What is Partial outage? Meaning, Architecture, Examples, Use Cases, and How to Measure It (2026 Guide)

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Quick Definition (30–60 words)

A partial outage is when a subset of a service, region, or user group experiences degraded or unavailable functionality while other parts remain healthy. Analogy: a partial blackout where some city blocks lose power while others stay lit. Formal: a scoped availability failure affecting non-global service surface area.

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What is Partial outage?

A partial outage is a scoped availability or degradation incident that does not fully take down a product or service globally. It affects a subset of users, features, regions, or infrastructure components. It is NOT a full outage, a planned maintenance event (unless unplanned), nor purely a performance slowdown that impacts all traffic equally.

Key properties and constraints:

• Scope-limited: constrained to specific components, regions, or request types.
• Partial user impact: some users unaffected; others have degraded or no service.
• Heterogeneous symptoms: errors, timeouts, increased latency, incorrect responses.
• Transient or persistent: can be temporary (minutes) or persistent until remediated.
• Operationally ambiguous: hard to detect with coarse global metrics.
• Requires targeted mitigation strategies: routing, feature flags, retries, regional failover.

Where it fits in modern cloud/SRE workflows:

• Incident classification and priority: often urgent due to user segmentation.
• SLO-level impact: consumes error budget for affected SLIs but not global SLIs.
• Runbooks: needs scoped runbooks and playbooks for isolation and rollback.
• Observability: demands high cardinality telemetry and localized alerts.
• Automation: benefits from intelligent routing, canary rollbacks, and auto-heal.

Diagram description (text-only visualization):

• Users from multiple regions -> Edge/load balancer -> Traffic split by region and feature flag -> Microservices cluster A and B in region X and Y -> Dependencies include DB shard 1 and 2, third-party API -> Partial outage manifests as errors from cluster B and DB shard 2, while cluster A and shard 1 remain healthy.

Partial outage in one sentence

A partial outage is a constrained failure where only a portion of the service surface—users, regions, features, or infrastructure—fails or degrades while others continue to operate.

Partial outage vs related terms (TABLE REQUIRED)

ID	Term	How it differs from Partial outage	Common confusion
T1	Full outage	Global service is unavailable	People call any outage a full outage
T2	Degradation	Performance drop can be global or partial	Degradation can be partial or total
T3	Incident	Any operational problem	Not all incidents are outages
T4	Partial deployment failure	Only new release causes issues for subset	Blamed as an outage incorrectly
T5	Regional outage	Affects a geographic region only	Partial outage may be multi-region subset
T6	Feature flag failure	Feature-specific user impact	Can be mistaken for general outage
T7	Network partition	Connectivity split between components	Network partition can cause partial outage
T8	Capacity exhaust	Resource limits hit in subset	Often causes partial service unavailability
T9	Latency spike	Short delay increases response time	Latency may not cause request failures
T10	Dependency outage	Third party fails, affecting subset	Can cascade into partial outage

Row Details (only if any cell says “See details below”)

• None.

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Why does Partial outage matter?

Business impact:

• Revenue: even partial outages can block high-value customers or regions, causing measurable revenue loss.
• Trust: repeated partial outages erode customer confidence and increase churn.
• Compliance and contracts: SLAs tied to availability for subset services can trigger credits or legal exposure.
• Opportunity cost: manual mitigation consumes senior engineers and delays feature delivery.

Engineering impact:

• Incident burden: fragmented incidents increase mean time to repair (MTTR) without proper isolation.
• Velocity trade-offs: teams may pause deployments or add guardrails that slow release cadence.
• Technical debt exposure: hidden single points of failure become visible.
• Increased complexity: handling multiple partial outages across microservices calls for better automation and testing.

SRE framing:

• SLIs/SLOs: partial outage requires scoped SLIs (per-region or per-feature) rather than only global SLIs.
• Error budgets: consume error budget for specific slices; global budget may remain unused.
• Toil: manual routing changes or customer communications increase toil.
• On-call: needs targeted routing of incidents to owners who understand the affected slice.

What breaks in production — realistic examples:

1. Database shard reachable for 80% of users but one shard returns timeouts causing errors for 20% of users.
2. A new feature rolled out via phased deployment has a bug that crashes only mobile clients.
3. CDN edge POP in a region misroutes TLS handshakes causing regional failures for corporate customers.
4. A third-party payment gateway rate-limits specific merchant IDs leading to payment errors for a subset of transactions.
5. Autoscaling misconfiguration causes backend pool depletion under specific request patterns.

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Where is Partial outage used? (TABLE REQUIRED)

ID	Layer/Area	How Partial outage appears	Typical telemetry	Common tools
L1	Edge and CDN	Some POPs fail or misroute	Edge errors and regional RUM	CDN logs CDN config
L2	Network	Packet loss in specific AZ	Packet loss counters and traceroutes	Network monitors traceroute
L3	Service mesh	One subset of pods drops requests	Per-pod request success	Mesh telemetry mesh dashboard
L4	Application	Feature endpoint errors for subset	Error rate by user segment	APM user traces
L5	Data layer	Shard or replica lag	Replica lag metrics and errors	DB monitoring DB alerts
L6	Serverless	Cold-start spikes for specific region	Invocation errors and latency	Serverless dashboard logs
L7	Kubernetes	Node pool or daemonset issue	Pod crashloop, node Ready	K8s monitoring kubectl
L8	CI/CD	Canary fails for subset users	Deployment failure metrics	CI job logs rollout tool
L9	Security	WAF rule blocks specific clients	Block count and false positives	WAF logs SIEM
L10	Third-party API	Vendor returns 403 for certain IDs	Vendor error code distribution	API gateway logs

Row Details (only if needed)

• None.

