[global-hive-sync-tool] Add a global hive sync tool to sync hudi table across clusters. Add a way to rollback the replicated time stamp if we fail to sync or if we partly sync
Co-authored-by: Jagmeet Bali <jsbali@uber.com>
The HoodieInputFormatUtils.getTableMetaClientByBasePath returns the map
with table base path as keys while the HoodieRealtimeInputFormatUtils
query it with the partition path.
* [HUDI-1789] In HoodieParquetInoutFormat we currently default to the latest version of base files.
This PR attempts to add a new jobConf
`hoodie.%s.consume.snapshot.time`
This new config will allow us to read older snapshots.
- Reusing hoodie.%s.consume.commit for point in time snapshot queries as well.
- Adding javadocs and some more tests
Addresses leaks, perf degradation observed during testing. These were regressions from the original rfc-15 PoC implementation.
* Pass a single instance of HoodieTableMetadata everywhere
* Fix tests and add config for enabling metrics
- Removed special casing of assumeDatePartitioning inside FSUtils#getAllPartitionPaths()
- Consequently, IOException is never thrown and many files had to be adjusted
- More diligent handling of open file handles in metadata table
- Added config for controlling reuse of connections
- Added config for turning off fallback to listing, so we can see tests fail
- Changed all ipf listing code to cache/amortize the open/close for better performance
- Timelineserver also reuses connections, for better performance
- Without timelineserver, when metadata table is opened from executors, reuse is not allowed
- HoodieMetadataConfig passed into HoodieTableMetadata#create as argument.
- Fix TestHoodieBackedTableMetadata#testSync
* [HUDI-1479] Use HoodieEngineContext to parallelize fetching of partition paths
* Adding testClass for FileSystemBackedTableMetadata
Co-authored-by: Nishith Agarwal <nagarwal@uber.com>
* [HUDI-1434] fix incorrect log file path in HoodieWriteStat
* HoodieWriteHandle#close() returns a list of WriteStatus objs
* Handle rolled-over log files and return a WriteStatus per log file written
- Combined data and delete block logging into a single call
- Lazily initialize and manage write status based on returned AppendResult
- Use FSUtils.getFileSize() to set final file size, consistent with other handles
- Added tests around returned values in AppendResult
- Added validation of the file sizes returned in write stat
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
* Incremental Query should work even when there are partitions that have no incremental changes
Co-authored-by: Sivabalan Narayanan <sivabala@uber.com>
* [HUDI-892] RealtimeParquetInputFormat skip adding projection columns if there are no log files
* [HUDI-892] for test
* [HUDI-892] fix bug generate array from split
* [HUDI-892] revert test log
Remove APIs in `HoodieTestUtils`
- `createCommitFiles`
- `createDataFile`
- `createNewLogFile`
- `createCompactionRequest`
Migrated usages in `TestCleaner#testPendingCompactions`.
Also improved some API names in `HoodieTestTable`.
* [HUDI-995] Use HoodieTestTable in more classes
Migrate test data prep logic in
- TestStatsCommand
- TestHoodieROTablePathFilter
Re-implement methods for create new commit times in HoodieTestUtils and HoodieClientTestHarness
- Move relevant APIs to HoodieTestTable
- Migrate usages
After changing to HoodieTestTable APIs, removed unused deprecated APIs in HoodieTestUtils
* [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>
The purpose of this pull request is to implement changes required on Hudi side to get Bootstrapped tables integrated with Presto. The testing was done against presto 0.232 and following changes were identified to make it work:
Annotation UseRecordReaderFromInputFormat is required on HoodieParquetInputFormat as well, because the reading for bootstrapped tables needs to happen through record reader to be able to perform the merge. On presto side, this annotation is already handled.
We need to internally maintain VIRTUAL_COLUMN_NAMES because presto's internal hive version hive-apache-1.2.2 has VirutalColumn as a class, versus the one we depend on in hudi which is an enum.
Dependency changes in hudi-presto-bundle to avoid runtime exceptions.
- This PR implements Spark Datasource for MOR table in the RDD approach.
- Implemented SnapshotRelation
- Implemented HudiMergeOnReadRDD
- Implemented separate Iterator to handle merge and unmerge record reader.
- Added TestMORDataSource to verify this feature.
- Clean up test file name, add tests for mixed query type tests
- We can now revert the change made in DefaultSource
Co-authored-by: Vinoth Chandar <vchandar@confluent.io>
- [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>
- Consolidate transform functions for tests in Transformations.java
- Consolidate assertion functions for tests in Assertions.java
- Make use of SchemaTestUtil for loading schema from resource
Notable changes:
1. HoodieFileWriter and HoodieFileReader abstractions for writer/reader side of a base file format
2. HoodieDataBlock abstraction for creation specific data blocks for base file formats. (e.g. Parquet has HoodieAvroDataBlock)
3. All hardocded references to Parquet / Parquet based classes have been abstracted to call methods which accept a base file format
4. HiveSyncTool accepts the base file format as a CLI parameter
5. HoodieDeltaStreamer accepts the base file format as a CLI parameter
6. HoodieSparkSqlWriter accepts the base file format as a parameter
