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new c07d9813515c [SPARK-57704][PYTHON][TEST] Add ASV microbenchmark for
SQL_TRANSFORM_WITH_STATE_PANDAS_INIT_STATE_UDF
c07d9813515c is described below
commit c07d9813515c864b2233a557b5337759cf2d93c7
Author: Yicong Huang <[email protected]>
AuthorDate: Thu Jul 2 22:10:20 2026 +0000
[SPARK-57704][PYTHON][TEST] Add ASV microbenchmark for
SQL_TRANSFORM_WITH_STATE_PANDAS_INIT_STATE_UDF
### What changes were proposed in this pull request?
Add ASV microbenchmarks for the
`SQL_TRANSFORM_WITH_STATE_PANDAS_INIT_STATE_UDF` eval type in
`python/benchmarks/bench_eval_type.py`, with both `time_*` and `peakmem_*`
variants over the same scenario grid as the plain
`SQL_TRANSFORM_WITH_STATE_PANDAS_UDF` benchmark plus a small seeded
initial-state dataset per group. The benchmark reconstructs the worker wire
protocol for `transformWithStateInPandas` with initial state: a single Arrow
stream whose top-level schema is `struct<inputDat [...]
### Why are the changes needed?
This is the last transformWithState Pandas eval type without benchmark
coverage. The eval type is slated for the serializer/eval-type refactor, and a
microbenchmark establishes the baseline needed to prove the refactor introduces
no regression.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests. Test-only addition; no behavior change.
Ran locally with `COLUMNS=120 asv run --python=same --bench
TransformWithStatePandasInitState -a repeat=3`. Results are stable across
repeated runs; one representative run below.
```text
[time] TransformWithStatePandasInitStateUDFTimeBench.time_worker
================ ============== ============ ============
-- udf
---------------- ----------------------------------------
scenario identity_udf sort_udf count_udf
================ ============== ============ ============
few_groups_sm 810±4ms 833±3ms 835±20ms
few_groups_lg 7.48±0.1s 7.70±0.3s 7.28±0.2s
many_groups_sm 7.93±0.3s 7.95±0.1s 8.87±0.05s
many_groups_lg 4.04±0.05s 4.10±0.02s 4.27±0.04s
wide_cols 8.29±0.3s 8.20±0.2s 7.60±0.04s
mixed_cols 3.42±0.05s 3.45±0.02s 3.25±0.03s
nested_struct 7.99±0.2s 7.91±0.02s 5.67±0.03s
================ ============== ============ ============
[peakmem] TransformWithStatePandasInitStateUDFPeakmemBench.peakmem_worker
================ ============== ========== ===========
-- udf
---------------- -------------------------------------
scenario identity_udf sort_udf count_udf
================ ============== ========== ===========
few_groups_sm 116M 115M 106M
few_groups_lg 248M 248M 248M
many_groups_sm 176M 177M 161M
many_groups_lg 151M 151M 151M
wide_cols 364M 367M 342M
mixed_cols 182M 182M 182M
nested_struct 210M 210M 210M
================ ============== ========== ===========
```
### Was this patch authored or co-authored using generative AI tooling?
No.
Closes #56794 from Yicong-Huang/SPARK-57704.
Authored-by: Yicong Huang <[email protected]>
Signed-off-by: Yicong-Huang <[email protected]>
(cherry picked from commit 332025b372e6942613757ac507c3e5af0871e8b4)
Signed-off-by: Yicong-Huang <[email protected]>
---
python/benchmarks/bench_eval_type.py | 165 +++++++++++++++++++++++++++++++++++
1 file changed, 165 insertions(+)
diff --git a/python/benchmarks/bench_eval_type.py
b/python/benchmarks/bench_eval_type.py
index f220b804be0b..cec2ead32be0 100644
--- a/python/benchmarks/bench_eval_type.py
+++ b/python/benchmarks/bench_eval_type.py
@@ -1981,3 +1981,168 @@ class TransformWithStatePandasUDFPeakmemBench(
_TransformWithStatePandasBenchMixin, _PeakmemBenchBase
):
pass
+
+
+# -- SQL_TRANSFORM_WITH_STATE_PANDAS_INIT_STATE_UDF
----------------------------
+# Stateful streaming with Pandas plus an initial-state dataset. The UDF
+# signature is ``(api_client, mode, key, state_values, init_states)`` and
+# returns ``Iterator[pandas.DataFrame]``.
