carloea2 opened a new issue, #8476:
URL: https://github.com/apache/texera/issues/8476
### Feature Summary
Allow Python UDFs to explicitly receive and return Arrow batches, avoiding
the intermediate Python Tuple conversion while keeping existing UDF APIs
unchanged.
PyAmber currently expands incoming Arrow tables into Tuples and converts UDF
table output back through Tuples before rebuilding Arrow. This adds substantial
overhead for operators that already work on entire batches.
An exploratory local conversion benchmark on upstream commit ec3a9dd3ca,
using PyArrow 23.0.1 and 10,000 rows with 10 string columns of 64-character
values, measured:
| Conversion path | Time |
| --- | --- |
| Current Arrow, Tuple, pandas, Tuple, Arrow path | 1,566 ms |
| Direct Arrow, pandas, Arrow path | 20.4 ms |
This is approximately 77 times faster for the measured conversion path. It
indicates potential savings from avoiding row conversion, not a measured 77
times improvement in workflow execution. The proposed engine path has not been
implemented. The full Arrow Flight benchmark was blocked locally by a JOOQ
schema mismatch, so an end-to-end benchmark is still needed.
### Proposed Solution or Design
Introduce an explicit opt-in API, for example:
```python
import pyarrow as pa
import pyarrow.compute as pc
from pyamber import ArrowBatchOperator # Proposed API
class FilterPrices(ArrowBatchOperator):
BATCH_SIZE = 4096
def process_batch(self, batch: pa.Table, port: int):
yield batch.filter(pc.greater(batch["price"], 100))
```
The engine would deliver Arrow batches directly and accept Arrow output
without expanding every row into a Tuple. Syntax alone would not remove the
current input and output conversions.
- Keep existing TupleOperatorV2, BatchOperator, and TableOperator behavior
unchanged.
- Respect configured batch sizes, final partial batches, port boundaries,
and row order.
- Preserve schema validation, Texera partition hashing, storage writes, and
backpressure.
- Define inspection and retry around a batch invocation for the new API,
with control handling between invocations and output yields. Do not
automatically rerun UDFs to fall back, since they may have side effects.
- Specify null, timestamp, binary, and error behavior explicitly, including
what happens to valid output preceding a validation failure.
Start with ArrowBatchOperator. A separate ArrowTableOperator could later
support whole-port input at completion. Validate the proposal with reproducible
conversion and full engine benchmarks, plus tests for control handling and
output equivalence where the APIs share semantics.
### Affected Area
Workflow Engine (Amber)
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