nzw921rx opened a new issue, #12063:
URL: https://github.com/apache/seatunnel/issues/12063

   ## Description
   
   This is a focused investigation and improvement task for the latency 
variance observed in the IMap job-lifecycle growth benchmarks.
   
   It is not a performance regression report.
   
   The benchmark results show noticeable sample-to-sample variance in both 
running-job growth and completed-job history growth:
   
   | Benchmark | Parameters | Score | Error | CV |
   | --- | --- | ---: | ---: | ---: |
   | `runningJobGrowth` | `initialStoredJobCount=0` | 410.975 us/op | 15.74% | 
14.72% |
   | `runningJobGrowth` | `initialStoredJobCount=1000` | 342.508 us/op | 10.58% 
| 9.90% |
   | `completedJobHistoryGrowth` | `initialStoredJobCount=0` | 2,219.33 us/op | 
10.63% | 9.94% |
   | `completedJobHistoryGrowth` | `initialStoredJobCount=1000` | 2,015.11 
us/op | 10.03% | 9.38% |
   
   Benchmark run:
   
   https://github.com/apache/seatunnel/actions/runs/33750118527
   
   Other benchmarks executed in the same environment produced substantially 
lower variance. These two methods also share the same job-lifecycle growth 
workload, so they should be investigated together.
   
   The goals of this issue are to:
   
   1. Identify whether the variance comes from the benchmark fixture, Hazelcast 
IMap operations, serialization/allocation, WAL/FileMapStore persistence, 
lifecycle operations, or the execution environment.
   2. Improve the benchmark fixture or methodology if it causes the instability.
   3. Optimize the production job-lifecycle storage path if profiling confirms 
an implementation bottleneck.
   4. Preserve running-job and completed-job state correctness and durability.
   
   ## Relevant benchmarks
   
   ```text
   IMapJobStorageBenchmark.runningJobGrowth
   IMapJobStorageBenchmark.completedJobHistoryGrowth
   ```
   
   ## Profiling tools
   
   For an overview of the Zeta benchmark suite and usage, see the [SeaTunnel 
Zeta Benchmark Guide](https://seatunnel.apache.org/docs/engines/zeta/benchmark).
   
   SeaTunnel provides the `Benchmarks Diagnostics` workflow for running one 
exact benchmark method with CPU, wall-clock, lock, GC, or JFR profiling:
   
   
https://github.com/apache/seatunnel/actions/workflows/benchmarks_diagnostics.yml
   
   The benchmark can also be run directly from the GitHub Actions page by 
selecting **Benchmarks Diagnostics**, clicking **Run workflow**, and providing 
the target branch, tag, commit SHA, or trusted PR number together with the 
exact benchmark method.
   
   ## Local profiling
   
   Build the benchmark module:
   
   ```bash
   ./mvnw -Pbenchmark -pl seatunnel-benchmarks -am -DskipTests package
   ```
   
   Run a profiler locally:
   
   ```bash
   bash tools/benchmarks/profile_benchmarks.sh profile cpu \
     --repository . \
     --benchmark 'IMapJobStorageBenchmark.runningJobGrowth$'
   ```
   
   Replace `cpu` with `wall`, `lock`, or `gc` as needed. The same command can 
be used for:
   
   ```text
   IMapJobStorageBenchmark.completedJobHistoryGrowth$
   ```
   
   Capture a JFR recording:
   
   ```bash
   bash tools/benchmarks/profile_benchmarks.sh capture jfr \
     --repository . \
     --benchmark 'IMapJobStorageBenchmark.runningJobGrowth$'
   ```
   
   CPU, wall-clock, and lock profiling require `ASYNC_PROFILER_HOME`. The 
GitHub Actions workflow installs the required profiler automatically.
   
   ## Expected outcome
   
   This issue should result in both root-cause analysis and a focused 
improvement:
   
   - Identify and explain the primary source of the latency variance.
   - Improve the benchmark when the fixture or methodology is responsible.
   - Optimize the production job-lifecycle storage path when an implementation 
problem is confirmed.
   - Add or update benchmark coverage to validate the improvement.
   - Provide before-and-after results collected with the same JDK, runner, 
benchmark arguments, and storage configuration.
   - Compare both operation latency and variance before and after the change.
   - Add appropriate tests and confirm that running-job and completed-job state 
correctness and durability remain unchanged.
   
   The implementation should focus on the confirmed source and avoid unrelated, 
broad state-store refactoring.
   


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