This change is addressing issues in regards to Metadata Table observing ingesting duplicated records leading to it persisting incorrect file-sizes for the files referred to in those records.
There are multiple issues that were leading to that:
- [HUDI-3322] Incorrect Rollback Plan generation: Rollback Plan generated for MOR tables was overly expansively listing all log-files with the latest base-instant as the ones that have been affected by the rollback, leading to invalid MT records being ingested referring to those.
- [HUDI-3343] Metadata Table including Uncommitted Log Files during Bootstrap: Since MT is bootstrapped at the end of the commit operation execution (after FS activity, but before committing to the timeline), it was actually incorrectly ingesting some files that were part of the intermediate state of the operation being committed.
This change will unblock Stack of PRs based off #4556
* [HUDI-2763] Metadata table records - support for key deduplication and virtual keys
- The backing log format for the metadata table is HFile, a KeyValue type.
Since the key field in the metadata record payload is a duplicate of the
Key in the Cell, the redundant key field in the record can be emptied
to save on the cost.
- HoodieHFileWriter and HoodieHFileDataBlock will now serialize records
with the key field emptied by default. HFile writer tries to find if
the record has metadata payload schema field 'key' and if so it does
the key trimming from the record payload.
- HoodieHFileReader when reading the serialized records back from disk,
it materializes the missing keyFields if any. HFile reader tries to
find if the record has metadata payload schema fiels 'key' and if so
it does the key materialization in the record payload.
- Tests have been added to verify the default virtual keys and key
deduplication support for the metadata table records.
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
* [HUDI-3030] InProcessLockPovider as default when any async servcies enabled with no lock provider override
- Making InProcessLockProvider as the default lock provider when
any async services are enabled and when no lock provider is
explicitly set.
- This is the workaround for metadata table updates racing with
async table serice operations
* [HUDI-3030] InProcessLockPovider as default when any async servcies enabled with no lock provider override
- Renaming isAnyTableServicesInline/Async() to areAnyTableServicesInline/Async()
* [HUDI-3030] InProcessLockPovider as default when any async servcies enabled with no lock provider override
- Additionally checking for write config properties when verifying
the lock provider override. Updated the unit test for this case.
- Made FileSystemBasedLockProviderTestClass thread safe and fixed the
tryLock retry logic.
- Made TestHoodieClientMultiWriter. testHoodieClientBasicMultiWriter
deterministic in verifying the HoodieWriteConflictException.
* [HUDI-3029] Transaction manager: avoid deadlock when doing begin and end transactions
- Transaction manager has begin and end transactions as synchronized methods.
Based on the lock provider implementaion, this can lead to deadlock
situation when the underlying lock() calls are blocking or with a long timeout.
- Fixing transaction manager begin and end transactions to not get to deadlock
and to not assume anything on the lock provider implementation.
- Adding Local JVM process based lock provider implementation
- This local lock provider can be used by a single writer process with async
table operations to guard the metadata tabl against concurrent updates.
- Changes the default config of marker type (HoodieWriteConfig.MARKERS_TYPE or hoodie.write.markers.type) from DIRECT to TIMELINE_SERVER_BASED for Spark Engine.
- Adds engine-specific marker type configs: Spark -> TIMELINE_SERVER_BASED, Flink -> DIRECT, Java -> DIRECT.
- Uses DIRECT markers as well for Spark structured streaming due to timeline server only available for the first mini-batch.
- Fixes the marker creation method for non-partitioned table in TimelineServerBasedWriteMarkers.
- Adds the fallback to direct markers even when TIMELINE_SERVER_BASED is configured, in WriteMarkersFactory: when HDFS is used, or embedded timeline server is disabled, the fallback to direct markers happens.
- Fixes the closing of timeline service.
- Fixes tests that depend on markers, mainly by starting the timeline service for each test.
* Rebased `DFSPropertiesConfiguration` to access Hadoop config in liue of FS to avoid confusion
* Fixed `readConfig` to take Hadoop's `Configuration` instead of FS;
Fixing usages
* Added test for local FS access
* Rebase to use `FSUtils.getFs`
* Combine properties provided as a file along w/ overrides provided from the CLI
* Added helper utilities to `HoodieClusteringConfig`;
Make sure corresponding config methods fallback to defaults;
* Fixed DeltaStreamer usage to respect properly combined configuration;
Abstracted `HoodieClusteringConfig.from` convenience utility to init Clustering config from `Properties`
* Tidying up
* `lint`
* Reverting changes to `HoodieWriteConfig`
* Tdiying up
* Fixed incorrect merge of the props
* Converted `HoodieConfig` to wrap around `Properties` into `TypedProperties`
* Fixed compilation
* Fixed compilation
* [HUDI-2443] Hudi KVComparator for all HFile writer usages
- Hudi relies on custom class shading for Hbase's KeyValue.KVComparator to
avoid versioning and class loading issues. There are few places which are
still using the Hbase's comparator class directly and version upgrades
would make them obsolete. Refactoring the HoodieKVComparator and making
all HFile writer creation using the same shaded class.
