Support statefulset workloads in autoscaling#44855
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Static quality checks✅ Please find below the results from static quality gates Successful checksInfo
30 successful checks with minimal change (< 2 KiB)
On-wire sizes (compressed)
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Regression DetectorRegression Detector ResultsMetrics dashboard Baseline: 654cf36 Optimization Goals: ✅ No significant changes detected
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| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
|---|---|---|---|---|---|---|
| ➖ | docker_containers_cpu | % cpu utilization | -0.17 | [-3.14, +2.80] | 1 | Logs |
Fine details of change detection per experiment
| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
|---|---|---|---|---|---|---|
| ➖ | quality_gate_metrics_logs | memory utilization | +2.28 | [+2.06, +2.50] | 1 | Logs bounds checks dashboard |
| ➖ | tcp_syslog_to_blackhole | ingress throughput | +1.44 | [+1.37, +1.51] | 1 | Logs |
| ➖ | quality_gate_idle_all_features | memory utilization | +0.70 | [+0.67, +0.74] | 1 | Logs bounds checks dashboard |
| ➖ | ddot_metrics | memory utilization | +0.31 | [+0.08, +0.54] | 1 | Logs |
| ➖ | docker_containers_memory | memory utilization | +0.22 | [+0.15, +0.29] | 1 | Logs |
| ➖ | ddot_metrics_sum_cumulative | memory utilization | +0.16 | [-0.00, +0.33] | 1 | Logs |
| ➖ | ddot_logs | memory utilization | +0.11 | [+0.05, +0.18] | 1 | Logs |
| ➖ | file_to_blackhole_1000ms_latency | egress throughput | +0.05 | [-0.37, +0.47] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency | egress throughput | +0.01 | [-0.51, +0.54] | 1 | Logs |
| ➖ | file_tree | memory utilization | +0.01 | [-0.04, +0.07] | 1 | Logs |
| ➖ | tcp_dd_logs_filter_exclude | ingress throughput | +0.01 | [-0.08, +0.10] | 1 | Logs |
| ➖ | uds_dogstatsd_to_api | ingress throughput | -0.00 | [-0.14, +0.13] | 1 | Logs |
| ➖ | file_to_blackhole_500ms_latency | egress throughput | -0.01 | [-0.40, +0.38] | 1 | Logs |
| ➖ | uds_dogstatsd_to_api_v3 | ingress throughput | -0.01 | [-0.13, +0.11] | 1 | Logs |
| ➖ | uds_dogstatsd_20mb_12k_contexts_20_senders | memory utilization | -0.03 | [-0.09, +0.03] | 1 | Logs |
| ➖ | file_to_blackhole_100ms_latency | egress throughput | -0.08 | [-0.13, -0.04] | 1 | Logs |
| ➖ | ddot_metrics_sum_delta | memory utilization | -0.09 | [-0.29, +0.11] | 1 | Logs |
| ➖ | quality_gate_idle | memory utilization | -0.13 | [-0.18, -0.09] | 1 | Logs bounds checks dashboard |
| ➖ | docker_containers_cpu | % cpu utilization | -0.17 | [-3.14, +2.80] | 1 | Logs |
| ➖ | otlp_ingest_logs | memory utilization | -0.36 | [-0.47, -0.26] | 1 | Logs |
| ➖ | ddot_metrics_sum_cumulativetodelta_exporter | memory utilization | -0.74 | [-0.97, -0.50] | 1 | Logs |
| ➖ | otlp_ingest_metrics | memory utilization | -1.05 | [-1.20, -0.90] | 1 | Logs |
| ➖ | quality_gate_logs | % cpu utilization | -2.55 | [-4.02, -1.09] | 1 | Logs bounds checks dashboard |
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:
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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".
Replicate Execution Details
We run multiple replicates for each experiment/variant. However, we allow replicates to be automatically retried if there are any failures, up to 8 times, at which point the replicate is marked dead and we are unable to run analysis for the entire experiment. We call each of these attempts at running replicates a replicate execution. This section lists all replicate executions that failed due to the target crashing or being oom killed.
Note: In the below tables we bucket failures by experiment, variant, and failure type. For each of these buckets we list out the replicate indexes that failed with an annotation signifying how many times said replicate failed with the given failure mode. In the below example the baseline variant of the experiment named experiment_with_failures had two replicates that failed by oom kills. Replicate 0, which failed 8 executions, and replicate 1 which failed 6 executions, all with the same failure mode.
| Experiment | Variant | Replicates | Failure | Logs | Debug Dashboard |
|---|---|---|---|---|---|
| experiment_with_failures | baseline | 0 (x8) 1 (x6) | Oom killed | Debug Dashboard |
The debug dashboard links will take you to a debugging dashboard specifically designed to investigate replicate execution failures.
