* [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
* [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>
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.
- 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
Adds the neccessary changes to hudi for support of presto querying hudi
merge-on-read table's realtime view.
Co-authored-by: Brandon Scheller <bschelle@amazon.com>
This is to ensure that tests will execute all code paths, even the ones
written under DEBUG log levels. This will improve coverage as well as
ensure there are no surprised when DEBUG log level is enabled in
production.
- Brings more order and cohesion to the classes in hudi-common
- Utils classes related to a particular concept (avro, timeline,...) are placed near to the package
- common.fs package now contains all the filesystem level classes including wrapper filesystem
- bloom.filter package renamed to just bloom
- config package contains classes that help store properties
- common.fs.inline package contains all the inline filesystem classes/impl
- common.table.timeline now consolidates all timeline related classes
- common.table.view consolidates all the classes related to filesystem view metadata
- common.table.timeline.versioning contains all classes related to versioning of timeline
- Fix few unit tests as a result
- Moved the test packages around to match the source file move
- Rename AvroUtils to TimelineMetadataUtils & minor fixes/typos
Summary:
- InputPathHandler class classifies inputPaths into incremental, non incremental and non hoodie paths.
- Incremental queries leverage HoodieCommitMetadata to get partitions that are affected and only lists those partitions as opposed to listing all partitions
- listStatus() processes each category separately
- Docs were talking about storage types before, cWiki moved to "Table"
- Most of code already has HoodieTable, HoodieTableMetaClient - correct naming
- Replacing renaming use of dataset across code/comments
- Few usages in comments and use of Spark SQL DataSet remain unscathed