* [HUDI-2443] Hudi KVComparator for all HFile writer usages
- Moving HoodieKVComparator from common.bootstrap.index to common.util
* [HUDI-2443] Hudi KVComparator for all HFile writer usages
- Retaining the old HoodieKVComparatorV2 for boostrap case. Adding the
new comparator as HoodieKVComparatorV2 to differentiate from the old
one.
* [HUDI-2443] Hudi KVComparator for all HFile writer usages
- Renamed HoodieKVComparatorV2 to HoodieMetadataKVComparator and moved it
under the package org.apache.hudi.metadata.
* Make comparator classname configurable
* Revert new config and address other review comments
Co-authored-by: Sagar Sumit <sagarsumit09@gmail.com>
* [HUDI-2332] Add clustering and compaction in Kafka Connect Sink
* Disable validation check on instant time for compaction and adjust configs
* Add javadocs
* Add clustering and compaction config
* Fix transaction causing missing records in the target table
* Add debugging logs
* Fix kafka offset sync in participant
* Adjust how clustering and compaction are configured in kafka-connect
* Fix clustering strategy
* Remove irrelevant changes from other published PRs
* Update clustering logic and others
* Update README
* Fix test failures
* Fix indentation
* Fix clustering config
* Add JavaCustomColumnsSortPartitioner and make async compaction enabled by default
* Add test for JavaCustomColumnsSortPartitioner
* Add more changes after IDE sync
* Update README with clarification
* Fix clustering logic after rebasing
* Remove unrelated changes
* [HUDI-2101]support z-order for hudi
* Renaming some configs for consistency/simplicity.
* Minor code cleanups
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
* [HUDI-2285] Adding Synchronous updates to metadata before completion of commits in data timelime.
- This patch adds synchronous updates to metadata table. In other words, every write is first committed to metadata table followed by data table. While reading metadata table, we ignore any delta commits that are present only in metadata table and not in data table timeline.
- Compaction of metadata table is fenced by the condition that we trigger compaction only when there are no inflight requests in datatable. This ensures that all base files in metadata table is always in sync with data table(w/o any holes) and only there could be some extra invalid commits among delta log files in metadata table.
- Due to this, archival of data table also fences itself up until compacted instant in metadata table.
All writes to metadata table happens within the datatable lock. So, metadata table works in one writer mode only. This might be tough to loosen since all writers write to same FILES partition and so, will result in a conflict anyways.
- As part of this, have added acquiring locks in data table for those operations which were not before while committing (rollback, clean, compaction, cluster). To note, we were not doing any conflict resolution. All we are doing here is to commit by taking a lock. So that all writes to metadata table is always a single writer.
- Also added building block to add buckets for partitions, which will be leveraged by other indexes like record level index, etc. For now, FILES partition has only one bucket. In general, any number of buckets per partition is allowed and each partition has a fixed fileId prefix with incremental suffix for each bucket within each partition.
Have fixed [HUDI-2476]. This fix is about retrying a failed compaction if it succeeded in metadata for first time, but failed w/ data table.
- Enabling metadata table by default.
- Adding more tests for metadata table
Co-authored-by: Prashant Wason <pwason@uber.com>
Update Azure CI ubuntu from 16.04 to 18.04 due to 16.04 will be removed soon
Fixed some consistently failed tests
* fix TestCOWDataSourceStorage TestMORDataSourceStorage
* reset mocks
Also update readme badge
Co-authored-by: Raymond Xu <2701446+xushiyan@users.noreply.github.com>
* [HUDI-1989] Refactor clustering tests for MoR table
* refactor assertion helper
* add CheckedFunction
* SparkClientFunctionalTestHarness.java
* put back original test case
* move testcases out from TestHoodieMergeOnReadTable.java
* add TestHoodieSparkMergeOnReadTableRollback.java
* use SparkClientFunctionalTestHarness
* add tag
