIndexTaskTest into SeekableStreamIndexTaskTestBase
* More test fixes
* More test fixes
* Add a comment to VersionedIntervalTimelineTestBase
* Fix tests
* Set DataSegment.size(0) in more tests
* Specify DataSegment.size(0) in more places in tests
* Fix more tests
* Fix DruidSchemaTest
* Set DataSegment's size in more tests and benchmarks
* Fix HdfsDataSegmentPusherTest
* Doc changes addressing comments
* Extended doc for visibility
* Typo
* Typo 2
* Address comment
* Fix dependency analyze warnings
Update the maven dependency plugin to the latest version and fix all
warnings for unused declared and used undeclared dependencies in the
compile scope. Added new travis job to add the check to CI. Also fixed
some source code files to use the correct packages for their imports and
updated druid-forbidden-apis to prevent regressions.
* Address review comments
* Adjust scope for org.glassfish.jaxb:jaxb-runtime
* Fix dependencies for hdfs-storage
* Consolidate netty4 versions
* Enable code coverage
Code coverage was disabled via
https://github.com/apache/incubator-druid/pull/3122 due to an issue with
cobertura in Travis CI. Switch code coverage tool from cobertura to
jacoco to avoid issue and re-enable coveralls for Travis CI.
* Exclude non-production code
* Exclude benchmark generated code
* Exclude DruidTestRunnerFactory
* Fix dependency analyze warnings
Update the maven dependency plugin to the latest version and fix all
warnings for unused declared and used undeclared dependencies in the
compile scope. Added new travis job to add the check to CI. Also fixed
some source code files to use the correct packages for their imports.
* Fix licenses and dependencies
* Fix licenses and dependencies again
* Fix integration test dependency
* Address review comments
* Fix unit test dependencies
* Fix integration test dependency
* Fix integration test dependency again
* Fix integration test dependency third time
* Fix integration test dependency fourth time
* Fix compile error
* Fix assert package
* 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.
* Rename io.druid to org.apache.druid.
* Fix META-INF files and remove some benchmark results.
* MonitorsConfig update for metrics package migration.
* Reorder some dimensions in inner queries for some reason.
* Fix protobuf tests.
* Enable parallel test
* Remove unnecessary NotThreadSafe annocation
* Randomize the start port when finding available ports
* Fix test failure
* Change to handle all negatives
* 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
Jonathan Wei
Chi Cao Minh
jon-wei
Alexander Saydakov
Gian Merlino
Clint Wylie
Roman Leventov
Benedict Jin
Jihoon Son
David Lim
Roman Leventov
Xavier Léauté
Xavier Léauté
Charles Allen
nishant