[VL] Add RDDScanExec support to Velox backend#12113
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Implements VeloxRDDScanTransformer to offload RDDScanExec to Velox's native row-to-columnar conversion path. This enables columnar execution for DataFrames backed by LogicalRDD (e.g., from df.checkpoint(), df.localCheckpoint(), or programmatic RDD creation). Key design: - Validates schema via VeloxValidatorApi.validateSchema (recursive) plus Arrow compatibility checks (rejects MapType, interval types) - Handles BatchCarrierRow (from checkpoint) by unwrapping directly - Standard InternalRow path delegates to RowToVeloxColumnarExec.toColumnarBatchIterator - Preserves original partitioning and ordering from the source RDDScanExec - Unsupported schemas fall back gracefully to vanilla Spark Test coverage: 13 tests covering basic types, complex types, aggregation, empty RDD, nulls, fallback scenarios, and BatchCarrierRow from checkpoint. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
The logical plan's output contains unresolved attributes which cause AnalysisException when creating a new DataFrame from LogicalRDD. Use analyzed.output to get fully resolved attributes with proper types and expression IDs. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Implements VeloxRDDScanTransformer to offload RDDScanExec to Velox's native row-to-columnar conversion path. This enables columnar execution for DataFrames backed by LogicalRDD (e.g., from df.checkpoint(), df.localCheckpoint(), or programmatic RDD creation).
Key design:
Test coverage: 13 tests covering basic types, complex types, aggregation, empty RDD, nulls, fallback scenarios, and BatchCarrierRow from checkpoint.
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Related issue: #8629