Currently, all Hudi Relations bear performance gap relative to Spark's HadoopFsRelation
and the reason to that is SchemaPruning optimization rule (pruning nested schemas)
that is unfortunately predicated on usage of HadoopFsRelation, meaning that it's
not applied in cases when any other relation is used.
This change is porting this rule to Hudi relations (MOR, Incremental, etc)
by the virtue of leveraging HoodieSparkSessionExtensions mechanism
injecting modified version of the original SchemaPruning rule
that is adopted to work w/ Hudi's custom relations.
- Added customOptimizerRules to HoodieAnalysis
- Added NestedSchemaPrunning Spark's Optimizer rule
- Handle Spark's Optimizer pruned data schema (to effectively prune nested schemas)
- Enable HoodieClientTestHarness to inject HoodieSparkSessionExtensions
- Injecting Spark Session extensions for TestMORDataSource, TestCOWDataSource
- Disabled fallback to HadoopFsRelation
- Move clean related configuration to HoodieCleanConfig
- Move Archival related configuration to HoodieArchivalConfig
- hoodie.compaction.payload.class move this to HoodiePayloadConfig
* [HUDI-3730] Improve meta sync class design and hierarchies (#5754)
* Implements class design proposed in RFC-55
Co-authored-by: jian.feng <fengjian428@gmial.com>
Co-authored-by: jian.feng <jian.feng@shopee.com>
There are multiple issues with our current DataSource V2 integrations: b/c we advertise Hudi tables as V2, Spark expects it to implement certain APIs which are not implemented at the moment, instead we're using custom Resolution rule (in HoodieSpark3Analysis) to instead manually fallback to V1 APIs. This commit fixes the issue by reverting DSv2 APIs and making Spark use V1, except for schema evaluation logic.
Make truncate partition and drop partition behave as drop partition with purge, which delete all records via Hudi DELETE_PARTITION; partition removed from metastore
* [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
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.
* Update CompactionHoodiePathCommand.scala
fix NPE when run schdule using spark-sql if the commits time < hoodie.compact.inline.max.delta.commits
* Update CompactionHoodiePathCommand.scala
fix IndexOutOfBoundsException when there`s no schedule for compaction
* Update CompactionHoodiePathCommand.scala
fix CI issue
* [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>
* Bootstrapping initial support for Metadata Table in Spark Datasource
- Consolidated Avro/Row conversion utilities to center around Spark's AvroDeserializer ; removed duplication
- Bootstrapped HoodieBaseRelation
- Updated HoodieMergeOnReadRDD to be able to handle Metadata Table
- Modified MOR relations to be able to read different Base File formats (Parquet, HFile)
