biruktesf-db opened a new pull request, #56343:
URL: https://github.com/apache/spark/pull/56343
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### What changes were proposed in this pull request?
When consuming query results on the spark connect client, count each
RecordBatch once (num_records += batch.num_rows) and validate row_count only
after the IPC stream is fully consumed. The Scala client in SparkResult.scala
already validates after the loop and is unaffected.
### Why are the changes needed?
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1. If you propose a new API, clarify the use case for a new API.
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The Arrow IPC streaming format wraps a result as
`[Schema][RecordBatch]*[EOS]` a single message can carry multiple
RecordBatches, and `pa.ipc.open_stream(...)` parses all of them. The server's
arrow_batch.row_count is the total rows across every RecordBatch in the message
and the spark connect client validates the row count inside the per-batch loop:
```
for batch in reader:
num_records_in_batch += batch.num_rows
if num_records_in_batch != b.arrow_batch.row_count: # checked too
early
raise SparkConnectException(...)
num_records += num_records_in_batch # also
double-counts
```
When a message contains more than one RecordBatch, the check fires after the
first batch, before the stream is fully consumed, and throws:
`SparkConnectException: Expected N rows in arrow batch but got M. (M < N)`
Impact: Any code path that produces multi-RecordBatch IPC streams (e.g.
Arrow-native IPC buffer compression) fails to fetch results, even though the
payload is well-formed and parseable by PyArrow.
### Does this PR introduce _any_ user-facing change?
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Note that it means *any* user-facing change including all aspects such as
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No
### How was this patch tested?
Added unit tests for the client,
### Was this patch authored or co-authored using generative AI tooling?
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