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When should you use Partial outage?

When it’s necessary:

• You need to limit collateral damage during failures: route traffic away from unhealthy regions or features.
• You want to preserve availability for unaffected users while isolating a problematic slice.
• SLA/SLOs are defined per-customer, region, or feature and you need targeted incident handling.

When it’s optional:

• You can tolerate brief global degradation for a simpler remediation when impact is minimal.
• If the affected slice represents negligible traffic or low-value users.

When NOT to use / overuse it:

• Don’t over-segment SLIs/SLOs to the point of creating operational noise and indistinguishable alerts.
• Avoid using partial outages as a persistent workaround—fix root cause.
• Avoid wide-ranging feature flags for every small behavior; complexity increases risk.

Decision checklist:

• If high-value users are affected AND global traffic healthy -> prioritize scoped remediation.
• If errors affect majority of customers -> treat as full outage and invoke broader playbook.
• If third-party dependency affects subset -> consider retry/backoff and degrade gracefully.
• If deployment caused issue in canary stage -> rollback canary or disable feature flag.

Maturity ladder:

• Beginner: basic regional metrics and manual routing.
• Intermediate: scoped SLIs, feature flags, automated traffic shifting.
• Advanced: automated guarded rollouts, AI-assisted anomaly detection, dynamic failover with no human intervention.

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How does Partial outage work?

Components and workflow:

• Ingress: load balancer, CDN, API gateway routes traffic by region, client type, or feature.
• Routing rules: service discovery and routing tables determine target clusters.
• Service instances: pods, VMs, or serverless functions serving traffic.
• Data stores: sharded or partitioned storage with per-shard health.
• Observability plane: high-cardinality traces, logs, metrics, RUM.
• Control plane: CI/CD, feature flagging, orchestration for rollback and traffic control.
• Automation: policies for circuit breaking, rate limiting, and auto-remediation.

Data flow and lifecycle:

1. Request enters via edge; routing evaluates rules.
2. Request lands on an instance; instance consults local dependencies.
3. Failure occurs in a subset (shard, region, feature).
4. Observability emits high-cardinality telemetry scoped to the affected slice.
5. Alerting triggers scoped incident responses and automated mitigations.
6. Traffic reroutes or feature is disabled; validation checks restore service.

Edge cases and failure modes:

• Cross-dependency cascades: one shard failure causes a fan-out of retries and overload.
• Split-brain routing: control plane thinks traffic is safe to route while data plane fails.
• Monitoring blind spots: no per-slice SLIs leads to unnoticed partial outage.
• Automation misfires: automated rollback targets wrong revision under noisy signals.

Typical architecture patterns for Partial outage

• Canary and Feature-flagged Rollouts: use flags and canaries to limit blast radius; best for new features.
• Regional Failover with Active-Standby: route traffic to standby region when primary region shows partial failures; best for regional disasters.
• Shard-aware Circuit Breakers: per-shard circuit breakers prevent cascade and limit failures to shards; best for databases and cache layers.
• Service Mesh Traffic Shaping: leverage mesh to route away from unhealthy pods or versions; best for microservices with high cardinality routing.
• Edge-level Request Filtering: apply WAF or edge rules to block malformed traffic that causes subset failures; best for security-triggered incidents.

Failure modes & mitigation (TABLE REQUIRED)

ID	Failure mode	Symptom	Likely cause	Mitigation	Observability signal
F1	Shard failure	Errors for subset keys	Hardware or DB index issue	Isolate shard and failover	Replica lag and error spike
F2	Canary regression	New version fails for subset	Bug in feature code path	Rollback canary or disable flag	Canary error rate high
F3	Edge POP failure	Regional TLS errors	CDN POP config error	Reroute to healthy POPs	Edge 5xxs and RUM drops
F4	Mesh sidecar crash	Pod subset fails requests	Sidecar misconfiguration	Restart sidecars or roll back	Pod restarts and traces
F5	Rate-limited vendor	4xx from dependency for some IDs	Vendor throttling per merchant	Throttle back or switch vendor	Upstream error codes
F6	Autoscaler misconfig	Pod starvation under pattern	Wrong metrics for scale	Adjust autoscaling policy	CPU queue length and OOMs
F7	Security rule false positive	Legit traffic blocked	Overaggressive WAF rule	Patch or scope rule	Block counts and client IDs
F8	Network micro-partition	Inter-AZ timeouts	Routing table or SDN bug	Reconfigure routes or failover	Packet loss and TCP retries

Row Details (only if needed)

• None.

