ericm-db opened a new pull request, #54098:
URL: https://github.com/apache/spark/pull/54098
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### What changes were proposed in this pull request?
This PR introduces the SequentialUnion logical plan node, which enables
sequential processing of multiple streaming sources. Unlike the existing Union
operator that processes all children concurrently, SequentialUnion processes
each child source to completion before moving to the next.
Key changes:
- Added SequentialUnion case class extending UnionBase in
basicLogicalOperators.scala
- Supports byName and allowMissingCol parameters for schema compatibility
- Includes flatten() method to handle nested SequentialUnion nodes (enabling
chaining)
- Validates minimum 2 children and parameter constraints
- Added comprehensive test suite in SequentialUnionSuite.scala
This is the foundational logical plan component. Execution support and
user-facing APIs will be added in follow-up PRs.
### Why are the changes needed?
Sequential Union enables backfill-to-live streaming scenarios where
historical data must be processed completely before transitioning to live data,
while preserving stateful operator state across the transition.
Example use case: Process historical Parquet data completely, then
seamlessly switch to live Kafka streaming - all within a single query that
maintains aggregations, watermarks, and deduplication state.
This pattern is not currently possible with the existing Union operator,
which processes all sources concurrently.
### Does this PR introduce _any_ user-facing change?
No. This PR only adds the internal logical plan node. No user-facing APIs or
behavior changes are introduced yet.
### How was this patch tested?
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Added new test suite SequentialUnionSuite with tests covering:
- Basic node creation and validation
- Minimum children requirement (at least 2)
- Output schema verification
- byName and allowMissingCol parameter constraints
- Flattening logic for nested SequentialUnions (single level, deeply nested,
multiple nested scenarios)
### Was this patch authored or co-authored using generative AI tooling?
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No
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