(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
- 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.
- Fixes#246
- Bump up default parallelism to 1500, to handle large upserts
- Add docs on s3 confuration & tuning tips with tested spark knobs
- Fix bug to not duplicate hoodie metadata fields when input dataframe is another hoodie dataset
- Improve speed of ROTablePathFilter by removing directory check
- Move to spark-avro 4.0 to handle issue with nested fields with same name
- Keep AvroConversionUtils in sync with spark-avro 4.0
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
- Introduced concept of converters to be able to serde generic datatype for SpillableMap
- Fixed/Added configs to Hoodie Configs
- Changed HoodieMergeHandle to start using SpillableMap
- When append() is not supported, rollover to new file always (instead of failing)
- Provide way to configure archive log folder (avoids small files inside .hoodie)
- Datasets written via Spark datasource archive to .hoodie/archived
- HoodieClientExample will now retain only 2,3 commits to exercise archival path during dev cycles
- Few tweaks to code structure around CommitArchiveLog
- Reviving PR 191, to make FileSystem creation off actual path
- Streamline all filesystem access to HoodieTableMetaClient
- Hadoop Conf from Spark Context serialized & passed to executor code too
- Pick up env vars prefixed with HOODIE_ENV_ into Configuration object
- Cleanup usage of FSUtils.getFS, piggybacking off HoodieTableMetaClient.getFS
- Adding s3a to supported schemes & support escaping "." in env vars
- Tests use HoodieTestUtils.getDefaultHadoopConf
- 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