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Key Concepts, Keywords & Terminology for Partial outage

Glossary (40+ terms). Each term line: term — definition — why it matters — common pitfall

• Availability — Measure of uptime for a service — Core of outage analysis — Confusing availability with latency
• Partial outage — Scoped availability failure — Primary topic — Misclassified as full outage
• SLI — Service Level Indicator metric — Basis for SLOs — Choosing wrong metric
• SLO — Service Level Objective target — Guides reliability effort — Setting unrealistic targets
• Error budget — Allowable errors before action — Enables paced engineering — Misusing to ignore issues
• Canary deployment — Small scale release for testing — Limits blast radius — Not representative sample
• Feature flag — Toggle to change behavior at runtime — Quick mitigation tool — Flag sprawl and complexity
• Circuit breaker — Prevents cascading failures — Protects dependencies — Incorrect thresholds cause blocking
• Rate limiting — Controls request rates — Prevents overload — Overly strict limits affect users
• Sharding — Data partitioning by key — Limits impact to a shard — Uneven shard distribution
• Replica lag — Delay in replicas catching up — Risk to consistency — Blind spots in monitoring
• Regional failover — Redirect traffic between regions — Resilience for outages — Data sovereignty issues
• Active-active — Multiple regions serve traffic simultaneously — Improves availability — Consistency challenges
• Active-passive — One region serves traffic, others standby — Simpler consistency — Longer failover time
• Observability — Telemetry that reveals system state — Essential to detect partial outages — High-cardinality costs
• High cardinality — Many dimensions in metrics/traces — Enables slicing by user/region — Storage and cost implications
• RUM — Real User Monitoring — Client side performance insights — Privacy and sampling constraints
• APM — Application Performance Monitoring — Deep tracing of requests — Instrumentation overhead
• Log aggregation — Centralized logs for analysis — Debugging incidents — Log blowup and retention costs
• Metrics — Numeric system measures — Trend detection — Metric resolution vs alert noise
• Tracing — Distributed request flow tracking — Root cause analysis — Sampling may drop critical traces
• Error budget policy — Rules for handling error budgets — Operational discipline — Ignoring enforcement
• Incident response — Process to manage incidents — Lowers MTTR — Poor role definition causes confusion
• Runbook — Step-by-step remediation guidance — Guides responders — Stale runbooks are harmful
• Playbook — Higher-level incident actions — Situational flexibility — Overly generic playbooks fail to help
• Chaos engineering — Fault injection testing — Validates resilience — Unsafe experiments in prod
• Auto-heal — Automated corrective actions — Rapid recovery — Bad automation can worsen outage
• Service mesh — Layer for service-to-service routing — Fine-grained control — Complexity and sidecar overhead
• Edge POP — CDN point of presence — Affects regional users — POP misconfig causes broad impact
• SDN — Software-defined networking — Dynamic routing control — Misconfig risks partitioning
• Throttling — Intentional slowdown for fairness — Protects system — Poorly tuned throttles block critical traffic
• Graceful degradation — Reduced functionality mode — Keeps system partially usable — Hard to design fallback UX
• Compensation logic — Business-level undo measures — Maintains invariants — Complex to implement
• Blue-green deploy — Deployment pattern with two environments — Fast rollback — Costly in infra duplication
• Rollback — Reverting to known good state — Quick mitigation — Data migrations complicate rollback
• Postmortem — Incident analysis document — Drives long-term improvement — Blameful culture prevents truth
• MTTR — Mean time to repair — Operational health indicator — Focusing only on MTTR misses prevention
• SLA — Service Level Agreement contractual commitment — Customer expectations — Legal and financial impact
• Synthetic monitoring — Simulated user checks — Early detection — Failing to align with real user paths
• Health check — Endpoint for readiness or liveness — Orchestrator uses it — Fragile or too lax checks
• Blast radius — Magnitude of impact from change — Drives design decisions — Poorly quantified

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How to Measure Partial outage (Metrics, SLIs, SLOs) (TABLE REQUIRED)

ID	Metric/SLI	What it tells you	How to measure	Starting target	Gotchas
M1	Per-region availability	Region-specific uptime	Successful requests divided by total per region	99.9% per critical region	Aggregation hides region failures
M2	Feature-flag success rate	Feature-specific health	Success rate for flagged users	99.5% for new flags	Low sample size for canaries
M3	Shard error rate	Errors scoped to data shard	Errors per shard key group	99.9% success per shard	Hot keys skew results
M4	Per-customer latency	Latency for high-value customers	95th percentile per customer	200ms p95 for premium	Cardinality explosion
M5	Dependency error proportion	Fraction of errors due to upstreams	Count of upstream errors / total	<5% of total errors	Mapping errors to vendor sometimes hard
M6	Edge POP error rate	POP-specific request failures	Edge 5xx per POP	99.7% per POP	POP naming and discovery complexity
M7	Canary error ratio	New version vs baseline errors	Error ratio new version divided by baseline	<1.5x baseline	Baseline drift during peak traffic
M8	Pod restart rate	Stability of subset pods	Restarts per pod per hour	<0.01 restarts/hr	Some restarts are normal due to updates
M9	Replica lag ms	Data consistency exposure	Seconds lag on replicas	<200ms for critical data	Asymmetric replication patterns
M10	Circuit breaker trips	Dependency health signal	Count of CB opens per time	Minimal allowed per policy	Excessive CBs mask real issues

Row Details (only if needed)

• None.

Best tools to measure Partial outage

List of 6 popular types: observability platforms, APM, RUM, CDN/edge monitoring, service mesh telemetry, DB monitoring.

