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.
- Provided option to trigger clean every nth commit with default number of commits as 1 so that existing users are not affected.
Co-authored-by: sivabalan <n.siva.b@gmail.com>
Actually method FlinkWriteHelper#deduplicateRecords does not guarantee the records sequence, but there is a
implicit constraint: all the records in one bucket should have the same bucket type(instant time here),
the BucketStreamWriteFunction breaks the rule and fails to comply with this constraint.
closeapache/hudi#5018
* Fixed metadata conversion util to extract schema from `HoodieCommitMetadata`
* Fixed failure to fetch columns to index in empty table
* Abort indexing seq in case there are no columns to index
* Fallback to index at least primary key columns, in case no writer schema could be obtained to index all columns
* Fixed `getRecordFields` incorrectly ignoring default value
* Make sure Hudi metadata fields are also indexed
Refactoring Spark DataSource Relations to avoid code duplication.
Following Relations were in scope:
- BaseFileOnlyViewRelation
- MergeOnReadSnapshotRelaation
- MergeOnReadIncrementalRelation
- Adopt HoodieData in Spark action commit executors
- Make Spark independent DeleteHelper, WriteHelper, MergeHelper in hudi-client-common
- Make HoodieTable in WriteClient APIs have raw type to decouple with Client's generic types
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
Desc: Add a hive sync config(hoodie.datasource.hive_sync.sync_comment). This config defaults to false.
While syncing data source to hudi, add column comments to source avro schema, and the sync_comment is true, syncing column comments to the hive table.
Rework of #4761
This diff introduces following changes:
- Write stats are converted to metadata index records during the commit. Making them use the HoodieData type so that the record generation scales up with needs.
- Metadata index init support for bloom filter and column stats partitions.
- When building the BloomFilter from the index records, using the type param stored in the payload instead of hardcoded type.
- Delta writes can change column ranges and the column stats index need to be properly updated with new ranges to be consistent with the table dataset. This fix add column stats index update support for the delta writes.
Co-authored-by: Manoj Govindassamy <manoj.govindassamy@gmail.com>
- This change makes sure MT records are updated appropriately on HDFS: previously after Log File append operations MT records were updated w/ just the size of the deltas being appended to the original files, which have been found to be the cause of issues in case of Rollbacks that were instead updating MT with records bearing the full file-size.
- To make sure that we hedge against similar issues going f/w, this PR alleviates this discrepancy and streamlines the flow of MT table always ingesting records bearing full file-sizes.
* [HUDI-3445] Clustering Command Based on Call Procedure Command for Spark SQL
* [HUDI-3445] Clustering Command Based on Call Procedure Command for Spark SQL
* [HUDI-3445] Clustering Command Based on Call Procedure Command for Spark SQL
Co-authored-by: shibei <huberylee.li@alibaba-inc.com>
