* [HUDI-2285] Adding Synchronous updates to metadata before completion of commits in data timelime.
- This patch adds synchronous updates to metadata table. In other words, every write is first committed to metadata table followed by data table. While reading metadata table, we ignore any delta commits that are present only in metadata table and not in data table timeline.
- Compaction of metadata table is fenced by the condition that we trigger compaction only when there are no inflight requests in datatable. This ensures that all base files in metadata table is always in sync with data table(w/o any holes) and only there could be some extra invalid commits among delta log files in metadata table.
- Due to this, archival of data table also fences itself up until compacted instant in metadata table.
All writes to metadata table happens within the datatable lock. So, metadata table works in one writer mode only. This might be tough to loosen since all writers write to same FILES partition and so, will result in a conflict anyways.
- As part of this, have added acquiring locks in data table for those operations which were not before while committing (rollback, clean, compaction, cluster). To note, we were not doing any conflict resolution. All we are doing here is to commit by taking a lock. So that all writes to metadata table is always a single writer.
- Also added building block to add buckets for partitions, which will be leveraged by other indexes like record level index, etc. For now, FILES partition has only one bucket. In general, any number of buckets per partition is allowed and each partition has a fixed fileId prefix with incremental suffix for each bucket within each partition.
Have fixed [HUDI-2476]. This fix is about retrying a failed compaction if it succeeded in metadata for first time, but failed w/ data table.
- Enabling metadata table by default.
- Adding more tests for metadata table
Co-authored-by: Prashant Wason <pwason@uber.com>
[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>
