geserdugarov commented on code in PR #18276:
URL: https://github.com/apache/hudi/pull/18276#discussion_r3042950213
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rfc/rfc-98/rfc-98.md:
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@@ -52,25 +49,294 @@ The current implementation of Spark Datasource V2
integration is presented in th
## Implementation
-<!-- -->
+Hudi's write path is mature, and involves indexing, precombining,
upsert/insert routing, file sizing, and table services
(compaction/clustering/cleaning).
+Also `HoodieSparkSqlWriter::write` handles schema evolution, partition
encoding, metadata updates, and multi-writer concurrency.
+DSv2's `WriteBuilder` >> `BatchWrite` >> DataWriter API is too simplistic for
this, and moving to this entirely would be a non-starter. Also, due to the
flexibility of the V1 API in terms of allowing the writes to shuffle data after
the `df.write.format....save` is invoked, Hudi supports a streaming DF write
for its upsert operation. A good majority of Hudi jobs work this way today, and
we cannot break all of these at once.
+
+The proposed approach is hybrid: DSv2 for reads, with a DSv1 fallback for
writes (`V2TableWithV1Fallback`) in the current state.
+Later, if a DSv2 write path can be implemented without loss of performance or
functionality, it may become possible to move to full DSv2 support.
+However, this migration should still be incremental, please check the "Future
Work" chapter for details.
+
+Overall proposed architecture for the hybrid approach is shown in the
following schema:
+
+
+
+### DataFrame API
+
+A new SPI short name, `"hudi_v2"`, activates the DSv2 read path when using the
Spark DataFrame API.
+The existing `"hudi"` path remains unchanged.
+This is done to unblock incremental development of the DSv2 path and will be
removed in the long term, please check the "Future Work" chapter for details.
+It also allows switching later from the current DSv1 fallback to a DSv2 write
path, if an implementation without performance degradation is found.
+The DSv2 write path is currently under research.
+
+<table>
+<tr>
+<th>Operation</th>
+<th>Current implementation</th>
+<th>Additional functionality proposed in this RFC</th>
+</tr>
+<tr>
+<td>Write</td>
+<td>
+<pre>
+df.write.format("hudi").mode(...).save(path)
+ v
+BaseDefaultSource (V1) -> DefaultSource
+ v
+CreatableRelationProvider.createRelation(...)
+ v
+HoodieSparkSqlWriter.write(...)
+ v
+SparkRDDWriteClient -> upsert/insert/bulk_insert
+</pre>
+</td>
+<td>
+<pre>
+df.write.format("hudi_v2").mode(...).save(path)
+ v
+HoodieDataSourceV2 (TableProvider + DataSourceRegister +
CreatableRelationProvider)
+ v
+Spark treats as V1 source for writes
+ v
+CreatableRelationProvider.createRelation(...)
+ v
+HoodieSparkSqlWriter.write(...)
+ v
+SparkRDDWriteClient -> upsert/insert/bulk_insert
+</pre>
+</td>
+</tr>
+<tr>
+<td>Read</td>
+<td>
+<pre>
+spark.read.format("hudi").load(path)
+ v
+V1 DataSource resolution (via ServiceLoader + DataSourceRegister)
+ v
+BaseDefaultSource found
+(extends DefaultSource with DataSourceRegister)
+(not a TableProvider)
+ v
+Spark treats as V1 DataSource
+ v
+DefaultSource.createRelation(...)
+ v
+MergeOnReadSnapshotRelation / BaseRelation
+ v
+LogicalRelation -> FileScan -> ...
+</pre>
+</td>
+<td>
+<pre>
+spark.read.format("hudi_v2").load(path)
+ v
+DataSourceV2Utils.lookupProvider("hudi_v2")
+ v
+HoodieDataSourceV2 found
+(extends TableProvider with DataSourceRegister)
+(does not extend SupportsCatalogOptions)
+ v
+Spark uses TableProvider.getTable() directly
+(no catalog routing since no SupportsCatalogOptions)
+ v
+HoodieDataSourceV2.getTable(...)
+ v
+HoodieSparkV2Table(...)
+(no catalogTable, no tableIdentifier)
+ v
+HoodieScanBuilder -> HoodieBatchScan -> ...
+</pre>
+</td>
+</tr>
+</table>
+
+### SQL Queries
+
+Spark SQL API is managed by new configuration parameter
`hoodie.datasource.read.use.v2`, which controls the returned table type.
