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USMON-1008: Parse Produce Response #28526
USMON-1008: Parse Produce Response #28526
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Test changes on VMUse this command from test-infra-definitions to manually test this PR changes on a VM: inv create-vm --pipeline-id=43523913 --os-family=ubuntu Note: This applies to commit c533f19 |
Regression DetectorRegression Detector ResultsRun ID: b1775dee-5c6d-4c43-b13c-778a9338d03a Metrics dashboard Target profiles Baseline: b8d3295 Performance changes are noted in the perf column of each table:
No significant changes in experiment optimization goalsConfidence level: 90.00% There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.
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perf | experiment | goal | Δ mean % | Δ mean % CI | links |
---|---|---|---|---|---|
➖ | pycheck_lots_of_tags | % cpu utilization | +2.10 | [-0.44, +4.64] | Logs |
➖ | basic_py_check | % cpu utilization | +2.08 | [-0.71, +4.87] | Logs |
➖ | tcp_syslog_to_blackhole | ingress throughput | +0.51 | [-12.16, +13.17] | Logs |
➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +0.33 | [-0.55, +1.21] | Logs |
➖ | otel_to_otel_logs | ingress throughput | +0.06 | [-0.75, +0.87] | Logs |
➖ | idle | memory utilization | +0.06 | [+0.01, +0.10] | Logs |
➖ | tcp_dd_logs_filter_exclude | ingress throughput | +0.00 | [-0.01, +0.01] | Logs |
➖ | uds_dogstatsd_to_api | ingress throughput | -0.00 | [-0.00, +0.00] | Logs |
➖ | file_tree | memory utilization | -0.55 | [-0.64, -0.45] | Logs |
Bounds Checks
perf | experiment | bounds_check_name | replicates_passed |
---|---|---|---|
❌ | idle | memory_usage | 9/10 |
Explanation
A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".
For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:
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Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.
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Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.
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Its configuration does not mark it "erratic".
…as needed. Still, I don't see the error code in the user mode while running UT
…d to fix the UT though
…ests, and changed the helper functions to support this
…e_parse_response_partition_loop_fetch
…hould wait for the produce response
…will parse the response
/merge |
🚂 MergeQueue: waiting for PR to be ready This merge request is not mergeable yet, because of pending checks/missing approvals. It will be added to the queue as soon as checks pass and/or get approvals. Use |
🚂 MergeQueue: pull request added to the queue The median merge time in Use |
What does this PR do?
This PR adds support for parsing Kafka produce responses in the Kernel as part of the USM Kafka monitoring feature in system-probe. It includes capturing error codes and measuring latency for Kafka produce requests.
Motivation
Capturing error codes and latency for Kafka produce requests is crucial, as it provides the same visibility for produce requests that we already offer for fetch requests. This ensures that customers can effectively monitor and analyze their Kafka traffic.
Additional Notes
Possible Drawbacks / Trade-offs
Currently, we only support parsing produce requests with a single partition, so we’ve also limited response parsing to single-partition produce requests. While this hasn't caused issues in our dogfooding environment or with customers so far, we may revisit this decision in the future. Expanding support for multiple partitions would add complexity to the code.
Describe how to test/QA your changes
Load test results:
Link to staging deployment