Rebaseline core services with batch processing for prepare-position events [1 pts]

[PaulGregoryBaker]

Goal:

As a DevOps Engineer
I want to baseline core services with the new position prepare batching
so that we can ensure the performance and stability of the system with the updated functionality.

Acceptance Criteria:

• Update the Docker stack with the new position prepare batching changes.
• Prepare a set of performance test cases that stress the core services under different scenarios.
• Execute the performance test cases against the updated core services.
• Capture and analyze performance metrics and results.
• Document the baseline performance metrics for future reference.
• Verify that the batch configuration is somewhat optimised to improve performance without too adversely affecting latency.
• Verify that a recommendation/guideline is provider for the batch configuration

Complexity: Medium > Updating the Docker stack and running performance tests requires technical expertise.
Uncertainty: Low > The process for running performance tests and capturing results is well-established.

---

Tasks:

• Modify the Docker stack to include the new position prepare batching changes.
• Design and create a set of performance test cases that cover various usage scenarios.
• Execute the performance test cases against the updated core services.
• Monitor system performance and capture relevant metrics (e.g., response time, throughput, resource utilization).
• Analyze the test results and identify any performance bottlenecks or issues.
  • Identified an issue with kafka consumption on prepare handler when using latest version of central-ledger(v17.2.0) where as the version v17.0.3 is working fine. A story is created mojaloop/3529
  • As a temporary solution (to be not blocked by the above issue), backported some functionality to older version i.e v17.0.3 and used custom docker build for this test. The changes are in this branch central-ledger:feat/prepare-handler-publish-topic
• Document the baseline performance metrics and share them with the team.

Done

• Acceptance Criteria pass
• Docker stack is updated with new position prepare batching.
• Performance test cases are designed and executed successfully.
• Performance metrics and results are captured and documented.
• Any identified performance issues or bottlenecks are reported to the development team for resolution.

Pull Requests:

• feat(mojaloop/3520): rebaseline core services using position prepare batching ml-perf-characterization#12
• feat(mojaloop/3520): rebaseline with position prepare batching ml-core-test-harness#32

Follow-up:

• Collaborate with the development team to address any performance issues or bottlenecks found during testing.
• Perform regular performance testing to track system performance over time and detect any regressions.

Dependencies:

• New position prepare batching changes are available for integration.

Accountability:

• Owner: [Your Name]
• QA/Review: [Reviewer's Name]

[PaulGregoryBaker]

Hey team! Please add your planning poker estimate with Zenhub @aaronreynoza @kleyow @mdebarros @oderayi @sri-miriyala @vijayg10