[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<inputData, initState>` (matching `TransformWithStateInPySparkPythonInitialStateRunner`), emitting all initial-state batches first then all data batches (the JVM `initData ++ data` ordering), with the inactive side of each batch written as an all-null struct so `TransformWithStateInPandasInitStateSerializer` never sees a mixed batch and regroups rows by the leading key.
### 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 <17627829+Yicong-Huang@users.noreply.github.com>
Signed-off-by: Yicong-Huang <17627829+Yicong-Huang@users.noreply.github.com>
Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R (Deprecated), and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
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Spark is built using Apache Maven. To build Spark and its example programs, run:
./build/mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.)
More detailed documentation is available from the project site, at “Building Spark”.
For general development tips, including info on developing Spark using an IDE, see “Useful Developer Tools”.
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1,000,000,000:
scala> spark.range(1000 * 1000 * 1000).count()
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1,000,000,000:
>>> spark.range(1000 * 1000 * 1000).count()
Spark also comes with several sample programs in the examples directory. To run one of them, use ./bin/run-example <class> [params]. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be spark:// URL, “yarn” to run on YARN, and “local” to run locally with one thread, or “local[N]” to run locally with N threads. You can also use an abbreviated class name if the class is in the examples package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Testing first requires building Spark. Once Spark is built, tests can be run using:
./dev/run-tests
Please see the guidance on how to run tests for a module, or individual tests.
There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at “Specifying the Hadoop Version and Enabling YARN” for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Please review the Contribution to Spark guide for information on how to get started contributing to the project.
