- Upgrade junit to 5.7.2
- Downgrade surefire and failsafe to 2.22.2
- Fix test failures that were previously not reported
- Improve azure pipeline configs
Co-authored-by: liujinhui1994 <965147871@qq.com>
Co-authored-by: Y Ethan Guo <ethan.guoyihua@gmail.com>
- Added pure immutable test yamls to integ test framework. Added SparkBulkInsertNode as part of it.
- Added delete_partition support to integ test framework using spark-datasource.
- Added a single yaml to test all non core write operations (insert overwrite, insert overwrite table and delete partitions)
- Added tests for 4 concurrent spark datasource writers (multi-writer tests).
- Fixed readme w/ sample commands for multi-writer.
Hudi will be taking on promise for it bundles to stay compatible with Spark minor versions (for ex 2.4, 3.1, 3.2): meaning that single build of Hudi (for ex "hudi-spark3.2-bundle") will be compatible with ALL patch versions in that minor branch (in that case 3.2.1, 3.2.0, etc)
To achieve that we'll have to remove (and ban) "spark-avro" as a dependency, which on a few occasions was the root-cause of incompatibility b/w consecutive Spark patch versions (most recently 3.2.1 and 3.2.0, due to this PR).
Instead of bundling "spark-avro" as dependency, we will be copying over some of the classes Hudi depends on and maintain them along the Hudi code-base to make sure we're able to provide for the aforementioned guarantee. To workaround arising compatibility issues we will be applying local patches to guarantee compatibility of Hudi bundles w/in the Spark minor version branches.
Following Hudi modules to Spark minor branches is currently maintained:
"hudi-spark3" -> 3.2.x
"hudi-spark3.1.x" -> 3.1.x
"hudi-spark2" -> 2.4.x
Following classes hierarchies (borrowed from "spark-avro") are maintained w/in these Spark-specific modules to guarantee compatibility with respective minor version branches:
AvroSerializer
AvroDeserializer
AvroUtils
Each of these classes has been correspondingly copied from Spark 3.2.1 (for 3.2.x branch), 3.1.2 (for 3.1.x branch), 2.4.4 (for 2.4.x branch) into their respective modules.
SchemaConverters class in turn is shared across all those modules given its relative stability (there're only cosmetical changes from 2.4.4 to 3.2.1).
All of the aforementioned classes have their corresponding scope of visibility limited to corresponding packages (org.apache.spark.sql.avro, org.apache.spark.sql) to make sure broader code-base does not become dependent on them and instead relies on facades abstracting them.
Additionally, given that Hudi plans on supporting all the patch versions of Spark w/in aforementioned minor versions branches of Spark, additional build steps were added to validate that Hudi could be properly compiled against those versions. Testing, however, is performed against the most recent patch versions of Spark with the help of Azure CI.
Brief change log:
- Removing spark-avro bundling from Hudi by default
- Scaffolded Spark 3.2.x hierarchy
- Bootstrapped Spark 3.1.x Avro serializer/deserializer hierarchy
- Bootstrapped Spark 2.4.x Avro serializer/deserializer hierarchy
- Moved ExpressionCodeGen,ExpressionPayload into hudi-spark module
- Fixed AvroDeserializer to stay compatible w/ both Spark 3.2.1 and 3.2.0
- Modified bot.yml to build full matrix of support Spark versions
- Removed "spark-avro" dependency from all modules
- Fixed relocation of spark-avro classes in bundles to assist in running integ-tests.
* Rebased `DFSPropertiesConfiguration` to access Hadoop config in liue of FS to avoid confusion
* Fixed `readConfig` to take Hadoop's `Configuration` instead of FS;
Fixing usages
* Added test for local FS access
* Rebase to use `FSUtils.getFs`
* Combine properties provided as a file along w/ overrides provided from the CLI
* Added helper utilities to `HoodieClusteringConfig`;
Make sure corresponding config methods fallback to defaults;
* Fixed DeltaStreamer usage to respect properly combined configuration;
Abstracted `HoodieClusteringConfig.from` convenience utility to init Clustering config from `Properties`
* Tidying up
* `lint`
* Reverting changes to `HoodieWriteConfig`
* Tdiying up
* Fixed incorrect merge of the props
* Converted `HoodieConfig` to wrap around `Properties` into `TypedProperties`
* Fixed compilation
* Fixed compilation
* [HUDI-1870] Add more Spark CI build tasks
- build for spark3.0.x
- build for spark-shade-unbundle-avro
- fix build failures
- delete unnecessary assertion for spark 3.0.x
- use AvroConversionUtils#convertAvroSchemaToStructType instead of calling SchemaConverters#toSqlType directly to solve the compilation failures with spark-shade-unbundle-avro (#5)
Co-authored-by: Yann <biyan900116@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>
* Adding support to ingest records with old schema after table's schema is evolved
* Rebasing against latest master
- Trimming test file to be < 800 lines
- Renaming config names
* Addressing feedback
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
