* `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>
- 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-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>
* [HUDI-2101]support z-order for hudi
* Renaming some configs for consistency/simplicity.
* Minor code cleanups
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
- There are two code paths, where we are taking double locking. this was added as part of adding data table locks to update metadata table. Fixing those flows to avoid taking locks if a parent transaction already acquired a lock.
- Fix is to make Metadata table writer creation aware of the currently inflight action so that it can
make some informed decision about whether bootstrapping is needed for the table and whether
any pending action on the data timeline can be ignored.
* [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>
- This patch introduces rollback plan and rollback.requested instant. Rollback will be done in two phases, namely rollback plan and rollback action. In planning, we prepare the rollback plan and serialize it to rollback.requested. In the rollback action phase, we fetch details from the plan and just delete the files as per the plan. This will ensure final rollback commit metadata will contain all files that got rolled back even if rollback failed midway and retried again.
- Added upgrade and downgrade step to and from 0.9.0. Upgrade adds few table properties. Downgrade recreates timeline server based marker files if any.
- Rollback infers the directory structure and does rollback based on the strategy used while markers were written. "write markers type" in write config is used to determine marker strategy only for new writes.
* [HUDI-2119] Ensure the rolled-back instance was previously synced to the Metadata Table when syncing a Rollback Instant.
If the rolled-back instant was synced to the Metadata Table, a corresponding deltacommit with the same timestamp should have been created on the Metadata Table timeline. To ensure we can always perfomr this check, the Metadata Table instants should not be archived until their corresponding instants are present in the dataset timeline. But ensuring this requires a large number of instants to be kept on the metadata table.
In this change, the metadata table will keep atleast the number of instants that the main dataset is keeping. If the instant being rolled back was before the metadata table timeline, the code will throw an exception and the metadata table will have to be re-bootstrapped. This should be a very rare occurance and should occur only when the dataset is being repaired by rolling back multiple commits or restoring to an much older time.
* Fixed checkstyle
* Improvements from review comments.
Fixed checkstyle
Replaced explicit null check with Option.ofNullable
Removed redundant function getSynedInstantTime
* Renamed getSyncedInstantTime and getSyncedInstantTimeForReader.
Sync is confusing so renamed to getUpdateTime() and getReaderTime().
* Removed getReaderTime which is only for testing as the same method can be accessed during testing differently without making it part of the public interface.
* Fix compilation error
* Reverting changes to HoodieMetadataFileSystemView
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