Tool — Observability platform (example: modern metrics/tracing platform)

• What it measures for Partial outage: Metrics, traces, logs and high-cardinality slices.
• Best-fit environment: Cloud-native microservices and multi-region deployments.
• Setup outline:
• Instrument services with metrics and distributed tracing.
• Tag spans and metrics with region, shard, feature flag.
• Configure high-cardinality indexing and sampling rules.
• Build dashboards per-slice.
• Integrate with alerting and incident system.
• Strengths:
• Centralized correlated telemetry.
• Powerful slicing by dimensions.
• Limitations:
• Storage cost at high cardinality.
• Query performance vs volume tradeoffs.

Tool — APM

• What it measures for Partial outage: End-to-end traces and transaction errors for affected paths.
• Best-fit environment: Complex distributed transactions and backend services.
• Setup outline:
• Instrument key transactions.
• Enable distributed context propagation.
• Tag by customer ID and feature flag.
• Strengths:
• Fast root cause pinpointing.
• Transaction-level visibility.
• Limitations:
• Sampling may drop low-volume failures.
• Agent overhead on hosts.

Tool — RUM / Client telemetry

• What it measures for Partial outage: Client-side errors, performance, and regional user impact.
• Best-fit environment: Web and mobile frontends.
• Setup outline:
• Integrate lightweight SDK in client.
• Capture errors, timings, geo, and user ID if allowed.
• Configure sampling and privacy handling.
• Strengths:
• Real user experience visibility.
• Detects client-specific partial outages.
• Limitations:
• Data privacy and sampling.
• Ad blockers can reduce signal.

Tool — CDN / Edge monitoring

• What it measures for Partial outage: POP-specific errors and routing issues.
• Best-fit environment: High traffic CDNs and global edge services.
• Setup outline:
• Enable per-POP logging and synthetic checks.
• Track TLS negotiation and origin health per POP.
• Strengths:
• Early detection of POP-specific failures.
• Controls at edge for mitigation.
• Limitations:
• Limited trace propagation past edge.
• Depends on CDN feature set.

Tool — Service mesh telemetry

• What it measures for Partial outage: Per-pod and per-route metrics and circuit breaker state.
• Best-fit environment: Kubernetes microservices.
• Setup outline:
• Install mesh sidecars.
• Enable telemetry and mTLS if needed.
• Create routing rules for canary isolation.
• Strengths:
• Fine-grained control and telemetry.
• Dynamic traffic management.
• Limitations:
• Sidecar overhead and operational complexity.
• Mesh upgrades can cause perturbations.

Tool — Database monitoring

• What it measures for Partial outage: Replica lag, errors, slow queries per shard.
• Best-fit environment: Sharded, replicated data stores.
• Setup outline:
• Instrument replication metrics.
• Track per-shard query latency and error rates.
• Strengths:
• Direct insight into data layer health.
• Can trigger targeted failover.
• Limitations:
• Some DBs lack per-shard granularity.
• Monitoring agents may add load.

Recommended dashboards & alerts for Partial outage

Executive dashboard:

• Overall global availability: top-line percentage and trend.
• Regions with notable deviation: per-region availability sparkline.
• Business impact: percentage of revenue affected.
• Error budget consumption: scoped and global.
• High-level remediation status: open incidents and mitigations.

On-call dashboard:

• Active alerts and affected slice details.
• Per-region and per-feature SLI panels.
• Recent deploys and canary status.
• Dependency error counts and circuit breaker state.
• Runbook link and recent incidents.

Debug dashboard:

• Raw traces for affected requests.
• Per-pod logs with tail capability.
• DB shard metrics and replica lag.
• Network path metrics and edge POP logs.
• Feature flag membership and rollout percentages.

Alerting guidance:

• Page vs ticket: page when high-severity partial outage affects multiple high-value customers or core payments; create ticket for minor feature regressions affecting low-value slice.
• Burn-rate guidance: alert on sustained burn above planned error budget pace; for partial SLIs, consider proportional burn-rate thresholds.
• Noise reduction tactics:
• Dedupe alerts by grouping identical error signatures.
• Use alert suppression windows during planned rollouts.
• Aggregate low-volume errors into periodic tickets rather than paging.

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Implementation Guide (Step-by-step)

1) Prerequisites – Ownership defined for services and dependencies. – Instrumentation libraries and telemetry export configured. – Feature flagging system available. – CI/CD with canary capability. – Incident communication channels and runbook templates.

2) Instrumentation plan – Tag all telemetry with region, AZ, customer ID, feature flag, and deployment version. – Add health checks per shard and per feature. – Ensure traces propagate context across services.

3) Data collection – Ingest metrics at high cardinality only where necessary. – Sample traces but keep full traces for high-value paths. – Use RUM to capture client-side errors.

4) SLO design – Define SLIs per region, feature, or customer tier. – Set SLOs based on business impact and historical data. – Create error budget policies for scoped SLOs.

5) Dashboards – Build executive, on-call, and debug dashboards as above. – Include links to runbooks and recent deploy metadata.

6) Alerts & routing – Create scoped alerts for per-region or per-feature SLO breaches. – Route alerts to owning teams and escalation paths. – Integrate with incident management and paging tools.

