* [HUDI-2923] Fixing metadata table reader when metadata compaction is inflight
* Fixing retry of pending compaction in metadata table and enhancing tests
- 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.
* `ZCurveOptimizeHelper` > `ZOrderingIndexHelper`;
Moved Z-index helper under `hudi.index.zorder` package
* Tidying up `ZOrderingIndexHelper`
* Fixing compilation
* Fixed index new/original table merging sequence to always prefer values from new index;
Cleaned up `HoodieSparkUtils`
* Added test for `mergeIndexSql`
* Abstracted Z-index name composition w/in `ZOrderingIndexHelper`;
* Fixed `DataSkippingUtils` to interrupt prunning in case data filter contains non-indexed column reference
* Properly handle exceptions origination during pruning in `HoodieFileIndex`
* Make sure no errors are logged upon encountering `AnalysisException`
* Cleaned up Z-index updating sequence;
Tidying up comments, java-docs;
* Fixed Z-index to properly handle changes of the list of clustered columns
* Tidying up
* `lint`
* Suppressing `JavaDocStyle` first sentence check
* Fixed compilation
* Fixing incorrect `DecimalType` conversion
* Refactored test `TestTableLayoutOptimization`
- Added Z-index table composition test (against fixtures)
- Separated out GC test;
Tidying up
* Fixed tests re-shuffling column order for Z-Index table `DataFrame` to align w/ the one by one loaded from JSON
* Scaffolded `DataTypeUtils` to do basic checks of Spark types;
Added proper compatibility checking b/w old/new index-tables
* Added test for Z-index tables merging
* Fixed import being shaded by creating internal `hudi.util` package
* Fixed packaging for `TestOptimizeTable`
* Revised `updateMetadataIndex` seq to provide Z-index updating process w/ source table schema
* Make sure existing Z-index table schema is sync'd to source table's one
* Fixed shaded refs
* Fixed tests
* Fixed type conversion of Parquet provided metadata values into Spark expected schemas
* Fixed `composeIndexSchema` utility to propose proper schema
* Added more tests for Z-index:
- Checking that Z-index table is built correctly
- Checking that Z-index tables are merged correctly (during update)
* Fixing source table
* Fixing tests to read from Parquet w/ proper schema
* Refactored `ParquetUtils` utility reading stats from Parquet footers
* Fixed incorrect handling of Decimals extracted from Parquet footers
* Worked around issues in javac failign to compile stream's collection
* Fixed handling of `Date` type
* Fixed handling of `DateType` to be parsed as `LocalDate`
* Updated fixture;
Make sure test loads Z-index fixture using proper schema
* Removed superfluous scheme adjusting when reading from Parquet, since Spark is actually able to perfectly restore schema (given Parquet was previously written by Spark as well)
* Fixing race-condition in Parquet's `DateStringifier` trying to share `SimpleDataFormat` object which is inherently not thread-safe
* Tidying up
* Make sure schema is used upon reading to validate input files are in the appropriate format;
Tidying up;
* Worked around javac (1.8) inability to infer expression type properly
* Updated fixtures;
Tidying up
* Fixing compilation after rebase
* Assert clustering have in Z-order layout optimization testing
* Tidying up exception messages
* XXX
* Added test validating Z-index lookup filter correctness
* Added more test-cases;
Tidying up
* Added tests for string expressions
* Fixed incorrect Z-index filter lookup translations
* Added more test-cases
* Added proper handling on complex negations of AND/OR expressions by pushing NOT operator down into inner expressions for appropriate handling
* Added `-target:jvm-1.8` for `hudi-spark` module
* Adding more tests
* Added tests for non-indexed columns
* Properly handle non-indexed columns by falling back to a re-write of containing expression as `TrueLiteral` instead
* Fixed tests
* Removing the parquet test files and disabling corresponding tests
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
* [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-1937] Rollback unfinished replace commit to allow updates while clustering
* Revert and delete requested replacecommit too
* Rollback pending clustering instants transactionally
* No double locking and add a config to enable rollback
* Update config to be clear about rollback only on conflict
- Adds support for generating commit timestamps with millisecs granularity.
- Older commit timestamps (in secs granularity) will be suffixed with 999 and parsed with millisecs format.
* [HUDI-1870] Add more Spark CI build tasks
- build for spark3.0.x
- build for spark-shade-unbundle-avro
- fix build failures
- delete unnecessary assertion for spark 3.0.x
- use AvroConversionUtils#convertAvroSchemaToStructType instead of calling SchemaConverters#toSqlType directly to solve the compilation failures with spark-shade-unbundle-avro (#5)
Co-authored-by: Yann <biyan900116@gmail.com>
- Metadata table today has virtual keys disabled, thereby populating the metafields
for each record written out and increasing the overall storage space used. Hereby
adding virtual keys support for metadata table so that metafields are disabled
for metadata table records.
- Adding a custom KeyGenerator for Metadata table so as to not rely on the
default Base/SimpleKeyGenerators which currently look for record key
and partition field set in the table config.
- AbstractHoodieLogRecordReader's version of processing next data block and
createHoodieRecord() will be a generic version and making the derived class
HoodieMetadataMergedLogRecordReader take care of the special creation of
records from explictly passed in partition names.
* Enabling timeline server based markers
* Enabling timeline server based markers and marker based rollback
* Removing constraint that timeline server can be enabled only for hdfs
* Fixing tests
* [HUDI-2634] Improved the metadata table bootstrap for very large tables.
Following improvements are implemented:
1. Memory overhead reduction:
- Existing code caches FileStatus for each file in memory.
- Created a new class DirectoryInfo which is used to cache a director's file list with parts of the FileStatus (only filename and file len). This reduces the memory requirements.
2. Improved parallelism:
- Existing code collects all the listing to the Driver and then creates HoodieRecord on the Driver.
- This takes a long time for large tables (11million HoodieRecords to be created)
- Created a new function in SparkRDDWriteClient specifically for bootstrap commit. In it, the HoodieRecord creation is parallelized across executors so it completes fast.
3. Fixed setting to limit the number of parallel listings:
- Existing code had a bug wherein 1500 executors were hardcoded to perform listing. This leads to exception due to limit in the spark's result memory.
- Corrected the use of the config.
Result:
Dataset has 1299 partitions and 12Million files.
file listing time=1.5mins
HoodieRecord creation time=13seconds
deltacommit duration=2.6mins
Co-authored-by: Sivabalan Narayanan <n.siva.b@gmail.com>
