- 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.
- Avoid using udf for key generator for SimpleKeyGen and NonPartitionedKeyGen.
- Fixed NonPartitioned Key generator to directly fetch record key from row rather than involving GenericRecord.
- Other minor fixes around using static values instead of looking up hashmap.
This PR fixes the projection logic around a nested field which is used as the pre-combined key field. The fix is to only check and append the root level field for projection, i.e., "a", for a nested field "a.b.c" in the mandatory columns.
- Changes the logic to check and append the root level field for a required nested field in the mandatory columns in HoodieBaseRelation.appendMandatoryColumns
- 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
Make truncate partition and drop partition behave as drop partition with purge, which delete all records via Hudi DELETE_PARTITION; partition removed from metastore
Currently, Data Skipping is not handling correctly the case when column-stats are not aligned and, for ex, some of the (column, file) combinations are missing from the CSI.
This could occur in different scenarios (schema evolution, CSI config changes), and has to be handled properly when we're composing CSI projection for Data Skipping. This PR addresses that.
- Added appropriate aligning for the transposed CSI projection
Addressing the problem of Data Skipping not respecting Metadata Table configs which might differ b/w write/read paths. More details could be found in HUDI-3812.
- Fixing Data Skipping configuration to respect MT configs (on the Read path)
- Tightening up DS handling of cases when no top-level columns are in the target query
- Enhancing tests to cover all possible case
- Adding capability to fetch Metadata Records by key prefix so that Data Skipping could fetch only Column Stats
- Index records pertaining to the columns being queried by, instead of reading out whole Index.
- Fixed usages of HFileScanner in HFileReader. few code paths uses cached scanner if available. Other code paths uses its own HFileScanner w/ positional read.
Brief change log
- Rebasing ColumnStatsIndexSupport to rely on HoodieBackedTableMetadata in lieu of reading t/h Spark DS
- Adding methods enabling key-prefix lookups to HoodiFileReader, HoodieHFileReader
- Wiring key-prefix lookup t/h LogRecordScanner impls
- Cleaning up HoodieHFileReader impl
Co-authored-by: sivabalan <n.siva.b@gmail.com>
Co-authored-by: Sagar Sumit <sagarsumit09@gmail.com>
* [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>
* [HUDI-2560] introduce id_based schema to support full schema evolution.
* add test for FileBasedInternalSchemaStorageManger and rebase code
* add support for change column type and fix some test case
* fix some bugs encountered in the production env and delete useless code
* fix test error
* rebase code
* fixed some nested schema change bugs
* [HUDI-2429][Stacked On HUDI-2560]Support full schema evolution for spark
* [use dummyInternalSchema instead of null]
* add support for spark3.1.x
* remove support for spark3.1.x , sicne some compile fail
* support spark3.1.x
* rebase and prepare solve all comments
* address all comments
* rebase code
* fixed the count(*) bug
* try to get internalSchema by parser commit file/history file directly, not use metaclient which is time cost
address some comments
* fixed all comments
* fix new comments
* rebase code,fix UT failed
* fixed mistake
* rebase code ,fixed new comments
* rebase code , and prepare for address new comments
* address commits
* address new comments
* fix new issues
* control fallback original write logical
In order to drop any metadata partition (index), we can reuse the DELETE_PARTITION operation in metadata table. Subsequent to this, we can support drop index (with table config update) for async metadata indexer.
- Add a new API in HoodieTableMetadataWriter
- Current only supported for Spark metadata writer
As of now, delete partitions will ensure all file groups are deleted, but the partition as such is not deleted. So, get all partitions might be returning the deleted partitions as well. but no data will be served since all file groups are deleted. With this patch, we are fixing it. We are letting cleaner take care of deleting the partitions when all file groups pertaining to a partitions are deleted.
- Fixed the CleanPlanActionExecutor to return meta info about list of partitions to be deleted. If there are no valid file groups for a partition, clean planner will include the partition to be deleted.
- Fixed HoodieCleanPlan avro schema to include the list of partitions to be deleted
- CleanActionExecutor is fixed to delete partitions if any (as per clean plan)
- Same info is added to HoodieCleanMetadata
- Metadata table when applying clean metadata, will check for partitions to be deleted and will update the "all_partitions" record for the deleted partitions.
Co-authored-by: sivabalan <n.siva.b@gmail.com>
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.
* Added `DataSkippingFailureMode` to control how DS handles failures in the flow (either "strict", when exception would be thrown, or "fallback" when it will just fallback to the full-scan)
* Make sure tests execute in `DataSkippingFailureMode.Strict`
* Fixed Column Stats Index record merging sequence missing `columnName`
Refactoring Spark DataSource Relations to avoid code duplication.
Following Relations were in scope:
- BaseFileOnlyViewRelation
- MergeOnReadSnapshotRelaation
- MergeOnReadIncrementalRelation
NOTE: This change is first part of the series to clean up Hudi's Spark DataSource related implementations, making sure there's minimal code duplication among them, implementations are consistent and performant
This PR is making sure that BaseFileOnlyViewRelation only reads projected columns as well as avoiding unnecessary serde from Row to InternalRow
Brief change log
- Introduced HoodieBaseRDD as a base for all custom RDD impls
- Extracted common fields/methods to HoodieBaseRelation
- Cleaned up and streamlined HoodieBaseFileViewOnlyRelation
- Fixed all of the Relations to avoid superfluous Row <> InternalRow conversions