7) Runbooks & automation – Create runbooks for common partial outage patterns: shard failover, feature rollback, POP reroute. – Automate mitigation steps where safe: disable feature flag, shift traffic, scale nodes.

8) Validation (load/chaos/game days) – Run targeted chaos experiments on shards, POPs, and node pools. – Validate runbook steps and automated responses in pre-production. – Conduct game days with on-call teams.

9) Continuous improvement – Postmortems with action items and SLO adjustments. – Track runbook effectiveness and update after incidents. – Invest in automation to reduce manual steps.

Checklists

Pre-production checklist:

• Telemetry tags implemented and validated.
• Canary and rollback paths tested.
• Synthetic checks for critical slices in place.
• Runbook for partial outage scenarios available.

Production readiness checklist:

• Scoped SLIs and SLOs defined and monitored.
• Alerts routed and tested to on-call.
• Feature flags can be toggled safely in prod.
• Failover and reroute automation configured.

Incident checklist specific to Partial outage:

• Identify affected slice: region, shard, feature, customer.
• Correlate deploys and recent config changes.
• Execute runbook: toggle feature flag or reroute.
• Notify stakeholders and open incident.
• Validate mitigation via metrics and RUM.
• Postmortem and action items.

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Use Cases of Partial outage

Provide 8–12 concise use cases.

1) Global SaaS with multi-tenant DB shards – Context: Multi-tenant database with per-tenant shard mapping. – Problem: One shard experiences IO errors. – Why Partial outage helps: Isolate tenant impact and failover shard. – What to measure: Per-shard error rate and replica lag. – Typical tools: DB monitoring, feature flag, tenant router.

2) Phased feature rollout for mobile clients – Context: Major UI feature rolled to 10% users. – Problem: Mobile clients crash for subset of OS versions. – Why Partial outage helps: Limit blast radius and revert for affected users. – What to measure: Crash rate by OS and feature flag segment. – Typical tools: Crash reporting, feature flags, APM.

3) CDN edge misconfiguration – Context: CDN POP misrouting requests to wrong origin. – Problem: Regional users receive errors. – Why Partial outage helps: Detect by per-POP telemetry and reroute. – What to measure: Edge errors per POP, TLS handshake failure. – Typical tools: CDN logs, synthetic monitoring, edge control plane.

4) Vendor API rate limits for payment gateway – Context: Payment vendor throttles merchant accounts. – Problem: Payments fail for certain merchants. – Why Partial outage helps: Detect and route to backup vendor for those IDs. – What to measure: Upstream 4xx per merchant and success ratio. – Typical tools: API gateway, vendor monitoring, circuit breaker.

5) Kubernetes node pool AMI bug – Context: New AMI causes kubelet crash on certain instance types. – Problem: Node pool loses subset pods. – Why Partial outage helps: Evacuate nodes and shift traffic to healthy node pools. – What to measure: Node Ready status, pod restarts. – Typical tools: K8s monitoring, deployment automation.

6) Serverless cold-start region issue – Context: One cloud region exhibits high cold-start latency. – Problem: Lambda functions slow for specific region. – Why Partial outage helps: Route traffic to warm regional replicas. – What to measure: Invocation latency and error rates per region. – Typical tools: Serverless metrics, edge routing.

7) CI/CD rollout causing regression – Context: Gradual deployment to 20% of users. – Problem: New code causes downstream errors under specific query pattern. – Why Partial outage helps: Stop rollout and revert for affected bucket. – What to measure: Canary vs baseline error rate. – Typical tools: CI/CD canary, APM.

8) WAF rule misfire blocking corporate clients – Context: New WAF rule intended to stop bots. – Problem: Rule blocks customers from specific IP ranges. – Why Partial outage helps: Disable or scope rule to reduce impact. – What to measure: WAF block counts per IP range and client signature. – Typical tools: WAF dashboard, SIEM.

9) Multi-region cache inconsistency – Context: CDN or cache invalidation only partially propagated. – Problem: Some regions see stale or inconsistent responses. – Why Partial outage helps: Fallback to origin for affected regions. – What to measure: Cache hit ratio by region and error rates. – Typical tools: Cache metrics, synthetic checks.

10) Internal API breaking subset of services – Context: Internal API contract changed without versioning. – Problem: Only services using new path fail. – Why Partial outage helps: Re-introduce old contract or route affected services to fallback. – What to measure: 4xx/5xx by client service and API version used. – Typical tools: API gateway, service mesh traces.

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Scenario Examples (Realistic, End-to-End)

Provide 4–6 scenarios with required inclusion.

Scenario #1 — Kubernetes node pool AMI regression (Kubernetes)

Context: A new node image is deployed to a Kubernetes node pool in one region.
Goal: Restore service for workloads affected by node crashes without impacting unaffected regions.
Why Partial outage matters here: Only workloads in that node pool are impacted; preserving other regions maintains availability.
Architecture / workflow: Ingress -> regional clusters -> node pools with nodes running problematic AMI -> pods scheduled to those nodes -> monitoring collects pod restarts and node readiness.
Step-by-step implementation:

1. Alert triggers on increased pod restarts for the node pool.
2. On-call inspects node readiness and AMI version tag.
3. Drain affected nodes using cordon and drain.
4. Scale up healthy node pool or spin up instances with previous AMI.
5. Rollback node image in infrastructure pipeline.
6. Validate via per-cluster SLI and pod restart metrics.
   What to measure: Node Ready percentage, pod restart rate, per-cluster availability.
   Tools to use and why: Kubernetes monitoring for node/pod metrics, infra automation for AMI rollbacks, cluster autoscaler.
   Common pitfalls: Assuming node issue is due to app container rather than AMI; forgetting to re-enable autoscaler.
   Validation: Run synthetic traffic to cluster region and verify error rates normalized.
   Outcome: Affected pod scheduling moves to healthy nodes; incident resolved with minimal customer impact.

