- Scaffolded `Spark24HoodieParquetFileFormat` extending `ParquetFileFormat` and overriding the behavior of adding partition columns to every row
- Amended `SparkAdapter`s `createHoodieParquetFileFormat` API to be able to configure whether to append partition values or not
- Fallback to append partition values in cases when the source columns are not persisted in data-file
- Fixing HoodieBaseRelation incorrectly handling mandatory columns
* Depend on FSUtils#getRelativePartitionPath(basePath, logFilePath.getParent)
to get the partition.
* If the list of log file paths in the split is empty, then fallback to usual behaviour.
* [HUDI-3290] Different file formats for the partition metadata file.
Partition metadata files are stored in each partition to help identify the base path of a table. These files are saved in the properties file format. Some query engines do not work when non Parquet/ORC files are found in a partition.
Added a new table config 'hoodie.partition.metafile.use.data.format' which when enabled (default false for backward compatibility) ensures that partition metafiles will be saved in the same format as the base files of a dataset.
For new datasets, the config can be set via hudi-cli. Deltastreamer has a new parameter --partition-metafile-use-data-format which will create a table with this setting.
* Code review comments
- Adding a new command to migrate from text to base file formats for meta file.
- Reimplementing readFromFS() to first read the text format, then base format
- Avoid extra exists() checks in readFromFS()
- Added unit tests, enabled parquet format across hoodie-hadoop-mr
- Code cleanup, restructuring, naming consistency.
* Wiring in all the other Spark code paths to respect this config
- Turned on parquet meta format for COW data source tests
- Removed the deltastreamer command line to keep it shorter
* populate HoodiePartitionMetadata#format after readFromFS()
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
Co-authored-by: Raymond Xu <2701446+xushiyan@users.noreply.github.com>
* Remove glob pattern basePath from the deltastreamer tests.
* [HUDI-3689] Fix file scheme config
for CI failure in TestHoodieRealTimeRecordReader
Co-authored-by: Raymond Xu <2701446+xushiyan@users.noreply.github.com>
Refactoring Spark DataSource Relations to avoid code duplication.
Following Relations were in scope:
- BaseFileOnlyViewRelation
- MergeOnReadSnapshotRelaation
- MergeOnReadIncrementalRelation
* Bootstrapping initial support for Metadata Table in Spark Datasource
- Consolidated Avro/Row conversion utilities to center around Spark's AvroDeserializer ; removed duplication
- Bootstrapped HoodieBaseRelation
- Updated HoodieMergeOnReadRDD to be able to handle Metadata Table
- Modified MOR relations to be able to read different Base File formats (Parquet, HFile)
Rebased Parquet-based FileInputFormat impls to inherit from MapredParquetInputFormat, to make sure that Hive is appropriately recognizing those impls and applying corresponding optimizations.
- Converted HoodieRealtimeFileInputFormatBase and HoodieFileInputFormatBase into standalone implementations that could be instantiated as standalone objects (which could be used for delegation)
- Renamed HoodieFileInputFormatBase > HoodieCopyOnWriteTableInputFormat, HoodieRealtimeFileInputFormatBase > HoodieMergeOnReadTableInputFormat
- Scaffolded HoodieParquetFileInputFormatBase for all Parquet impls to inherit from
- Rebased Parquet impls onto HoodieParquetFileInputFormatBase
Unify Hive's MOR implementations to avoid duplication to avoid duplication across implementations for different file-formats (Parquet, HFile, etc)
- Extracted HoodieRealtimeFileInputFormatBase (extending COW HoodieFileInputFormatBase base)
- Rebased Parquet, HFile implementations onto HoodieRealtimeFileInputFormatBase
- Tidying up
* [HUDI-2763] Metadata table records - support for key deduplication and virtual keys
- The backing log format for the metadata table is HFile, a KeyValue type.
Since the key field in the metadata record payload is a duplicate of the
Key in the Cell, the redundant key field in the record can be emptied
to save on the cost.
- HoodieHFileWriter and HoodieHFileDataBlock will now serialize records
with the key field emptied by default. HFile writer tries to find if
the record has metadata payload schema field 'key' and if so it does
the key trimming from the record payload.
- HoodieHFileReader when reading the serialized records back from disk,
it materializes the missing keyFields if any. HFile reader tries to
find if the record has metadata payload schema fiels 'key' and if so
it does the key materialization in the record payload.
- Tests have been added to verify the default virtual keys and key
deduplication support for the metadata table records.
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
* [HUDI-2480] FileSlice after pending compaction-requested instant-time is ignored by MOR snapshot reader
* include file slice after a pending compaction for spark reader
Co-authored-by: garyli1019 <yanjia.gary.li@gmail.com>
* [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>
