1. Go through dependencies list one round to ensure compliance. Generated current NOTICE list in all submodules (other apache projects like flink does this).
To be on conservative side regarding licensing, NOTICE.txt lists all dependencies including transitive. Pending Compliance questions reported in https://issues.apache.org/jira/browse/LEGAL-461
2. Automate generating NOTICE.txt files to allow future package compliance issues be identified early as part of code-review process.
3. Added NOTICE.txt and LICENSE.txt to all HUDI jars
(1) Apply transformation when using delta-streamer to ingest data.
(2) Add Hudi Incremental Source for Delta Streamer
(3) Allow delta-streamer config-property to be passed as command-line
(4) Add Hive Integration to Delta-Streamer and address Review comments
(5) Ensure MultiPartKeysValueExtractor handle hive style partition description
(6) Reuse same spark session on both source and transformer
(7) Support extracting partition fields from _hoodie_partition_path for HoodieIncrSource
(8) Reuse Binary Avro coders
(9) Add push down filter for Incremental source
(10) Add Hoodie DeltaStreamer metrics to track total time taken
- 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
- 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
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