Scenario #2 — Serverless cold-start regional degradation (serverless/managed-PaaS)

Context: A managed serverless function experiences elevated cold start latency in region B during peak hours.
Goal: Reduce latency for affected users and maintain throughput while root cause is identified.
Why Partial outage matters here: Only region B users see degraded performance; global service remains available.
Architecture / workflow: Edge routing -> region-aware routing rules -> serverless functions in multiple regions -> telemetry: invocation latency, error counts.
Step-by-step implementation:

1. Detect spike in invocation latency for region B via RUM and serverless metrics.
2. Shift traffic from region B to region A for new sessions using edge routing rules.
3. Spin up warmers or provision concurrency in region B for critical functions.
4. Investigate provider logs and resource limits for region B.
5. Apply long-term fix or request provider support.
6. Ramp traffic back gradually once validated.
   What to measure: p95 invocation latency per region, error rate, warm concurrency count.
   Tools to use and why: Edge routing controls, serverless provider metrics, RUM for real user effect.
   Common pitfalls: Moving traffic without considering data locality leading to consistency problems.
   Validation: Compare error and latency metrics after partial reroute.
   Outcome: Region B load reduced; user experience maintained while root cause addressed.

Scenario #3 — Payment vendor throttling affecting subset merchants (incident-response/postmortem)

Context: A vendor starts returning 429s for certain merchant IDs during a peak sale.
Goal: Keep merchant transactions flowing for critical accounts and remediate vendor impact.
Why Partial outage matters here: Only specific merchants affected; broad outage avoided.
Architecture / workflow: Payment request -> routing with merchant ID -> payment gateway -> vendor API; telemetry includes upstream response codes per merchant.
Step-by-step implementation:

1. Alert on increased 4xx errors for payment transactions; identify merchant IDs.
2. Engage incident commander and route critical merchant traffic to an alternative vendor or retry policy.
3. Apply graceful degradation for noncritical merchants with retry/backoff.
4. Open vendor support ticket and share error traces.
5. Postmortem: analyze why merchant-specific throttling happened and add vendor isolation patterns.
   What to measure: Payment success rate per merchant, vendor error codes, retry success.
   Tools to use and why: API gateway with per-merchant metrics, payment routing rules, incident tracking.
   Common pitfalls: Global retry loops causing vendor overload; failing to prioritize VIP merchants.
   Validation: Confirm backup vendor handles critical transactions and metrics return to baseline.
   Outcome: Critical merchants processed; vendor mitigated; long-term vendor routing added.

Scenario #4 — Cost-driven cache eviction causing users to see stale content (cost/performance trade-off)

Context: To reduce costs, cache TTLs were shortened for a region, but some high-value traffic experienced cache misses and higher latency.
Goal: Balance cost and performance for premium customers by selectively increasing TTLs.
Why Partial outage matters here: Only certain user segments experienced degraded performance due to cache policy change.
Architecture / workflow: Edge caching -> cache rules by path and user tier -> origin servers -> metrics show cache hit ratio per user tier.
Step-by-step implementation:

1. Detect increased origin latency correlated with specific user tier.
2. Update CDN rules to extend TTL for premium user paths using header-based rules.
3. Monitor hit ratio and origin load.
4. Implement cost allocation tracking for cache rules to show ROI.
5. Consider tiered caching strategy or regional cache sizing adjustments.
   What to measure: Cache hit ratio per user tier, origin latency, cost per GB transferred.
   Tools to use and why: CDN config and analytics, RUM, cost monitoring.
   Common pitfalls: Applying global TTL changes; forgetting to test edge invalidation behaviors.
   Validation: Premium user latency returns to SLA while cost delta analyzed.
   Outcome: Premium users restored to expected performance with controlled cost increase.

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Common Mistakes, Anti-patterns, and Troubleshooting

List of 20 mistakes with symptom -> root cause -> fix. Include observability pitfalls.

