* `ZCurveOptimizeHelper` > `ZOrderingIndexHelper`;
Moved Z-index helper under `hudi.index.zorder` package
* Tidying up `ZOrderingIndexHelper`
* Fixing compilation
* Fixed index new/original table merging sequence to always prefer values from new index;
Cleaned up `HoodieSparkUtils`
* Added test for `mergeIndexSql`
* Abstracted Z-index name composition w/in `ZOrderingIndexHelper`;
* Fixed `DataSkippingUtils` to interrupt prunning in case data filter contains non-indexed column reference
* Properly handle exceptions origination during pruning in `HoodieFileIndex`
* Make sure no errors are logged upon encountering `AnalysisException`
* Cleaned up Z-index updating sequence;
Tidying up comments, java-docs;
* Fixed Z-index to properly handle changes of the list of clustered columns
* Tidying up
* `lint`
* Suppressing `JavaDocStyle` first sentence check
* Fixed compilation
* Fixing incorrect `DecimalType` conversion
* Refactored test `TestTableLayoutOptimization`
- Added Z-index table composition test (against fixtures)
- Separated out GC test;
Tidying up
* Fixed tests re-shuffling column order for Z-Index table `DataFrame` to align w/ the one by one loaded from JSON
* Scaffolded `DataTypeUtils` to do basic checks of Spark types;
Added proper compatibility checking b/w old/new index-tables
* Added test for Z-index tables merging
* Fixed import being shaded by creating internal `hudi.util` package
* Fixed packaging for `TestOptimizeTable`
* Revised `updateMetadataIndex` seq to provide Z-index updating process w/ source table schema
* Make sure existing Z-index table schema is sync'd to source table's one
* Fixed shaded refs
* Fixed tests
* Fixed type conversion of Parquet provided metadata values into Spark expected schemas
* Fixed `composeIndexSchema` utility to propose proper schema
* Added more tests for Z-index:
- Checking that Z-index table is built correctly
- Checking that Z-index tables are merged correctly (during update)
* Fixing source table
* Fixing tests to read from Parquet w/ proper schema
* Refactored `ParquetUtils` utility reading stats from Parquet footers
* Fixed incorrect handling of Decimals extracted from Parquet footers
* Worked around issues in javac failign to compile stream's collection
* Fixed handling of `Date` type
* Fixed handling of `DateType` to be parsed as `LocalDate`
* Updated fixture;
Make sure test loads Z-index fixture using proper schema
* Removed superfluous scheme adjusting when reading from Parquet, since Spark is actually able to perfectly restore schema (given Parquet was previously written by Spark as well)
* Fixing race-condition in Parquet's `DateStringifier` trying to share `SimpleDataFormat` object which is inherently not thread-safe
* Tidying up
* Make sure schema is used upon reading to validate input files are in the appropriate format;
Tidying up;
* Worked around javac (1.8) inability to infer expression type properly
* Updated fixtures;
Tidying up
* Fixing compilation after rebase
* Assert clustering have in Z-order layout optimization testing
* Tidying up exception messages
* XXX
* Added test validating Z-index lookup filter correctness
* Added more test-cases;
Tidying up
* Added tests for string expressions
* Fixed incorrect Z-index filter lookup translations
* Added more test-cases
* Added proper handling on complex negations of AND/OR expressions by pushing NOT operator down into inner expressions for appropriate handling
* Added `-target:jvm-1.8` for `hudi-spark` module
* Adding more tests
* Added tests for non-indexed columns
* Properly handle non-indexed columns by falling back to a re-write of containing expression as `TrueLiteral` instead
* Fixed tests
* Removing the parquet test files and disabling corresponding tests
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
Main functions:
Support create table for hoodie.
Support CTAS.
Support Insert for hoodie. Including dynamic partition and static partition insert.
Support MergeInto for hoodie.
Support DELETE
Support UPDATE
Both support spark2 & spark3 based on DataSourceV1.
Main changes:
Add sql parser for spark2.
Add HoodieAnalysis for sql resolve and logical plan rewrite.
Add commands implementation for CREATE TABLE、INSERT、MERGE INTO & CTAS.
In order to push down the update&insert logical to the HoodieRecordPayload for MergeInto, I make same change to the
HoodieWriteHandler and other related classes.
1、Add the inputSchema for parser the incoming record. This is because the inputSchema for MergeInto is different from writeSchema as there are some transforms in the update& insert expression.
2、Add WRITE_SCHEMA to HoodieWriteConfig to pass the write schema for merge into.
3、Pass properties to HoodieRecordPayload#getInsertValue to pass the insert expression and table schema.
Verify this pull request
Add TestCreateTable for test create hoodie tables and CTAS.
Add TestInsertTable for test insert hoodie tables.
Add TestMergeIntoTable for test merge hoodie tables.
Add TestUpdateTable for test update hoodie tables.
Add TestDeleteTable for test delete hoodie tables.
Add TestSqlStatement for test supported ddl/dml currently.
- 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>
- Adding ability to use native spark row writing for bulk_insert
- Controlled by `ENABLE_ROW_WRITER_OPT_KEY` datasource write option
- Introduced KeyGeneratorInterface in hudi-client, moved KeyGenerator back to hudi-spark
- Simplified the new API additions to just two new methods : getRecordKey(row), getPartitionPath(row)
- Fixed all built-in key generators with new APIs
- Made the field position map lazily created upon the first call to row based apis
- Implemented native row based key generators for CustomKeyGenerator
- Fixed all the tests, with these new APIs
Co-authored-by: Balaji Varadarajan <varadarb@uber.com>
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
- Add spotless format fixing to project
- One time reformatting for conformity
- Build fails for formatting changes and mvn spotless:apply autofixes them
- 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
The code-style rules follow google style with some changes:
1. Increase line length from 100 to 120
2. Disable JavaDoc related checkstyles as this needs more manual work.
Both source and test code are checked for code-style
