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feat(autoscaling): implement CPURequestsRemoveLimitsMemoryRequestsAndLimits controlled value#49314

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feat(autoscaling): implement CPURequestsRemoveLimitsMemoryRequestsAndLimits controlled value#49314
clamoriniere wants to merge 3 commits intomainfrom
clamoriniere/autoscaling-burstable-v2

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@clamoriniere clamoriniere commented Apr 14, 2026

Summary

Implements the new CPURequestsRemoveLimitsMemoryRequestsAndLimits container controlledValues enum introduced in the datadog-operator (c59fc90).

When a container constraint sets this value, the autoscaler applies different strategies per resource:

  • CPU: only requests are controlled and any existing CPU limit is removed from the live pod, allowing the container to burst freely and avoid CPU throttling
  • Memory: both requests and limits are controlled (standard RequestsAndLimits semantics)

This is an alternative to the annotation-based burstable approach: the intent is expressed directly in the DPA/DPACP spec as a per-container constraint, with no separate annotation needed.

Changes

  • go.mod: bump datadog-operator/api to v0.0.0-20260414104914-c59fc90bbc2c
  • controller_vertical_helpers.go: strip CPU limit from the recommendation in applyVerticalConstraints when CPURequestsRemoveLimitsMemoryRequestsAndLimits is set, so the backend never pushes a new CPU limit
  • pod_patcher.go: add getContainerControlledValues helper (specific name > wildcard); pass controlledValues down to patchContainerResources which actively deletes any pre-existing CPU limit from the live pod

Test plan

  • TestApplyVerticalConstraints_CPURequestsRemoveLimits — CPU limit stripped from recommendation, memory limit preserved, hash recomputed
  • TestPatchContainerResources — CPU limit removed from pod, idempotent when already absent
  • TestPatchPod — end-to-end CPU limit removal on a live pod container

🤖 Assisted by Claude:claude-sonnet-4-6

…Limits controlled value

Bump datadog-operator/api to v0.0.0-20260414104914-c59fc90bbc2c which
introduces the new CPURequestsRemoveLimitsMemoryRequestsAndLimits enum
value for container controlledValues.

When a container constraint sets this value the autoscaler applies
different strategies per resource:
- CPU: request recommendation applied, existing CPU limits actively
  removed from the live pod so the container can burst freely.
- Memory: both requests and limits are controlled (RequestsAndLimits
  semantics unchanged).

Changes:
- applyVerticalConstraints: strip CPU from recommendation limits when
  CPURequestsRemoveLimitsMemoryRequestsAndLimits is set so that the
  backend never pushes a new CPU limit.
- patchContainerResources: actively delete any pre-existing CPU limit
  from the live pod for the same controlled value.
- getContainerControlledValues: new helper resolving ControlledValues
  from spec constraints (specific name > wildcard).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
@clamoriniere clamoriniere requested a review from a team as a code owner April 14, 2026 12:15
@dd-octo-sts dd-octo-sts Bot added the internal Identify a non-fork PR label Apr 14, 2026
@github-actions github-actions Bot added the medium review PR review might take time label Apr 14, 2026
@clamoriniere clamoriniere added this to the 7.79.0 milestone Apr 14, 2026
@clamoriniere clamoriniere added the qa/done QA done before merge and regressions are covered by tests label Apr 14, 2026
…estsAndLimits

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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dd-octo-sts Bot commented Apr 14, 2026

Files inventory check summary

File checks results against ancestor b313f4b2:

Results for datadog-agent_7.79.0~devel.git.710.cd2fa28.pipeline.107584036-1_amd64.deb:

No change detected

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cit-pr-commenter-54b7da Bot commented Apr 14, 2026

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 0837f1e6-33dc-43d2-a8b3-bf67d72fb73f

Baseline: b313f4b
Comparison: cd2fa28
Diff

Optimization Goals: ❌ Regression(s) detected

perf experiment goal Δ mean % Δ mean % CI trials links
quality_gate_logs % cpu utilization +6.50 [+4.76, +8.24] 1 Logs bounds checks dashboard

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf experiment goal Δ mean % Δ mean % CI trials links
docker_containers_cpu % cpu utilization +0.02 [-2.97, +3.01] 1 Logs