1) Symptom: Global alert but only one region has errors -> Root cause: Aggregated metric hides slice -> Fix: Implement per-region SLIs and dashboards.
2) Symptom: Repeated partial outages after deploys -> Root cause: Missing canary testing -> Fix: Enforce canary gating and automated rollback.
3) Symptom: On-call blames CDN for all issues -> Root cause: Lack of end-to-end traces -> Fix: Correlate edge logs with backend traces.
4) Symptom: High latency only for premium customers -> Root cause: Tiered routing misconfiguration -> Fix: Validate routing rules and telemetry by customer tier.
5) Symptom: Low sample traces for failing requests -> Root cause: Tracing sampling drops rare errors -> Fix: Implement error-based trace retention.
6) Symptom: Alerts flood during partial outage -> Root cause: Alert per-instance not aggregated -> Fix: Group alerts and use dedupe rules.
7) Symptom: Runbook steps fail or outdated -> Root cause: Stale documentation -> Fix: Review and test runbooks periodically.
8) Symptom: Automatic rollback re-applies bad config -> Root cause: CI/CD misconfiguration -> Fix: Add artifact immutability and rollback checks.
9) Symptom: False positives in WAF cause blocks -> Root cause: Overly broad rules -> Fix: Scope WAF rules and add exclusions.
10) Symptom: Dependency errors not traced to vendor -> Root cause: Missing upstream tagging -> Fix: Tag upstream calls and log vendor IDs.
11) Symptom: Partial outage persists unnoticed -> Root cause: No RUM or client telemetry -> Fix: Add RUM and align synthetic checks to real paths.
12) Symptom: Performance degrades under specific key patterns -> Root cause: Hot key on shard -> Fix: Repartition or introduce caching for hot key.
13) Symptom: Circuit breakers trip too often -> Root cause: Tight thresholds or noisy metrics -> Fix: Tune CB thresholds and hysteresis.
14) Symptom: Pager fatigue from low-impact slices -> Root cause: Overly aggressive paging rules -> Fix: Set tiered paging and ticketing for low-impact slices.
15) Symptom: Metrics explode in cardinality -> Root cause: Tagging everything without plan -> Fix: Define cardinality policy and apply rollup metrics.
16) Symptom: Postmortem lacks action items -> Root cause: Blameful culture or poor facilitation -> Fix: Use blameless postmortems with clear owners.
17) Symptom: Automation escalates outages -> Root cause: Unsafe auto-heal scripts -> Fix: Add safety checks and canary for automation.
18) Symptom: Partial outage due to data migration -> Root cause: Migration not backward compatible -> Fix: Use backward-compatible migrations and feature gates.
19) Symptom: Observability gaps for low-volume customers -> Root cause: Sampling discards minority traffic -> Fix: Implement retention for flagged customer traces.
20) Symptom: Cost spike after per-slice telemetry -> Root cause: Uncontrolled high-cardinality metrics -> Fix: Apply cardinality limits and targeted indexing.

Observability-specific pitfalls (at least 5 included above):

• Dropped traces due to sampling.
• Cardinality explosion from unbounded tags.
• Aggregated metrics hiding slices.
• Lack of client-side telemetry causing blind spot.
• Metrics retention causing loss of historical slice context.

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Best Practices & Operating Model

Ownership and on-call:

• Define clear ownership for services and dependent components.
• Map ownership to customer tiers and regions.
• On-call rotation should include subject matter experts for regional and feature slices.

Runbooks vs playbooks:

• Runbooks: prescriptive steps for frequent, deterministic mitigations.
• Playbooks: higher-level strategies for ambiguous or complex incidents.
• Keep both versioned and easily accessible.

Safe deployments:

• Canary releases with automated verification gates.
• Blue-green for stateful changes where rollback is expensive.
• Feature flags for business logic and dark launches.

Toil reduction and automation:

• Automate common mitigation tasks: flag toggles, traffic shifts, shard failover.
• Record automation outcomes and ensure revert options.
• Measure toil reduction and adjust accordingly.

Security basics:

• Ensure observability pipelines encrypt and redact PII.
• Limit feature flag control to authorized engineers.
• Harden edge controls and guardrails to prevent accidental global blocks.

Weekly/monthly routines:

• Weekly: review per-slice SLI trends and failed alerts.
• Monthly: validate runbooks with tabletop exercises.
• Quarterly: game day focusing on partial outage scenarios and automation stress tests.

Postmortem review items specific to Partial outage:

• Affected slice identification latency.
• Accuracy of SLI segmentation.
• Effectiveness of runbook and automation.
• Action items to reduce similar future partial outages.

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Tooling & Integration Map for Partial outage (TABLE REQUIRED)

ID	Category	What it does	Key integrations	Notes
I1	Observability platform	Centralize metrics logs traces	APM CDN mesh DB	Use for high-cardinality slices
I2	APM	End-to-end traces and traces sampling	App frameworks DB	Helps with transaction tracing
I3	RUM	Client-side performance telemetry	CDN frontend	Detects client-specific partial outages
I4	CDN / Edge	Global routing and POP controls	Edge logs origin	Useful for per-POP mitigation
I5	Service mesh	Per-route traffic control	K8s metrics APM	Enables fine-grained routing
I6	Feature flagging	Toggle features for segments	CI/CD APM logs	Rapid mitigation for feature regressions
I7	CI/CD	Canary and rollback automation	VCS observability	Gate deployments by canary SLIs
I8	DB monitoring	Shard and replica observability	Orchestration APM	Key for data-layer partial outages
I9	Incident mgmt	Pager routing and timeline	Chat ops observability	Ties alerts to responders
I10	WAF / Sec	Block malicious traffic at edge	CDN SIEM	Can cause partial outages if misconfigured

Row Details (only if needed)

• None.

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Frequently Asked Questions (FAQs)

What exactly counts as a partial outage?

A partial outage is a scoped failure affecting a subset of the service surface such as region, feature, shard, or customer segment while other parts remain functional.

How is it different from degradation?

Degradation often refers to performance loss across a broader surface. Partial outage implies availability or correctness loss in a subset, though degradation can be partial too.

