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fix(ssi): extraneous or incorrect fields should error #34001
fix(ssi): extraneous or incorrect fields should error #34001
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This commit ensures that incorrect fields will throw an error instead of silently being ignored.
Regression DetectorRegression Detector ResultsMetrics dashboard Baseline: c356877 Optimization Goals: ✅ No significant changes detected
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perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
---|---|---|---|---|---|---|
➖ | tcp_syslog_to_blackhole | ingress throughput | +0.58 | [+0.48, +0.68] | 1 | Logs |
➖ | file_tree | memory utilization | +0.57 | [+0.50, +0.63] | 1 | Logs |
➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +0.53 | [-0.40, +1.45] | 1 | Logs |
➖ | quality_gate_idle | memory utilization | +0.45 | [+0.41, +0.49] | 1 | Logs bounds checks dashboard |
➖ | file_to_blackhole_500ms_latency | egress throughput | +0.44 | [-0.35, +1.22] | 1 | Logs |
➖ | file_to_blackhole_1000ms_latency | egress throughput | +0.08 | [-0.68, +0.85] | 1 | Logs |
➖ | file_to_blackhole_100ms_latency | egress throughput | +0.05 | [-0.64, +0.74] | 1 | Logs |
➖ | tcp_dd_logs_filter_exclude | ingress throughput | -0.00 | [-0.02, +0.02] | 1 | Logs |
➖ | uds_dogstatsd_to_api | ingress throughput | -0.01 | [-0.29, +0.27] | 1 | Logs |
➖ | file_to_blackhole_300ms_latency | egress throughput | -0.02 | [-0.65, +0.62] | 1 | Logs |
➖ | file_to_blackhole_0ms_latency | egress throughput | -0.02 | [-0.88, +0.84] | 1 | Logs |
➖ | file_to_blackhole_0ms_latency_http1 | egress throughput | -0.03 | [-0.95, +0.88] | 1 | Logs |
➖ | quality_gate_idle_all_features | memory utilization | -0.03 | [-0.10, +0.03] | 1 | Logs bounds checks dashboard |
➖ | file_to_blackhole_0ms_latency_http2 | egress throughput | -0.03 | [-0.94, +0.87] | 1 | Logs |
➖ | file_to_blackhole_1000ms_latency_linear_load | egress throughput | -0.18 | [-0.65, +0.29] | 1 | Logs |
➖ | quality_gate_logs | % cpu utilization | -1.87 | [-4.92, +1.18] | 1 | Logs |
Bounds Checks: ✅ Passed
perf | experiment | bounds_check_name | replicates_passed | links |
---|---|---|---|---|
✅ | file_to_blackhole_0ms_latency | lost_bytes | 10/10 | |
✅ | file_to_blackhole_0ms_latency | memory_usage | 10/10 | |
✅ | file_to_blackhole_0ms_latency_http1 | lost_bytes | 10/10 | |
✅ | file_to_blackhole_0ms_latency_http1 | memory_usage | 10/10 | |
✅ | file_to_blackhole_0ms_latency_http2 | lost_bytes | 10/10 | |
✅ | file_to_blackhole_0ms_latency_http2 | memory_usage | 10/10 | |
✅ | file_to_blackhole_1000ms_latency | memory_usage | 10/10 | |
✅ | file_to_blackhole_1000ms_latency_linear_load | memory_usage | 10/10 | |
✅ | file_to_blackhole_100ms_latency | lost_bytes | 10/10 | |
✅ | file_to_blackhole_100ms_latency | memory_usage | 10/10 | |
✅ | file_to_blackhole_300ms_latency | lost_bytes | 10/10 | |
✅ | file_to_blackhole_300ms_latency | memory_usage | 10/10 | |
✅ | file_to_blackhole_500ms_latency | lost_bytes | 10/10 | |
✅ | file_to_blackhole_500ms_latency | memory_usage | 10/10 | |
✅ | quality_gate_idle | intake_connections | 10/10 | bounds checks dashboard |
✅ | quality_gate_idle | memory_usage | 10/10 | bounds checks dashboard |
✅ | quality_gate_idle_all_features | intake_connections | 10/10 | bounds checks dashboard |
✅ | quality_gate_idle_all_features | memory_usage | 10/10 | bounds checks dashboard |
✅ | quality_gate_logs | intake_connections | 10/10 | |
✅ | quality_gate_logs | lost_bytes | 10/10 | |
✅ | quality_gate_logs | memory_usage | 10/10 |
Explanation
Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%
Performance changes are noted in the perf column of each table:
- ✅ = significantly better comparison variant performance
- ❌ = significantly worse comparison variant performance
- ➖ = no significant change in performance
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".
CI Pass/Fail Decision
✅ Passed. All Quality Gates passed.
- quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_idle_all_features, bounds check intake_connections: 10/10 replicas passed. Gate passed.
- quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_idle, bounds check intake_connections: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
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👍 for the container-platform files
The config part is owned by agent-configuration, so I'll leave it to them for review.
/merge |
View all feedbacks in Devflow UI.
The median merge time in
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What does this PR do?
This commit ensures that extraneous fields (and by extension, incorrect fields) will throw an error instead of silently being ignored.
Motivation
In Kubernetes SSI | Workload Selection 🎯, we have a pretty complex config structure for targets. Because the first target that matches applies, having a misconfigured target will be really confusing. Additionally, almost all fields in a target are optional, so it would be very easy to typo and end up with wildly different behavior.
A simple example is one I found myself typing
selector
when the actual key ispodSelector
. A default pod selector matches all pods, so this felt like the feature was totally broken when in fact I simply had an incorrect key.Describe how you validated your changes
The additional unit tests.
Possible Drawbacks / Trade-offs
Another solution is that we can use the
viper.UnmarshallKey
in the short term so that we can enableErrorUnused
and add a TODO. This would allow us to make the agent release without needing to modify thestructure
package.Additional Notes
I did not add a release note because this is fixing a bug in an unreleased feature. We have a release note for the feature as a whole.