Skip to content

Recommend Mode

Recommend mode is the safest way to start with attune. The operator collects Prometheus metrics, computes recommendations, and writes them to the policy's .status.recommendations field. No pods are modified.

Creating a Recommend-mode policy

apiVersion: attune.io/v1alpha1
kind: AttunePolicy
metadata:
  name: api-services
  namespace: production
spec:
  targetRef:
    kind: Deployment
    selector:
      matchLabels:
        tier: api
  metricsSource:
    prometheus:
      address: http://prometheus-server.monitoring:80
    historyWindow: 168h
    minimumDataPoints: 48
  cpu:
    percentile: 95
    overhead: "20"
    minAllowed: "50m"
    maxAllowed: "4000m"
    controlledValues: RequestsAndLimits
  memory:
    percentile: 99
    overhead: "30"
    minAllowed: "64Mi"
    maxAllowed: "8Gi"
    allowDecrease: false
  updateStrategy:
    type: Recommend
    cooldown: 1h

Reading recommendations from status

kubectl get attunepolicy api-services -o jsonpath='{.status.recommendations[*]}' | jq .
kubectl attune recommendations -n production
kubectl attune explain -n production api-services

Each entry in the array contains:

Field Description
workload Name of the matched Deployment/StatefulSet/DaemonSet
containers[].name Container name
containers[].current Current CPU/memory requests and limits
containers[].recommended Proposed CPU/memory requests and limits
containers[].explanation Estimator chain (percentile → margin → burst → confidence → bounds → change filter)
containers[].confidence Score between 0 and 1
containers[].dataPoints Number of Prometheus samples used
stale When true, Prometheus returned no fresh data; resizes are blocked

kubectl attune explain (and status.recommendations[].containers[].explanation) walks the same chain the controller used:

Stage Field What it means
Raw percentile rawPercentile Selected percentile across hourly buckets (max hour)
Overhead afterOverhead Safety margin applied to the percentile
Burst burstFactor / afterBurst Extra headroom when peak >> percentile
Confidence confidenceFactor / afterConfidence Widens when data is sparse
Bounds afterBounds / boundsApplied Clamped to minAllowed / maxAllowed
Change filter afterChangeFilter / changeFilterApplied Min % change and max step caps
Final final / finalAdjustment Value written to recommended (plus any controller post-steps)

Field names match status JSON so automation can parse without scraping prose.

Why no resize happened?

Recommendations can look "ready" while pods stay unchanged. Common reasons:

Reason Where to look What to do
Change filter / min change explanation.*.changeFilterApplied Expected for tiny deltas; lower min change or wait for drift
Cooldown condition Resizing=False reason CooldownActive Wait for cooldown / backoff
Budget cap events BudgetExhausted, metric attune_budget_exhausted_total Raise per-cycle caps or reduce targets
Canary not promoted status.canary.phase Wait for observation or set autoPromote
Deferred / Infeasible condition ResizeBlocked, workloads.deferred / infeasible See troubleshooting
Stale data recommendations[].stale Fix Prometheus reachability
Schedule window condition ScheduleBlocked Wait for window or adjust schedule
At target after clamp events / logs for filtering or memory clamp Expected when already near recommendation

Interpreting confidence scores

The confidence score reflects how much data backs the recommendation:

Score Meaning Action
0.0 - 0.3 Very low; sparse recent data Wait for more data
0.3 - 0.6 Moderate; partial coverage Review manually before acting
0.6 - 0.8 Good; substantial recent coverage Safe to promote to Canary
0.8 - 1.0 High; near-complete history coverage Safe to promote to Auto

Tip

Increase historyWindow and minimumDataPoints for workloads with weekly traffic patterns so the estimator captures weekday/weekend variation.

Automation / JSON

For scripts, prefer status JSON over parsing explain prose:

kubectl get attunepolicy api-services -n production -o json | \
  jq '.status.recommendations[] | {workload, containers: [.containers[] | {name, recommended, explanation}]}'

Schema fields under explanation are versioned with the CRD; breaking renames require a CRD/API bump and docs update.

Estimating savings

The policy status includes aggregated savings:

kubectl get attunepolicy api-services -o jsonpath='{.status.savings}' | jq .

{
  "cpuRequestReduction": "1200m",
  "memoryRequestReduction": "2Gi"
}

These values represent the total reduction across all matched workloads. Multiply by your per-core and per-GiB cloud pricing to estimate monthly cost savings.

Promoting to an active mode

When you are satisfied with the recommendations, change the mode:

  • Use Canary to resize a subset first.
  • Use OneShot to resize a single pod per reconciliation cycle.
  • Use Auto to resize all eligible pods (best for non-critical workloads).
kubectl patch attunepolicy api-services --type merge \
  -p '{"spec":{"updateStrategy":{"type":"Canary","canary":{"percentage":10,"observationPeriod":"30m"}}}}'