How should SLIs be structured for partial outages?

Use scoped SLIs by region, customer tier, or feature flag. Complement global SLIs with targeted ones for critical slices.

When should I page engineers for a partial outage?

Page when the partial outage impacts high-value customers, core revenue paths, or critical infrastructure components. Otherwise use a ticketing escalation.

How to avoid too many alerts from partial slices?

Group alerts, reduce cardinality in alert rules, and use aggregation thresholds. Prioritize alerts by business impact.

Does feature flagging cause complexity?

Yes; feature flags are powerful mitigation tools but cause complexity if overused. Track flags and have flag governance.

Can automation make partial outages worse?

Yes; unsafe automation or faulty auto-heal scripts can worsen situations. Implement safety checks and canary for automation.

How do I measure customer impact during a partial outage?

Use per-customer SLIs, RUM, and revenue attribution to estimate affected revenue and user sessions.

What telemetry is most important?

High-cardinality metrics for region, customer ID, and feature; traces for failing transactions; logs for contextual debugging.

How to test partial outage scenarios?

Run targeted chaos experiments, game days, and rehearsed runbook drills in staging and safe production windows.

How many SLOs should we have?

Varies / depends. Start with global and a few critical per-slice SLOs for regions, features, and premium customers, then expand based on risk and capacity.

Who owns partial outage mitigation?

The service owning the affected slice owns mitigation, supported by platform and infra teams for underlying failure domains.

Are partial outages covered by SLAs?

They can be if SLAs are scoped; often SLAs are global, so check contract wording. Not publicly stated if specific SLA terms apply.

How to handle third-party-induced partial outages?

Implement circuit breakers, fallback vendors, and per-vendor SLIs. Route critical customers to backup vendors as needed.

How to prevent shard hot keys causing partial outage?

Monitor key distributions, add cache layers, and redesign partitions to spread load or hot-key handling.

Is it expensive to monitor at high cardinality?

Yes; high-cardinality telemetry increases cost. Use targeted indexing, sampling, and rollups to control costs.

How to perform postmortem for partial outage?

Document timeline, affected slice, detection latency, mitigations applied, runbook effectiveness, and actionable remediation items.

Can partial outages be fully automated away?

Varies / depends. Some mitigations are automatable, but others require human judgment, especially complex multi-dependency failures.

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Conclusion

Partial outages are a common and impactful class of incidents in cloud-native systems. They demand scoped SLIs, high-cardinality telemetry, targeted runbooks, and automation where safe. Prioritize identifying affected slices quickly and isolating the failure to preserve availability for unaffected users.

Next 7 days plan (5 bullets):

• Day 1: Inventory current SLIs and tag telemetry by region, shard, and feature.
• Day 2: Implement or validate feature flagging and canary controls for critical services.
• Day 3: Build per-slice dashboards for executive and on-call views.
• Day 4: Create runbooks for top 3 partial outage failure modes and test them.
• Day 5–7: Run a targeted game day focusing on shard and region failure scenarios and update postmortem actions.

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Appendix — Partial outage Keyword Cluster (SEO)

Primary keywords

• Partial outage
• Partial service outage
• Scoped outage
• Regional outage
• Shard outage
• Partial degradation
• Partial availability failure
• Partial downtime

Secondary keywords

• Partial outage detection
• Partial outage mitigation
• Partial outage monitoring
• Per-region SLI
• Per-feature SLO
• High-cardinality telemetry
• Feature flag rollback
• Canary deployment failures

Long-tail questions

• What is a partial outage in cloud computing
• How to detect partial outage in Kubernetes
• How to measure partial outage for SaaS platforms
• How to create runbooks for partial outage scenarios
• Partial outage vs full outage difference explained
• How to route traffic during a partial outage
• How to implement per-customer SLIs for partial outages
• How to automate partial outage mitigation with feature flags
• Best practices for partial outage incident response
• How to use RUM to identify partial outages
• How to reduce blast radius of deployments causing partial outages
• How to test partial outage scenarios in production safely
• How to set SLOs for regional partial outages
• How to handle vendor-induced partial outages
• How to tune circuit breakers to prevent partial outages
• How to debug shard failures causing partial outage
• How to detect edge POP partial outages quickly
• How to design dashboards for partial outage detection
• How to prioritize paging for partial outages
• How to avoid alert fatigue from partial slice alerts

Related terminology

• SLI and SLO
• Error budget
• Canary rollout
• Feature flagging
• Service mesh
• Replica lag
• Circuit breaker
• RUM and APM
• Observability pipeline
• Synthetic monitoring
• Chaos engineering
• Auto-heal automation
• WAF rule tuning
• CDN POP monitoring
• Shard-aware architecture
• High cardinality metrics
• Per-tenant monitoring
• Blue-green deployment
• Rollback strategy
• Postmortem
• Incident command system
• On-call rotation
• Runbook
• Playbook
• Blast radius
• Hot key mitigation
• Tiered caching
• Edge routing
• Multi-region failover
• Vendor fallback routing
• Scoped alerts
• Dedupe alerting
• Game day testing
• Tracing sampling policies
• Metrics retention policy
• Cost-aware telemetry
• Security redaction
• Data partitioning
• Backward-compatible migration
• Graceful degradation