- Addresses issue where insert will combine and remove duplicates within batch
- Setting default insert combining to false (write client default)
- Set to true if filtering duplicates on insert/bulk_insert
(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
- 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
- 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
- Merged all filter* and get* methods
- new constructor takes filestatus[]
- All existing tests pass
- FileGroup is all files that belong to a fileID within a partition
- FileSlice is a generation of data and log files, starting at a base commit
- clean/rollback/write paths covered by existing tests
- Snapshot copier fixed to copy metadata file also, and test fixed
- Existing tables need to be repaired by addition of metadata, before this can be rolled out
* Add analytics to site
* Fix ugly favicon
* New & Improved HoodieDeltaStreamer
- Can incrementally consume from HDFS or Kafka, with exactly-once semantics!
- Supports Json/Avro data, Source can also do custom things
- Source is totally pluggable, via reflection
- Key generation is pluggable, currently added SimpleKeyGenerator
- Schema provider is pluggable, currently Filebased schemas
- Configurable field to break ties during preCombine
- Finally, can also plugin the HoodieRecordPayload, to get other merge types than overwriting
- Handles efficient avro serialization in Spark
Pending :
- Rewriting of HiveIncrPullSource
- Hive sync via hoodie-hive
- Cleanup & tests
* Minor fixes from master rebase
* Implementation of HiveIncrPullSource
- Copies commit by commit from source to target
* Adding TimestampBasedKeyGenerator
- Supports unix time & date strings
1. Create HoodieTable abstraction for commits and fileSystemView
2. HoodieMergeOnReadTable created
3. View is now always obtained from the table and the correct view based on the table type is returned
- Refactored timelines to be a single timeline for all active events and one for archived events. CommitTimeline and other timelines can be inferred by applying a filter on the activeTimelime
- Introduced HoodieInstant to abstract different types of action, commit time and if isInFlight
- Implemented other review comments
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