+#
+# Unlike the plain TWS variant, the input wire stream wraps two datasets into a
+# single Arrow stream whose top-level schema is
+# ``struct<inputData: dataSchema, initState: initStateSchema>`` (see
+# ``TransformWithStateInPySparkPythonInitialStateRunner``). Each batch carries
+# either inputData or initState rows -- never both -- with the inactive column
+# written as an all-null struct. Matching the JVM ``initData ++ data``
ordering,
+# all initial-state batches are emitted first (initState populated), then all
+# data batches (inputData populated).
``TransformWithStateInPandasInitStateSerializer``
+# regroups rows by the leading key column, so each key surfaces as an init-only
+# call followed by a data-only call; the empty side of each call is filtered
out
+# before the UDF sees it.
+
+
+class
_TransformWithStatePandasInitStateBenchMixin(_TransformWithStatePandasBenchMixin):
+ """Provides ``_write_scenario`` for
SQL_TRANSFORM_WITH_STATE_PANDAS_INIT_STATE_UDF.
+
+ Reuses the plain-TWS scenario grid for the input data and seeds a small
+ initial-state dataset per group (``_INIT_ROWS_PER_GROUP`` rows sharing the
+ input schema). The initial-state deserialization cost (nested-struct
flatten
+ plus per-key regrouping) is incurred during ``load_stream`` regardless of
+ whether the UDF reads ``init_states``.
+ """
+
+ # Initial state is small relative to the streamed data (one seeded chunk
per
+ # key), so data deserialization stays the dominant cost -- mirroring
+ # production where initial state loads once and input data streams per
batch.
+ _INIT_ROWS_PER_GROUP = 100
+
+ @classmethod
+ def _build_init_batches(cls, name):
+ """Build the initial-state Arrow batches for a scenario.
+
+ Shares the input schema (same value columns) but with only
+ ``_INIT_ROWS_PER_GROUP`` rows per group, pre-sorted by the leading key.
+ """
+ np.random.seed(7)
+ num_groups, _, num_value_cols, value_pool = cls._scenario_configs[name]
+ total_rows = num_groups * cls._INIT_ROWS_PER_GROUP
+ key_array = pa.array(
+ np.repeat(np.arange(num_groups, dtype=np.int32),
cls._INIT_ROWS_PER_GROUP),
+ type=pa.int32(),
+ )
+ value_arrays = [
+ value_pool[i % len(value_pool)][0](total_rows) for i in
range(num_value_cols)
+ ]
+ names = ["col_0"] + [f"col_{i + 1}" for i in range(num_value_cols)]
+ full_batch = pa.RecordBatch.from_arrays([key_array] + value_arrays,
names=names)
+ batch_size = MockDataFactory.MAX_RECORDS_PER_BATCH
+ return [
+ full_batch.slice(offset, min(batch_size, total_rows - offset))
+ for offset in range(0, total_rows, batch_size)
+ ]
+
+ @staticmethod
+ def _wrap_nested(flat_batch, struct_type, *, is_init):
+ """Wrap a flat batch into a ``struct<inputData, initState>`` batch.
+
+ The populated side carries ``flat_batch``'s columns; the inactive side
is
+ an all-null struct array of the same length, so ``flatten_columns`` in
the
+ serializer treats it as empty.
