- This adds a restore plan and serializes it to restore.requested meta file in timeline. This also means that we are introducing schedule and execution phases for restore which was not present before.
* [HUDI-1295] Metadata Index - Bloom filter and Column stats index to speed up index lookups
- Today, base files have bloom filter at their footers and index lookups
have to load the base file to perform any bloom lookups. Though we have
interval tree based file purging, we still end up in significant amount
of base file read for the bloom filter for the end index lookups for the
keys. This index lookup operation can be made more performant by having
all the bloom filters in a new metadata partition and doing pointed
lookups based on keys.
* [HUDI-1295] Metadata Index - Bloom filter and Column stats index to speed up index lookups
- Adding indexing support for clean, restore and rollback operations.
Each of these operations will now be converted to index records for
bloom filter and column stats additionally.
* [HUDI-1295] Metadata Index - Bloom filter and Column stats index to speed up index lookups
- Making hoodie key consistent for both column stats and bloom index by
including fileId instead of fileName, in both read and write paths.
- Performance optimization for looking up records in the metadata table.
- Avoiding multi column sorting needed for HoodieBloomMetaIndexBatchCheckFunction
* [HUDI-1295] Metadata Index - Bloom filter and Column stats index to speed up index lookups
- HoodieBloomMetaIndexBatchCheckFunction cleanup to remove unused classes
- Base file checking before reading the file footer for bloom or column stats
* [HUDI-1295] Metadata Index - Bloom filter and Column stats index to speed up index lookups
- Updating the bloom index and column stats index to have full file name
included in the key instead of just file id.
- Minor test fixes.
* [HUDI-1295] Metadata Index - Bloom filter and Column stats index to speed up index lookups
- Fixed flink commit method to handle metadata table all partition update records
- TestBloomIndex fixes
* [HUDI-1295] Metadata Index - Bloom filter and Column stats index to speed up index lookups
- SparkHoodieBloomIndexHelper code simplification for various config modes
- Signature change for getBloomFilters() and getColumnStats(). Callers can
just pass in interested partition and file names, the index key is then
constructed internally based on the passed in parameters.
- KeyLookupHandle and KeyLookupResults code refactoring
- Metadata schema changes - removed the reserved field
* [HUDI-1295] Metadata Index - Bloom filter and Column stats index to speed up index lookups
- Removing HoodieColumnStatsMetadata and using HoodieColumnRangeMetadata instead.
Fixed the users of the the removed class.
* [HUDI-1295] Metadata Index - Bloom filter and Column stats index to speed up index lookups
- Extending meta index test to cover deletes, compactions, clean
and restore table operations. Also, fixed the getBloomFilters()
and getColumnStats() to account for deleted entries.
* [HUDI-1295] Metadata Index - Bloom filter and Column stats index to speed up index lookups
- Addressing review comments - java doc for new classes, keys sorting for
lookup, index methods renaming.
* [HUDI-1295] Metadata Index - Bloom filter and Column stats index to speed up index lookups
- Consolidated the bloom filter checking for keys in to one
HoodieMetadataBloomIndexCheckFunction instead of a spearate batch
and lazy mode. Removed all the configs around it.
- Made the metadata table partition file group count configurable.
- Fixed the HoodieKeyLookupHandle to have auto closable file reader
when checking bloom filter and range keys.
- Config property renames. Test fixes.
* [HUDI-1295] Metadata Index - Bloom filter and Column stats index to speed up index lookups
- Enabling column stats indexing for all columns by default
- Handling column stat generation errors and test update
* [HUDI-1295] Metadata Index - Bloom filter and Column stats index to speed up index lookups
- Metadata table partition file group count taken from the slices when
the table is bootstrapped.
- Prep records for the commit refactored to the base class
- HoodieFileReader interface changes for filtering keys
- Multi column and data types support for colums stats index
* [HUDI-1295] Metadata Index - Bloom filter and Column stats index to speed up index lookups
- rebase to latest master and merge fixes for the build and test failures
* [HUDI-1295] Metadata Index - Bloom filter and Column stats index to speed up index lookups
- Extending the metadata column stats type payload schema to include
more statistics about the column ranges to help query integration.
* [HUDI-1295] Metadata Index - Bloom filter and Column stats index to speed up index lookups
- Addressing review comments
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
Refactoring layout optimization (clustering) flow to
- Enable support for linear (lexicographic) ordering as one of the ordering strategies (along w/ Z-order, Hilbert)
- Reconcile Layout Optimization and Clustering configuration to be more congruent
* Introduce hudi-spark3-common and hudi-spark2-common modules to place classes that would be reused in different spark versions, also introduce hudi-spark3.1.x to support spark 3.1.x.
* Introduce hudi format under hudi-spark2, hudi-spark3, hudi-spark3.1.x modules and change the hudi format in original hudi-spark module to hudi_v1 format.
* Manually tested on Spark 3.1.2 and Spark 3.2.0 SQL.
* Added a README.md file under hudi-spark-datasource module.
- There is a chance that the actual write eventually failed in data table but commit was successful in Metadata table, and if compaction was triggered in MDT, compaction could have included the uncommitted data. But once compacted, it may never be ignored while reading from metadata table. So, this patch fixes the bug. Metadata table compaction is triggered before applying the commit to metadata table to circumvent this issue.
* [HUDI-2909] Handle logical type in TimestampBasedKeyGenerator
Timestampbased key generator was returning diff values for row writer and non row writer path. this patch fixes it and is guarded by a config flag (`hoodie.datasource.write.keygenerator.consistent.logical.timestamp.enabled`)
* [HUDI-2154] Add index key field to HoodieKey
* [HUDI-2157] Add the bucket index and its read/write implemention of Spark engine.
* revert HUDI-2154 add index key field to HoodieKey
* fix all comments and introduce a new tricky way to get index key at runtime
support double insert for bucket index
* revert spark read optimizer based on bucket index
* add the storage layout
* index tag, hash function and add ut
* fix ut
* address partial comments
* Code review feedback
* add layout config and docs
* fix ut
* rename hoodie.layout and rebase master
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
* [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.
* [HUDI-2923] Fixing metadata table reader when metadata compaction is inflight
* Fixing retry of pending compaction in metadata table and enhancing tests
* `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.
* [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>
