* [HUDI-845] Added locking capability to allow multiple writers
1. Added LockProvider API for pluggable lock methodologies
2. Added Resolution Strategy API to allow for pluggable conflict resolution
3. Added TableService client API to schedule table services
4. Added Transaction Manager for wrapping actions within transactions
* Fix flaky MOR unit test
* Update Spark APIs to make it be compatible with both spark2 & spark3
* Refactor bulk insert v2 part to make Hudi be able to compile with Spark3
* Add spark3 profile to handle fasterxml & spark version
* Create hudi-spark-common module & refactor hudi-spark related modules
Co-authored-by: Wenning Ding <wenningd@amazon.com>
- This change breaks `hudi-client` into `hudi-client-common` and `hudi-spark-client` modules
- Simple usages of Spark using jsc.parallelize() has been redone using EngineContext#map, EngineContext#flatMap etc
- Code changes in the PR, break classes into `BaseXYZ` parent classes with no spark dependencies living in `hudi-client-common`
- Classes on `hudi-spark-client` are named `SparkXYZ` extending the parent classes with all the Spark dependencies
- To simplify/cleanup, HoodieIndex#fetchRecordLocation has been removed and its usages in tests replaced with alternatives
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
* [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>
- Generalize the hive-sync module for syncing to multiple metastores
- Added new options for datasource
- Added new command line for delta streamer
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
- [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>
- Support Glue catalog and other metastore implementations
- Remove shading from hudi utilities bundle
- Add maven profile to optionally shade hive in utilities bundle
- Add spotless format fixing to project
- One time reformatting for conformity
- Build fails for formatting changes and mvn spotless:apply autofixes them
1. Remove dnl utils jar from git
2. Add LICENSE Headers in missing files
3. Fix NOTICE and LICENSE in all HUDI packages and in top-level
4. Fix License wording in certain HUDI source files
5. Include non java/scala code in RAT licensing check
6. Use whitelist to include dependencies as part of timeline-server bundling
- Documented principles applied for redesign at packaging/README.md
- No longer depends on incl commons-codec, commons-io, commons-pool, commons-dbcp, commons-lang, commons-logging, avro-mapred
- Introduce new FileIOUtils & added checkstyle rule for illegal import of above
- Parquet, Avro dependencies moved to provided scope to enable being picked up from Hive/Spark/Presto instead
- Pickup jackson jars for Hive sync tool from HIVE_HOME & unbundling jackson everywhere
- Remove hive-jdbc standalone jar from being bundled in Spark/Hive/Utilities bundles
- 6.5x reduced number of classes across bundles
- Redo all classes based on org.parquet only
- remove unuused dependencies like parquet-hadoop, common-configuration2
- timeline-service does not build a fat jar anymore
- Fix utilities and hadoop-mr bundles based on above
- [HUDI-172] Cleanup Maven POM/Classpath
- Fix ordering of dependencies in poms, to enable better resolution
- Idea is to place more specific ones at the top
- And place dependencies which use them below them
- [HUDI-68] : Automate demo steps on docker setup
- Move hive queries from hive cli to beeline
- Standardize on taking query input from text command files
- Deltastreamer ingest, also does hive sync in a single step
- Spark Incremental Query materialized as a derived Hive table using datasource
- Fix flakiness in HDFS spin up and output comparison
- Code cleanup around streamlining and loc reduction
- Also fixed pom to not shade some hive classs in spark, to enable hive sync
