DanielLeens opened a new issue, #11580:
URL: https://github.com/apache/seatunnel/issues/11580

   ### Search Before Asking
   
   - [x] I had searched in the 
[issues](https://github.com/apache/seatunnel/issues) and found no similar 
feature requirement.
   
   ### Description
   
   SeaTunnel already has useful building blocks for correctness and integration 
validation, such as `FakeSource`, `Assert`, connector E2E tests, and engine E2E 
containers. However, there is no standard benchmark harness that can run 
reproducible performance workloads, collect metrics, and compare results across 
versions or engines.
   
   A dedicated benchmark framework would help contributors and users evaluate 
throughput, latency, checkpoint behavior, resource usage, and regression risk 
in a consistent way.
   
   ### Proposed Scope
   
   This issue proposes adding a benchmark harness for SeaTunnel with at least 
the following capabilities:
   
   1. Define reusable benchmark workloads, for example `FakeSource -> 
LocalFile`, `FakeSource -> JDBC`, `JDBC -> JDBC`, and streaming workloads.
   2. Support configurable job mode, engine, parallelism, row count, split 
count, payload size, checkpoint interval, and sink settings.
   3. Collect benchmark evidence such as total records, elapsed time, 
throughput, failure status, and, where available, checkpoint and resource 
metrics.
   4. Produce machine-readable result files, such as JSON or CSV, so benchmark 
results can be compared across commits or releases.
   5. Reuse existing SeaTunnel test/container infrastructure where practical, 
instead of introducing a separate runtime model.
   
   ### Expected Deliverables
   
   - A new benchmark module, script set, or clearly documented benchmark entry 
point.
   - At least one runnable baseline benchmark workload for SeaTunnel Zeta.
   - Documentation explaining how to run the benchmark and how to interpret the 
output.
   - A result format that can be used for future regression comparison.
   
   ### Notes
   
   `FakeSource` is a good starting point for generating controlled data, but it 
may need benchmark-specific tuning or a separate performance source mode to 
avoid test-oriented throttling affecting throughput measurements.
   


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