You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Yicong Huang (Jira)" <ji...@apache.org> on 2026/09/25 01:30:00 UTC
[jira] [Assigned] (SPARK-59755) Add ASV benchmarks for Arrow incremental aggregate and window UDF eval types
[ https://issues.apache.org/jira/browse/SPARK-59755?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Yicong Huang reassigned SPARK-59755:
------------------------------------
Assignee: Yicong Huang
> Add ASV benchmarks for Arrow incremental aggregate and window UDF eval types
> ----------------------------------------------------------------------------
>
> Key: SPARK-59755
> URL: https://issues.apache.org/jira/browse/SPARK-59755
> Project: Spark
> Issue Type: Sub-task
> Components: PySpark
> Affects Versions: 5.0.0
> Reporter: Yicong Huang
> Assignee: Yicong Huang
> Priority: Major
> Labels: pull-request-available
>
> Add ASV microbenchmarks to python/benchmarks/bench_eval_type.py for the three Arrow-native incremental Aggregator eval types, which have no worker microbenchmark today: SQL_GROUPED_AGG_ARROW_INCREMENTAL_PARTIAL_UDF (255), SQL_GROUPED_AGG_ARROW_INCREMENTAL_FINAL_UDF (256) and SQL_WINDOW_AGG_ARROW_INCREMENTAL_UDF (257). Each gets a _*BenchMixin driving worker_main over the mock wire protocol plus Time/Peakmem subclasses, following the existing pattern; the UDF payload is a cloudpickled incremental Aggregator (zero/reduce/merge/finish + bufferSchema). Scenarios: PARTIAL few keys vs many keys hitting the maxRecordsPerBatch cap flush; FINAL many groups of struct partial buffers including null buffer rows; WINDOW unbounded, running (running-buffer extension) and sliding (refold) frames. Benchmark-only, no product change; enables before/after measurement of the SPARK-59727 migration.
--
This message was sent by Atlassian Jira
(v8.20.10#820010)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org