* 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-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>
* [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>
* Adding support to ingest records with old schema after table's schema is evolved
* Rebasing against latest master
- Trimming test file to be < 800 lines
- Renaming config names
* Addressing feedback
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
* [HUDI-845] Added locking capability to allow multiple writers
1. Added LockProvider API for pluggable lock methodologies
2. Added Resolution Strategy API to allow for pluggable conflict resolution
3. Added TableService client API to schedule table services
4. Added Transaction Manager for wrapping actions within transactions
Addresses leaks, perf degradation observed during testing. These were regressions from the original rfc-15 PoC implementation.
* Pass a single instance of HoodieTableMetadata everywhere
* Fix tests and add config for enabling metrics
- Removed special casing of assumeDatePartitioning inside FSUtils#getAllPartitionPaths()
- Consequently, IOException is never thrown and many files had to be adjusted
- More diligent handling of open file handles in metadata table
- Added config for controlling reuse of connections
- Added config for turning off fallback to listing, so we can see tests fail
- Changed all ipf listing code to cache/amortize the open/close for better performance
- Timelineserver also reuses connections, for better performance
- Without timelineserver, when metadata table is opened from executors, reuse is not allowed
- HoodieMetadataConfig passed into HoodieTableMetadata#create as argument.
- Fix TestHoodieBackedTableMetadata#testSync
* [HUDI-1479] Use HoodieEngineContext to parallelize fetching of partition paths
* Adding testClass for FileSystemBackedTableMetadata
Co-authored-by: Nishith Agarwal <nagarwal@uber.com>
* Added ability to pass in `properties` to payload methods, so they can perform table/record specific merges
* Added default methods so existing payload classes are backwards compatible.
* Adding DefaultHoodiePayload to honor ordering while merging two records
* Fixing default payload based on feedback
* Fix flaky MOR unit test
* Update Spark APIs to make it be compatible with both spark2 & spark3
* Refactor bulk insert v2 part to make Hudi be able to compile with Spark3
* Add spark3 profile to handle fasterxml & spark version
* Create hudi-spark-common module & refactor hudi-spark related modules
Co-authored-by: Wenning Ding <wenningd@amazon.com>
1. Added the --clean-input and --clean-output parameters to clean the input and output directories before starting the job
2. Added the --delete-old-input parameter to deleted older batches for data already ingested. This helps keep number of redundant files low.
3. Added the --input-parallelism parameter to restrict the parallelism when generating input data. This helps keeping the number of generated input files low.
4. Added an option start_offset to Dag Nodes. Without ability to specify start offsets, data is generated into existing partitions. With start offset, DAG can control on which partition, the data is to be written.
5. Fixed generation of records for correct number of partitions
- In the existing implementation, the partition is chosen as a random long. This does not guarantee exact number of requested partitions to be created.
6. Changed variable blacklistedFields to be a Set as that is faster than List for membership checks.
7. Fixed integer division for Math.ceil. If two integers are divided, the result is not double unless one of the integer is casted to double.