Fine details of change detection per experiment

perf experiment goal Δ mean % Δ mean % CI trials links
quality_gate_logs % cpu utilization +6.50 [+4.76, +8.24] 1 Logs bounds checks dashboard
otlp_ingest_logs memory utilization +0.96 [+0.85, +1.07] 1 Logs
quality_gate_metrics_logs memory utilization +0.64 [+0.39, +0.88] 1 Logs bounds checks dashboard
ddot_metrics_sum_delta memory utilization +0.37 [+0.20, +0.54] 1 Logs
tcp_syslog_to_blackhole ingress throughput +0.31 [+0.14, +0.48] 1 Logs
file_tree memory utilization +0.25 [+0.19, +0.31] 1 Logs
uds_dogstatsd_20mb_12k_contexts_20_senders memory utilization +0.24 [+0.17, +0.30] 1 Logs
docker_containers_cpu % cpu utilization +0.02 [-2.97, +3.01] 1 Logs
tcp_dd_logs_filter_exclude ingress throughput +0.01 [-0.11, +0.12] 1 Logs
uds_dogstatsd_to_api ingress throughput +0.00 [-0.21, +0.21] 1 Logs
docker_containers_memory memory utilization -0.01 [-0.09, +0.07] 1 Logs
uds_dogstatsd_to_api_v3 ingress throughput -0.01 [-0.22, +0.20] 1 Logs
file_to_blackhole_500ms_latency egress throughput -0.02 [-0.42, +0.38] 1 Logs
quality_gate_idle_all_features memory utilization -0.02 [-0.06, +0.01] 1 Logs bounds checks dashboard
file_to_blackhole_1000ms_latency egress throughput -0.05 [-0.48, +0.38] 1 Logs
file_to_blackhole_100ms_latency egress throughput -0.05 [-0.18, +0.08] 1 Logs
quality_gate_idle memory utilization -0.05 [-0.10, -0.00] 1 Logs bounds checks dashboard
file_to_blackhole_0ms_latency egress throughput -0.09 [-0.66, +0.48] 1 Logs
otlp_ingest_metrics memory utilization -0.19 [-0.35, -0.02] 1 Logs
ddot_metrics memory utilization -0.22 [-0.39, -0.04] 1 Logs
ddot_metrics_sum_cumulativetodelta_exporter memory utilization -0.23 [-0.44, -0.01] 1 Logs
ddot_logs memory utilization -0.58 [-0.65, -0.51] 1 Logs
ddot_metrics_sum_cumulative memory utilization -0.73 [-0.87, -0.59] 1 Logs

Bounds Checks: ✅ Passed

perf experiment bounds_check_name replicates_passed observed_value links
docker_containers_cpu simple_check_run 10/10 655 ≥ 26
docker_containers_memory memory_usage 10/10 275.51MiB ≤ 370MiB
docker_containers_memory simple_check_run 10/10 683 ≥ 26
file_to_blackhole_0ms_latency memory_usage 10/10 0.19GiB ≤ 1.20GiB
file_to_blackhole_0ms_latency missed_bytes 10/10 0B = 0B
file_to_blackhole_1000ms_latency memory_usage 10/10 0.23GiB ≤ 1.20GiB
file_to_blackhole_1000ms_latency missed_bytes 10/10 0B = 0B
file_to_blackhole_100ms_latency memory_usage 10/10 0.21GiB ≤ 1.20GiB
file_to_blackhole_100ms_latency missed_bytes 10/10 0B = 0B
file_to_blackhole_500ms_latency memory_usage 10/10 0.22GiB ≤ 1.20GiB
file_to_blackhole_500ms_latency missed_bytes 10/10 0B = 0B
quality_gate_idle intake_connections 10/10 3 = 3 bounds checks dashboard
quality_gate_idle memory_usage 10/10 176.98MiB ≤ 181MiB bounds checks dashboard
quality_gate_idle_all_features intake_connections 10/10 3 = 3 bounds checks dashboard
quality_gate_idle_all_features memory_usage 10/10 502.62MiB ≤ 550MiB bounds checks dashboard
quality_gate_logs intake_connections 10/10 3 ≤ 6 bounds checks dashboard
quality_gate_logs memory_usage 10/10 203.38MiB ≤ 220MiB bounds checks dashboard
quality_gate_logs missed_bytes 10/10 0B = 0B bounds checks dashboard
quality_gate_metrics_logs cpu_usage 10/10 349.63 ≤ 2000 bounds checks dashboard
quality_gate_metrics_logs intake_connections 10/10 4 ≤ 6 bounds checks dashboard
quality_gate_metrics_logs memory_usage 10/10 425.81MiB ≤ 475MiB bounds checks dashboard
quality_gate_metrics_logs missed_bytes 10/10 0B = 0B 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:

  1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

  2. 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.

  3. Its configuration does not mark it "erratic".

CI Pass/Fail Decision

Passed. All Quality Gates passed.

  • quality_gate_metrics_logs, bounds check memory_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 cpu_usage: 10/10 replicas passed. Gate passed.
  • quality_gate_metrics_logs, bounds check missed_bytes: 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_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 missed_bytes: 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.

…val to pod patcher

Replace the `controlledValues` parameter threaded through `patchPod` /
`patchContainerResources` with a sentinel approach: `applyVerticalConstraints`
inserts `resource.MustParse("-1")` into `ContainerResources.Limits[cpu]` to
signal that any pre-existing CPU limit must be actively deleted from the live
pod. `patchContainerResources` detects the sentinel via `Cmp()` and deletes
the limit entry, keeping the function signatures clean.

The insertion is split into two phases: phase 1 deletes the CPU limit before
the clamping and `limits >= requests` invariant check; phase 2 inserts the
sentinel after the invariant check to prevent it from being overwritten.

`BuildStatus` is updated to call `ContainerResourcesForStatus()` (new helper
on `VerticalScalingValues`) which strips any negative-quantity limit entries
before writing to the DPA status, so the sentinel never leaks into the CRD.

Assisted-by: Claude:claude-sonnet-4-6
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