We provide an alternative way of fetching Column Stats Index within the reading process to avoid the penalty of a more heavy-weight execution scheduled through a Spark engine.
This set of changes makes sure that all builtin KeyGenerators properly implement Spark-specific APIs in a performant way (minimizing key-generators overhead)
Fixes the missing bloom filters in metadata table in the non-partitioned table due to incorrect record key generation, because of wrong file names when generating the metadata payload for the bloom filter.
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-4276] Reconcile schema-inject null values for missing fields and add new fields.
* fix comments
Co-authored-by: public (bdcee5037027) <mengtao0326@qq.com>
Fixed sequence determining whether Parquet's legacy-format writing property should be overridden to only kick in when it has not been explicitly specified by the caller
* Fixed Dictionary encoding config not being properly propagated to Parquet writer (making it unable to apply it, substantially bloating the storage footprint)
TestReaderFilterRowKeys needs to get the key from RECORD_KEY_METADATA_FIELD, but the writer in current UT does not populate the meta field and the schema does not contains meta fields.
This fix writes data with schema which contains meta fields and calls writeAvroWithMetadata for writing.
Co-authored-by: xicm <xicm@asiainfo.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.