❌ Retried Profiling Replicate Execution Failures (target internal profiling)
Note: Profiling replicas may still be executing. See the debug dashboard for up to date status.
| Experiment | Variant | Replicates | Failure | Debug Dashboard |
|---|---|---|---|---|
| quality_gate_idle_all_features | baseline | 11 (x4) | Oom killed | Debug Dashboard |
| quality_gate_idle_all_features | comparison | 11 (x4) | Oom killed | Debug Dashboard |
CI Pass/Fail Decision
✅ Passed. All Quality Gates 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_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_metrics_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_metrics_logs, bounds check cpu_usage: 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 lost_bytes: 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.
…9-enable-stateful-set-workloads
| func (u *verticalController) syncDeploymentKind( | ||
| // isRolloutComplete checks if all pods of a workload have completed a rollout | ||
| // Returns true if the rollout is complete (all pods updated). | ||
| func isRolloutComplete(recommendationID string, pods []*workloadmeta.KubernetesPod, podsPerRecomendationID map[string]int32) bool { |
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I think we can name this slightly differently, what we are checking if is the current recommendation is entirely rolled out, something like isRecommendationRolloutComplete
| } | ||
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| // Create a fake Deployment in the dynamic client | ||
| deployment := &unstructured.Unstructured{ |
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You can use ToUnstructured from pkg/clusteragent/autoscaling/utils.go
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Only found few spelling typos.
| func (u *verticalController) syncDeploymentKind( | ||
| // isRecommendationRolloutComplete checks if the current recommendation is entirely rolled out. | ||
| // Returns true if all pods have the given recommendation ID. | ||
| func isRecommendationRolloutComplete(recommendationID string, pods []*workloadmeta.KubernetesPod, podsPerRecomendationID map[string]int32) bool { |
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typo
| func isRecommendationRolloutComplete(recommendationID string, pods []*workloadmeta.KubernetesPod, podsPerRecomendationID map[string]int32) bool { | |
| func isRecommendationRolloutComplete(recommendationID string, pods []*workloadmeta.KubernetesPod, podsPerRecommendationID map[string]int32) bool { |
| func isRecommendationRolloutComplete(recommendationID string, pods []*workloadmeta.KubernetesPod, podsPerRecomendationID map[string]int32) bool { | ||
| // currently basic check with 100% match expected. | ||
| // TODO: Refine the logic and add backoff for stuck PODs. | ||
| return podsPerRecomendationID[recommendationID] == int32(len(pods)) |
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typo
| return podsPerRecomendationID[recommendationID] == int32(len(pods)) | |
| return podsPerRecommendationID[recommendationID] == int32(len(pods)) |
| targetGVK schema.GroupVersionKind, | ||
| recommendationID string, | ||
| pods []*workloadmeta.KubernetesPod, | ||
| podsPerRecomendationID map[string]int32, |
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typo
| podsPerRecomendationID map[string]int32, | |
| podsPerRecommendationID map[string]int32, |
| podsPerDirectOwner map[string]int32, | ||
| ) (autoscaling.ProcessResult, error) { | ||
| // Check if we need to rollout | ||
| if isRecommendationRolloutComplete(recommendationID, pods, podsPerRecomendationID) { |
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| if isRecommendationRolloutComplete(recommendationID, pods, podsPerRecomendationID) { | |
| if isRecommendationRolloutComplete(recommendationID, pods, podsPerRecommendationID) { |
| case k8sutil.StatefulSetKind: | ||
| return u.syncStatefulSetKind(ctx, podAutoscaler, autoscalerInternal, updateStrategy, target, targetGVK, recomendationID, pods, podsPerRecomendationID) | ||
| default: | ||
| autoscalerInternal.UpdateFromVerticalAction(nil, fmt.Errorf("automic rollout not available for target Kind: %s. Applying to existing PODs require manual trigger", targetGVK.Kind)) |
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typo
| autoscalerInternal.UpdateFromVerticalAction(nil, fmt.Errorf("automic rollout not available for target Kind: %s. Applying to existing PODs require manual trigger", targetGVK.Kind)) | |
| autoscalerInternal.UpdateFromVerticalAction(nil, fmt.Errorf("automatic rollout not available for target Kind: %s. Applying to existing PODs require manual trigger", targetGVK.Kind)) |
…9-enable-stateful-set-workloads
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All contributors have signed the CLA ✍️ ✅ |
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I have read the CLA Document and I hereby sign the CLA |
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/merge |
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What does this PR do?
Adds support for StatefulSets in Kubernetes Autoscaling
Motivation
https://datadoghq.atlassian.net/browse/CASCL-779
Describe how you validated your changes
Additional Notes