- Check to ensure written files are listable on storage
- Docs reflected to capture how this helps with s3 storage
- Unit tests added, corrections to existing tests
- Fix DeltaStreamer to manage archived commits in a separate folder
- Tests redone in the process
- Main changes are to RealtimeRecordReader and how it treats maps/arrays
- Make hive sync work with Hive 1/2 and CDH environments
- Fixes to make corner cases for Hive queries
- Spark Hive integration - Working version across Apache and CDH versions
- Known Issue - https://github.com/uber/hudi/issues/439
- Standardize version of jackson
- DFSPropertiesConfiguration replaces usage of commons PropertiesConfiguration
- Remove dependency on ConstructorUtils
- Throw error if ordering value is not present, during key generation
- Switch to shade plugin for hoodie-utilities
- Added support for consumption for Confluent avro kafka serdes
- Support for Confluent schema registry
- KafkaSource now deals with skews nicely, by doing round robin allocation of source limit across partitions
- Added support for BULK_INSERT operations as well
- Pass in the payload class config properly into HoodieWriteClient
- Fix documentation based on new usage
- Adding tests on deltastreamer, sources and all new util classes.
The code-style rules follow google style with some changes:
1. Increase line length from 100 to 120
2. Disable JavaDoc related checkstyles as this needs more manual work.
Both source and test code are checked for code-style
- Write with COW/MOR paths work fully
- Read with RO view works on both storages*
- Incremental view supported on COW
- Refactored out HoodieReadClient methods, to just contain key based access
- HoodieDataSourceHelpers class can be now used to construct inputs to datasource
- Tests in hoodie-client using new helpers and mechanisms
- Basic tests around save modes & insert/upserts (more to follow)
- Bumped up scala to 2.11, since 2.10 is deprecated & complains with scalatest
- Updated documentation to describe usage
- New sample app written using the DataSource API
- Introduce avro to save clean metadata with details about the last commit that was retained
- Save rollback metadata in the meta timeline
- Create savepoint metadata and add API to createSavepoint, deleteSavepoint and rollbackToSavepoint
- Savepointed commit should not be rolledback or cleaned or archived
- introduce cli commands to show, create and rollback to savepoints
- Write unit tests to test savepoints and rollbackToSavepoints
The following is the gist of changes done
- All low-level operation of creating a commit code was in HoodieClient which made it hard to share code if there was a compaction commit.
- HoodieTableMetadata contained a mix of metadata and filtering files. (Also few operations required FileSystem to be passed in because those were called from TaskExecutors and others had FileSystem as a global variable). Since merge-on-read requires a lot of that code, but will have to change slightly on how it operates on the metadata and how it filters the files. The two set of operation are split into HoodieTableMetaClient and TableFileSystemView.
- Everything (active commits, archived commits, cleaner log, save point log and in future delta and compaction commits) in HoodieTableMetaClient is a HoodieTimeline. Timeline is a series of instants, which has an in-built concept of inflight and completed commit markers.
- A timeline can be queries for ranges, contains and also use to create new datapoint (create a new commit etc). Commit (and all the above metadata) creation/deletion is streamlined in a timeline
- Multiple timelines can be merged into a single timeline, giving us an audit timeline to whatever happened in a hoodie dataset. This also helps with #55.
- Move to java 8 and introduce java 8 succinct syntax in refactored code
