- Adds field to RollbackMetadata that capture the logs written for rollback blocks
- Adds field to RollbackMetadata that capture new logs files written by unsynced deltacommits
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
* [HUDI-1479] Use HoodieEngineContext to parallelize fetching of partition paths
* Adding testClass for FileSystemBackedTableMetadata
Co-authored-by: Nishith Agarwal <nagarwal@uber.com>
* [HUDI-1276] [HUDI-1459] Make Clustering/ReplaceCommit and Metadata table be compatible
* Use filesystemview and json format from metadata. Add tests
Co-authored-by: Satish Kotha <satishkotha@uber.com>
* [HUDI-1434] fix incorrect log file path in HoodieWriteStat
* HoodieWriteHandle#close() returns a list of WriteStatus objs
* Handle rolled-over log files and return a WriteStatus per log file written
- Combined data and delete block logging into a single call
- Lazily initialize and manage write status based on returned AppendResult
- Use FSUtils.getFileSize() to set final file size, consistent with other handles
- Added tests around returned values in AppendResult
- Added validation of the file sizes returned in write stat
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
* 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
Remove APIs in `HoodieTestUtils`
- `createCommitFiles`
- `createDataFile`
- `createNewLogFile`
- `createCompactionRequest`
Migrated usages in `TestCleaner#testPendingCompactions`.
Also improved some API names in `HoodieTestTable`.
Migrate deprecated APIs in HoodieTestUtils to HoodieTestTable for test classes
- TestClientRollback
- TestCopyOnWriteRollbackActionExecutor
Use FileCreateUtils APIs in CompactionTestUtils.
Then remove unused deprecated APIs after migration.
* [HUDI-995] Use HoodieTestTable in more classes
Migrate test data prep logic in
- TestStatsCommand
- TestHoodieROTablePathFilter
Re-implement methods for create new commit times in HoodieTestUtils and HoodieClientTestHarness
- Move relevant APIs to HoodieTestTable
- Migrate usages
After changing to HoodieTestTable APIs, removed unused deprecated APIs in HoodieTestUtils
Add new Payload(OverwriteNonDefaultsWithLatestAvroPayload) for updating specified fields in storage
## Brief change log
update current value for several fields that you want to change.
The default payload OverwriteWithLatestAvroPayload overwrite the whole record when
compared to `orderingVal`.This doesn't meet our need when we just want to change specified fields.
For example: (suppose Default value is null)
```
current Value
Field: name age gender
Value: karl 20 male
```
```
insert Value
Field: name age gender
Value: null 30 null
```
```
After insert:
Field: name age gender
Value: karl 30 male
```
## Verify this pull request
Added TestOverwriteNonDefaultsWithLatestAvroPayload to verify the change.
When unit tests are run on shared machines (e.g. jenkins cluster), the unit tests sometimes fail due to BindException in starting HDFS Cluster. This is because the port chosen may have been bound by another process using the same machine. The fix is to retry the port selection a few times.
- Introduce HoodieWriteableTestTable for writing records into files
- Migrate writeParquetFiles() in HoodieClientTestUtils to HoodieWriteableTestTable
- Adopt HoodieWrittableTestTable for test cases in
- ITTestRepairsCommand.java
- TestHoodieIndex.java
- TestHoodieKeyLocationFetchHandle.java
- TestHoodieGlobalBloomIndex.java
- TestHoodieBloomIndex.java
- Renamed HoodieTestTable and FileCreateUtils APIs
- dataFile changed to baseFile
* [HUDI-960] Implementation of the HFile base and log file format.
1. Includes HFileWriter and HFileReader
2. Includes HFileInputFormat for both snapshot and realtime input format for Hive
3. Unit test for new code
4. IT for using HFile format and querying using Hive (Presto and SparkSQL are not supported)
Advantage:
HFile file format saves data as binary key-value pairs. This implementation chooses the following values:
1. Key = Hoodie Record Key (as bytes)
2. Value = Avro encoded GenericRecord (as bytes)
HFile allows efficient lookup of a record by key or range of keys. Hence, this base file format is well suited to applications like RFC-15, RFC-08 which will benefit from the ability to lookup records by key or search in a range of keys without having to read the entire data/log format.
Limitations:
HFile storage format has certain limitations when used as a general purpose data storage format.
1. Does not have a implemented reader for Presto and SparkSQL
2. Is not a columnar file format and hence may lead to lower compression levels and greater IO on query side due to lack of column pruning
Other changes:
- Remove databricks/avro from pom
- Fix HoodieClientTestUtils from not using scala imports/reflection based conversion etc
- Breaking up limitFileSize(), per parquet and hfile base files
- Added three new configs for HoodieHFileConfig - prefetchBlocksOnOpen, cacheDataInL1, dropBehindCacheCompaction
- Throw UnsupportedException in HFileReader.getRecordKeys()
- Updated HoodieCopyOnWriteTable to create the correct merge handle (HoodieSortedMergeHandle for HFile and HoodieMergeHandle otherwise)
* Fixing checkstyle
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
- Adding ability to use native spark row writing for bulk_insert
- Controlled by `ENABLE_ROW_WRITER_OPT_KEY` datasource write option
- Introduced KeyGeneratorInterface in hudi-client, moved KeyGenerator back to hudi-spark
- Simplified the new API additions to just two new methods : getRecordKey(row), getPartitionPath(row)
- Fixed all built-in key generators with new APIs
- Made the field position map lazily created upon the first call to row based apis
- Implemented native row based key generators for CustomKeyGenerator
- Fixed all the tests, with these new APIs
Co-authored-by: Balaji Varadarajan <varadarb@uber.com>
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
- This PR implements Spark Datasource for MOR table in the RDD approach.
- Implemented SnapshotRelation
- Implemented HudiMergeOnReadRDD
- Implemented separate Iterator to handle merge and unmerge record reader.
- Added TestMORDataSource to verify this feature.
- Clean up test file name, add tests for mixed query type tests
- We can now revert the change made in DefaultSource
Co-authored-by: Vinoth Chandar <vchandar@confluent.io>
- [HUDI-418] Bootstrap Index Implementation using HFile with unit-test
- [HUDI-421] FileSystem View Changes to support Bootstrap with unit-tests
- [HUDI-424] Implement Query Side Integration for querying tables containing bootstrap file slices
- [HUDI-423] Implement upsert functionality for handling updates to these bootstrap file slices
- [HUDI-421] Bootstrap Write Client with tests
- [HUDI-425] Added HoodieDeltaStreamer support
- [HUDI-899] Add a knob to change partition-path style while performing metadata bootstrap
- [HUDI-900] Metadata Bootstrap Key Generator needs to handle complex keys correctly
- [HUDI-424] Simplify Record reader implementation
- [HUDI-423] Implement upsert functionality for handling updates to these bootstrap file slices
- [HUDI-420] Hoodie Demo working with hive and sparkSQL. Also, Hoodie CLI working with bootstrap tables
Co-authored-by: Mehrotra <uditme@amazon.com>
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
Co-authored-by: Balaji Varadarajan <varadarb@uber.com>