+
+<table>
+<tr>
+<th>Operation</th>
+<th>Current implementation</th>
+<th>Additional functionality proposed in this RFC</th>
+</tr>
+<tr>
+<td>Write</td>
+<td>
+<pre>
+INSERT INTO hudi_table VALUES (...); -- table created with USING hudi
+ v
+Spark Analyzer resolves table via catalog
+ v
+HoodieCatalog.loadTable(Identifier("hudi_table"))
+ v
+isHoodieTable => true, v2ReadEnabled = false, schemaEvol = false
+ v
+RETURNS: V1Table(catalogTable) via v1TableWrapper
+ v
+Spark V1 write path -> InsertIntoHoodieTableCommand (analysis rule)
+ v
+HoodieSparkSqlWriter.write(...)
+</pre>
+</td>
+<td>
+<pre>
+INSERT INTO hudi_table VALUES (...); -- table created with USING hudi
+ v
+Spark Analyzer resolves table via catalog
+ v
+HoodieCatalog.loadTable(Identifier("hudi_table"))
+ v
+isHoodieTable => true, v2ReadEnabled = true
+ v
+RETURNS: HoodieSparkV2Table(...)
+ v
+SupportsWrite.newWriteBuilder() -> HoodieV1WriteBuilder
+ v
+V1Write -> InsertableRelation.insert(data, overwrite)
+ v
+Align columns (rename + cast to table's user schema)
+ v
+HoodieSparkSqlWriter.write(...)
+</pre>
+</td>
+</tr>
+<tr>
+<td>Read</td>
+<td>
+<pre>
+SELECT * FROM hudi_table; -- table created with USING hudi
+ v
+Spark Analyzer resolves table name via catalog
+ v
+HoodieCatalog.loadTable(Identifier("hudi_table"))
+ v
+super.loadTable(ident)
+ v
+V1Table(catalogTable) where catalogTable.provider = "hudi"
+ v
+isHoodieTable(catalogTable) => true
+ v
+v2ReadEnabled = false, schemaEvolutionEnabled = false (defaults)
+ v
+RETURNS: HoodieInternalV2Table(...).v1TableWrapper = V1Table(catalogTable)
+ v
+Spark uses V1 fallback -> DefaultSource.createRelation()
+ v
+HoodieFileIndex -> FileScan -> ...
+</pre>
+</td>
+<td>
+<pre>
+SELECT * FROM hudi_table; -- table created with USING hudi
+ v
+Spark Analyzer resolves table name via catalog
+ v
+HoodieCatalog.loadTable(Identifier("hudi_table"))
+ v
+super.loadTable(ident)
+ v
+V1Table(catalogTable) where catalogTable.provider = "hudi"
+ v
+isHoodieTable(catalogTable) => true
+ v
+v2ReadEnabled = conf("hoodie.datasource.read.use.v2") = true
+ v
+RETURNS: HoodieSparkV2Table(...)
+ v
+SupportsRead.newScanBuilder() -> HoodieScanBuilder
+ v
+HoodieBatchScan -> ...
+</pre>
+</td>
+</tr>
+</table>
### Read
-<!-- main part -->
+All new classes go into package `org.apache.spark.sql.hudi.v2` inside
`hudi-spark-common`.
+
+| Class | Spark Interface
| Responsibility
|
+|---------------------------------|-----------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `HoodieDataSourceV2` | `TableProvider`, `DataSourceRegister`,
`CreatableRelationProvider`
| SPI entry point for `format("hudi_v2")`.
`CreatableRelationProvider` enables DataFrame API writes via
`df.write.format("hudi_v2")`.
|
+| `HoodieSparkV2Table` | `Table`, `SupportsRead`, `SupportsWrite`,
`V2TableWithV1Fallback`
| Routes reads to DSv2, writes to DSv1 fallback via
`HoodieV1WriteBuilder`.
|
+| `HoodieScanBuilder` | `ScanBuilder`, `SupportsPushDownFilters`,
`SupportsPushDownRequiredColumns`, `PartialLimitPushDown`,
`SupportsPushDownAggregates` | Collects filter, column pruning, limit, and
aggregate pushdowns.
|
Review Comment:
Here `PartialLimitPushDown` description is missed. It's an intentional
custom Hudi interface, which extends `SupportsPushDownLimit`. The problem is
that Spark 3.4+ added `SupportsPushDownLimit::isPartiallyPushed` as a default
method (returning false). Spark 3.3 does not have this method. So to handle
building of Hudi for all those Spark versions we could add Java interface
`PartialLimitPushDown` to avoid the Scala `override` keyword issue. Java
default methods don't require `@Override`, so this will compile against both
Spark 3.3 (new method) and 3.4+ (override).
Added `PartialLimitPushDown` in 981303492e62a13a19ae98f0a8538abde267864a
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