[CONTINT-4545] Fix <missing> docker image layer digests from the docker collector #33384
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Decision✅ Passed |
Test changes on VMUse this command from test-infra-definitions to manually test this PR changes on a VM: inv aws.create-vm --pipeline-id=54529230 --os-family=ubuntuNote: This applies to commit b792781 |
Regression DetectorRegression Detector ResultsMetrics dashboard Baseline: 6136737 Optimization Goals: ✅ No significant changes detected
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| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
|---|---|---|---|---|---|---|
| ➖ | quality_gate_idle_all_features | memory utilization | +0.34 | [+0.28, +0.40] | 1 | Logs bounds checks dashboard |
| ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +0.30 | [-0.58, +1.18] | 1 | Logs |
| ➖ | file_to_blackhole_1000ms_latency_linear_load | egress throughput | +0.06 | [-0.41, +0.52] | 1 | Logs |
| ➖ | file_to_blackhole_100ms_latency | egress throughput | +0.04 | [-0.69, +0.76] | 1 | Logs |
| ➖ | file_to_blackhole_500ms_latency | egress throughput | +0.03 | [-0.76, +0.82] | 1 | Logs |
| ➖ | uds_dogstatsd_to_api | ingress throughput | +0.01 | [-0.27, +0.30] | 1 | Logs |
| ➖ | file_to_blackhole_300ms_latency | egress throughput | +0.01 | [-0.62, +0.64] | 1 | Logs |
| ➖ | tcp_dd_logs_filter_exclude | ingress throughput | +0.00 | [-0.02, +0.02] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency | egress throughput | -0.04 | [-0.88, +0.80] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency_http2 | egress throughput | -0.05 | [-0.82, +0.72] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency_http1 | egress throughput | -0.08 | [-0.97, +0.80] | 1 | Logs |
| ➖ | file_to_blackhole_1000ms_latency | egress throughput | -0.48 | [-1.25, +0.28] | 1 | Logs |
| ➖ | file_tree | memory utilization | -0.60 | [-0.67, -0.53] | 1 | Logs |
| ➖ | quality_gate_idle | memory utilization | -0.62 | [-0.68, -0.57] | 1 | Logs bounds checks dashboard |
| ➖ | quality_gate_logs | % cpu utilization | -1.33 | [-4.39, +1.73] | 1 | Logs |
| ➖ | tcp_syslog_to_blackhole | ingress throughput | -1.88 | [-1.97, -1.80] | 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, 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_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
- quality_gate_logs, 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_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
Docker history can incorrectly report layers inherited from base containers as having no created_by and no size; this commit attempts to accurately identify these falsely reported layers and assign them the appropriate digest.
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| digest := "" | ||
| if isInheritedLayer || !isEmptyLayer { | ||
| if isInheritedLayer { | ||
| log.Debugf("detected an inherited layer for image ID: \"%s\", assigning it digest: \"%s\"", inspect.ID, inspect.RootFS.Layers[inspectIdx]) |
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Is it better to use trace level if it is not frequently used?
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What does this PR do?
This PR solves the
<missing>image layer digests when inspecting an image from the Docker collector.Before
Image layer digests are missing
After
Image layer digests are populated
Motivation
Achieving consistency across our container runtime image collectors.
Describe how you validated your changes
dockeras the runtime (no additional args needed)crioas the runtimeworkload-list -vcommand on both deployed agents targeting a specific image that is affected by the docker misreporting issue (registry.k8s.io/kube-controller-manager@sha256:c8faedf1a5f3981ffade770c696b676d30613681a95be3287c1f7eec50e49b6d)full docker agent output
full crio output
sdiff output
Possible Drawbacks / Trade-offs
docker historyis unreliable - see belowdocker history generated by docker
docker history generated by containerd
Comparing the above outputs you can see that docker incorrectly reports multiple layers as being empty, this is a known issue.
This PR attempts to identify these incorrectly reported layers and assigns them their appropriate digest. There is a
log.Debug()message printed for observability.Additional Notes