+ """
+ n = flat_batch.num_rows
+ populated = pa.StructArray.from_arrays(
+ [flat_batch.column(i) for i in range(flat_batch.num_columns)],
+ names=flat_batch.schema.names,
+ )
+ null_struct = pa.array([None] * n, type=struct_type)
+ arrays = [null_struct, populated] if is_init else [populated,
null_struct]
+ return pa.RecordBatch.from_arrays(arrays, names=["inputData",
"initState"])
+
+ def _tws_init_identity(api_client, mode, key, state_values, init_states):
+ from pyspark.sql.streaming.stateful_processor_util import (
+ TransformWithStateInPandasFuncMode,
+ )
+
+ if mode == TransformWithStateInPandasFuncMode.PROCESS_DATA:
+ yield from state_values
+
+ def _tws_init_sort(api_client, mode, key, state_values, init_states):
+ from pyspark.sql.streaming.stateful_processor_util import (
+ TransformWithStateInPandasFuncMode,
+ )
+
+ if mode == TransformWithStateInPandasFuncMode.PROCESS_DATA:
+ for pdf in state_values:
+ yield pdf.sort_values(pdf.columns[0])
+
+ def _tws_init_count(api_client, mode, key, state_values, init_states):
+ import pandas as pd
+ from pyspark.sql.streaming.stateful_processor_util import (
+ TransformWithStateInPandasFuncMode,
+ )
+
+ if mode == TransformWithStateInPandasFuncMode.PROCESS_DATA:
+ # state_values and init_states arrive on separate per-key calls;
sum
+ # whichever is non-empty so both deserialization paths are counted.
+ total = sum(len(pdf) for pdf in state_values) + sum(len(pdf) for
pdf in init_states)
+ if total:
+ yield pd.DataFrame({"col_0": [key[0]], "col_1": [total]})
+
+ # ret_type=None means "echo the full input schema": the init-state worker
+ # path does not project value columns, so identity/sort receive and return
+ # the key column too. count_udf re-emits (key, total) explicitly.
+ _udfs = {
+ "identity_udf": (_tws_init_identity, None),
+ "sort_udf": (_tws_init_sort, None),
+ "count_udf": (
+ _tws_init_count,
+ StructType([StructField("col_0", IntegerType()),
StructField("col_1", IntegerType())]),
+ ),
+ }
+ params = [list(_TransformWithStatePandasBenchMixin._scenario_configs),
list(_udfs)]
+ param_names = ["scenario", "udf"]
+
+ def _write_scenario(self, scenario, udf_name, buf):
+ data_batches, schema = self._build_scenario(scenario)
+ init_batches = self._build_init_batches(scenario)
+ udf_func, ret_type = self._udfs[udf_name]
+ if ret_type is None:
+ ret_type = schema
+ n_value_cols = len(schema.fields) - self._NUM_KEY_COLS
+ # Two arg-offset groups -- one for input data, one for initial state.
+ # Both datasets share the schema, so each resolves to key=[0],
values=[1..n].
+ arg_offsets = MockUDFFactory.make_grouped_arg_offsets(
+ self._NUM_KEY_COLS, n_value_cols
+ ) + MockUDFFactory.make_grouped_arg_offsets(self._NUM_KEY_COLS,
n_value_cols)
+ grouping_key_schema = StructType(schema.fields[: self._NUM_KEY_COLS])
+ # Wrap both datasets into the struct<inputData, initState> wire schema;
+ # the two structs share a type since the datasets share a schema.
+ struct_type = pa.StructArray.from_arrays(
+ [data_batches[0].column(i) for i in
range(data_batches[0].num_columns)],
+ names=data_batches[0].schema.names,
+ ).type
+ nested_batches = [self._wrap_nested(b, struct_type, is_init=True) for
b in init_batches] + [
+ self._wrap_nested(b, struct_type, is_init=False) for b in
data_batches
+ ]
+ MockProtocolWriter.write_worker_input(
+ PythonEvalType.SQL_TRANSFORM_WITH_STATE_PANDAS_INIT_STATE_UDF,
+ lambda b: MockProtocolWriter.write_udf_payload(udf_func, ret_type,
arg_offsets, b),
+ lambda b:
MockProtocolWriter.write_data_payload(iter(nested_batches), b),
+ buf,
+ eval_conf={
+ "state_server_socket_port": str(_StubStateServer.get_port()),
+ "grouping_key_schema": grouping_key_schema.json(),
+ },
+ )
+
+
+class TransformWithStatePandasInitStateUDFTimeBench(
+ _TransformWithStatePandasInitStateBenchMixin, _TimeBenchBase
+):
+ pass
+
+
+class TransformWithStatePandasInitStateUDFPeakmemBench(
+ _TransformWithStatePandasInitStateBenchMixin, _PeakmemBenchBase
+):
+ pass
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