[Backport 7.77.x] fix(cluster-agent): Guard against re-admission for APM auto-instrumentation in image_volume mode#46819
Conversation
…tation in image_volume mode (#46743) ### What does this PR do? Avoid double-injection by returning early if the pod already has image_volume mode's init containers. Init_container mode was already guarded by checking for per-language init containers (e.g. datadog-lib-python-init). This change adds the same style of guard for image_volume mode by checking for the datadog-apm-inject-preload init container. ### Motivation The webhook may be run twice, but we do not want to inject twice. CSI mode needs a guard in the future as well. ### Describe how you validated your changes Tests in target_mutator_test.go were added for both re-admission cases: one for init_container mode and one for image_volume mode. The test asserts that the pod is not changed ("mutated") at all in the case that the representative init container(s) are present. ### Additional Notes Co-authored-by: mikayla.toffler <mikayla.toffler@datadoghq.com> (cherry picked from commit 2d33331) ___ Co-authored-by: Mikayla Toffler <46911781+mtoffl01@users.noreply.github.com>
Static quality checks✅ Please find below the results from static quality gates 31 successful checks with minimal change (< 2 KiB)
On-wire sizes (compressed)
|
Regression DetectorRegression Detector ResultsMetrics dashboard Baseline: cded3a1 Optimization Goals: ✅ No significant changes detected
|
| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
|---|---|---|---|---|---|---|
| ➖ | docker_containers_cpu | % cpu utilization | -2.52 | [-5.59, +0.55] | 1 | Logs |
Fine details of change detection per experiment
| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
|---|---|---|---|---|---|---|
| ➖ | quality_gate_logs | % cpu utilization | +1.27 | [-0.25, +2.79] | 1 | Logs bounds checks dashboard |
| ➖ | ddot_metrics | memory utilization | +0.73 | [+0.51, +0.94] | 1 | Logs |
| ➖ | otlp_ingest_logs | memory utilization | +0.31 | [+0.22, +0.40] | 1 | Logs |
| ➖ | quality_gate_metrics_logs | memory utilization | +0.20 | [-0.01, +0.41] | 1 | Logs bounds checks dashboard |
| ➖ | ddot_metrics_sum_cumulative | memory utilization | +0.19 | [+0.04, +0.35] | 1 | Logs |
| ➖ | otlp_ingest_metrics | memory utilization | +0.13 | [-0.03, +0.29] | 1 | Logs |
| ➖ | ddot_metrics_sum_delta | memory utilization | +0.05 | [-0.15, +0.26] | 1 | Logs |
| ➖ | uds_dogstatsd_to_api_v3 | ingress throughput | +0.02 | [-0.11, +0.15] | 1 | Logs |
| ➖ | file_to_blackhole_500ms_latency | egress throughput | +0.01 | [-0.36, +0.39] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency | egress throughput | +0.01 | [-0.48, +0.49] | 1 | Logs |
| ➖ | file_to_blackhole_1000ms_latency | egress throughput | +0.00 | [-0.42, +0.43] | 1 | Logs |
| ➖ | tcp_dd_logs_filter_exclude | ingress throughput | -0.01 | [-0.11, +0.09] | 1 | Logs |
| ➖ | uds_dogstatsd_to_api | ingress throughput | -0.01 | [-0.15, +0.13] | 1 | Logs |
| ➖ | file_to_blackhole_100ms_latency | egress throughput | -0.01 | [-0.06, +0.03] | 1 | Logs |
| ➖ | uds_dogstatsd_20mb_12k_contexts_20_senders | memory utilization | -0.06 | [-0.12, -0.01] | 1 | Logs |
| ➖ | quality_gate_idle_all_features | memory utilization | -0.12 | [-0.15, -0.08] | 1 | Logs bounds checks dashboard |
| ➖ | file_tree | memory utilization | -0.19 | [-0.25, -0.14] | 1 | Logs |
| ➖ | quality_gate_idle | memory utilization | -0.24 | [-0.28, -0.20] | 1 | Logs bounds checks dashboard |
| ➖ | ddot_logs | memory utilization | -0.26 | [-0.33, -0.20] | 1 | Logs |
| ➖ | tcp_syslog_to_blackhole | ingress throughput | -0.29 | [-0.36, -0.22] | 1 | Logs |
| ➖ | docker_containers_memory | memory utilization | -0.31 | [-0.39, -0.24] | 1 | Logs |
| ➖ | ddot_metrics_sum_cumulativetodelta_exporter | memory utilization | -0.37 | [-0.60, -0.14] | 1 | Logs |
| ➖ | docker_containers_cpu | % cpu utilization | -2.52 | [-5.59, +0.55] | 1 | Logs |
Bounds Checks: ✅ Passed
| perf | experiment | bounds_check_name | replicates_passed | links |
|---|---|---|---|---|
| ✅ | docker_containers_cpu | simple_check_run | 10/10 | |
| ✅ | docker_containers_memory | memory_usage | 10/10 | |
| ✅ | docker_containers_memory | simple_check_run | 10/10 | |
| ✅ | file_to_blackhole_0ms_latency | lost_bytes | 10/10 | |
| ✅ | file_to_blackhole_0ms_latency | memory_usage | 10/10 | |
| ✅ | file_to_blackhole_1000ms_latency | lost_bytes | 10/10 | |
| ✅ | file_to_blackhole_1000ms_latency | 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_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 | bounds checks dashboard |
| ✅ | quality_gate_logs | lost_bytes | 10/10 | bounds checks dashboard |
| ✅ | quality_gate_logs | memory_usage | 10/10 | bounds checks dashboard |
| ✅ | quality_gate_metrics_logs | cpu_usage | 10/10 | bounds checks dashboard |
| ✅ | quality_gate_metrics_logs | intake_connections | 10/10 | bounds checks dashboard |
| ✅ | quality_gate_metrics_logs | lost_bytes | 10/10 | bounds checks dashboard |
| ✅ | quality_gate_metrics_logs | memory_usage | 10/10 | bounds checks dashboard |
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:
-
Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.
-
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.
-
Its configuration does not mark it "erratic".
CI Pass/Fail Decision
✅ Passed. All Quality Gates 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_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.
- quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
- quality_gate_metrics_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
- quality_gate_metrics_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_metrics_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
- quality_gate_metrics_logs, bounds check cpu_usage: 10/10 replicas passed. Gate passed.
Backport 2d33331 from #46743.
What does this PR do?
Avoid double-injection by returning early if the pod already has image_volume mode's init containers.
Init_container mode was already guarded by checking for per-language init containers (e.g. datadog-lib-python-init). This change adds the same style of guard for image_volume mode by checking for the datadog-apm-inject-preload init container.
Motivation
The webhook may be run twice, but we do not want to inject twice. CSI mode needs a guard in the future as well.
Describe how you validated your changes
Tests in target_mutator_test.go were added for both re-admission cases: one for init_container mode and one for image_volume mode. The test asserts that the pod is not changed ("mutated") at all in the case that the representative init container(s) are present.
Additional Notes