Observability & Reliability Engineering

Observability & Reliability Engineering

Complete system for building observable, reliable services — from structured logging to incident response to SLO-driven development.

Quick Health Check (/16)

Score your current observability posture: SignalHealthy (2)Weak (1)Missing (0)Structured loggingJSON logs with trace_id correlationLogs exist but unstructuredConsole.log / print statementsMetrics collectionRED/USE metrics with dashboardsSome metrics, no dashboardsNo metricsDistributed tracingFull request path with samplingPartial traces, key services onlyNo tracingAlertingSLO-based alerts with runbooksThreshold alerts, some runbooksNo alerts or all-noiseIncident responseDefined process with roles + post-mortemsAd-hoc response, some docs"Whoever notices fixes it"SLOs definedSLOs with error budgets tracked weeklyInformal availability targetsNo reliability targetsOn-call rotationStructured rotation with escalationInformal "call someone"No on-callCost managementObservability budget tracked monthlySome awareness of costsNo idea what you spend 12-16: Production-grade. Focus on optimization. 8-11: Foundation exists. Fill the gaps systematically. 4-7: Significant risk. Prioritize alerting + incident response. 0-3: Flying blind. Start with Phase 1 immediately.

Log Architecture

Application → Structured JSON → Log Router → Storage → Query Engine ↓ Alert Pipeline

Required Fields (Every Log Line)

FieldTypePurposeExampletimestampISO-8601 UTCWhen2026-02-22T18:30:00.123ZlevelenumSeverityinfo, warn, error, fatalservicestringWhich servicepayment-apiversionstringWhich deployv2.3.1environmentstringWhich envproductionmessagestringWhat happenedPayment processed successfullytrace_idstringRequest correlationabc123def456span_idstringOperation within tracespan_789duration_msnumberHow long142

Contextual Fields (Add Per Domain)

# HTTP request context http: method: POST path: /api/v1/orders status: 201 client_ip: 203.0.113.42 # Anonymize in logs if needed user_agent: "Mozilla/5.0..." request_id: "req_abc123" # Business context business: user_id: "usr_456" tenant_id: "tenant_789" order_id: "ord_012" action: "checkout" amount_cents: 4999 currency: "USD" # Error context error: type: "PaymentDeclinedError" message: "Card declined: insufficient funds" code: "CARD_DECLINED" stack: "..." # Only in non-production or DEBUG level retry_count: 2 retryable: true

Log Level Decision Tree

Is the process about to crash? → FATAL (exit after logging) Did an operation fail that needs human attention? → ERROR (page someone or create ticket) Did something unexpected happen but we recovered? → WARN (review in daily triage) Is this a normal business event worth recording? → INFO (audit trail, business metrics) Is this useful for debugging but noisy in production? → DEBUG (off in prod, on in staging) Is this only useful when stepping through code? → TRACE (never in production)

Log Level Rules

ERROR means action required — if no one needs to act on it, it's WARN INFO is for business events — not internal implementation details No logging inside tight loops — aggregate and log summary Log at boundaries — API entry/exit, queue consume/publish, DB calls Never log secrets — API keys, tokens, passwords, PII (see scrubbing below)

PII & Secret Scrubbing

scrub_patterns: # Always redact - field_patterns: ["password", "secret", "token", "api_key", "authorization"] action: replace_with_redacted # Hash for correlation without exposure - field_patterns: ["email", "phone", "ssn", "national_id"] action: sha256_hash # Mask partially - field_patterns: ["credit_card", "card_number"] action: mask_last_4 # "****-****-****-1234" # IP anonymization - field_patterns: ["client_ip", "ip_address"] action: zero_last_octet # 203.0.113.0

Logger Setup (By Language)

Node.js (Pino): import pino from 'pino'; import { AsyncLocalStorage } from 'node:async_hooks'; const als = new AsyncLocalStorage<Record<string, string>>(); const logger = pino({ level: process.env.LOG_LEVEL || 'info', formatters: { level: (label) => ({ level: label }), }, mixin: () => als.getStore() ?? {}, redact: ['req.headers.authorization', '*.password', '*.token'], timestamp: pino.stdTimeFunctions.isoTime, }); // Middleware: inject context app.use((req, res, next) => { const ctx = { trace_id: req.headers['x-trace-id'] || crypto.randomUUID(), request_id: crypto.randomUUID(), service: 'payment-api', version: process.env.APP_VERSION, }; als.run(ctx, () => next()); }); Python (structlog): import structlog structlog.configure( processors=[ structlog.contextvars.merge_contextvars, structlog.processors.add_log_level, structlog.processors.TimeStamper(fmt="iso", utc=True), structlog.processors.JSONRenderer(), ], ) log = structlog.get_logger() # Bind context per-request: structlog.contextvars.bind_contextvars(trace_id=trace_id, user_id=user_id) Go (zerolog): log := zerolog.New(os.Stdout).With(). Timestamp(). Str("service", "payment-api"). Str("version", version). Logger() // Per-request: reqLog := log.With().Str("trace_id", traceID).Logger()

Log Storage Decision

VolumeSolutionRetentionCost<10 GB/dayLoki + Grafana30 days hot, 90 days coldLow10-100 GB/dayElasticsearch / OpenSearch14 days hot, 90 days S3Medium100+ GB/dayClickHouse or Datadog7 days hot, 30 days archiveHighBudget-constrainedLoki + S3 backend90 days all coldVery low

10 Logging Anti-Patterns

#Anti-PatternFix1log.error(err) with no contextAlways include: what operation, what input, what state2Logging request/response bodiesLog only in DEBUG; redact sensitive fields3String concatenation in log messagesUse structured fields: log.info("processed", { order_id, amount })4Catch-and-log-and-rethrowLog at the boundary where you handle it, not every layer5Different log formats per serviceStandardize schema across all services6No log rotation / retention policySet max size + TTL; archive to cold storage7Logging inside hot pathsAggregate: log summary every N items or every interval8Missing correlation IDsPropagate trace_id from first entry point through all services9Boolean log levels (verbose: true)Use standard levels with configurable minimum10Logging PII in plain textImplement scrubbing at the logger level

The RED Method (Request-Driven Services)

For every service endpoint, track: MetricWhatPrometheus ExampleRateRequests per secondhttp_requests_total{method, path, status}ErrorsFailed requests per secondhttp_requests_total{status=~"5.."} / totalDurationLatency distributionhttp_request_duration_seconds{method, path} (histogram)

The USE Method (Infrastructure Resources)

For every resource (CPU, memory, disk, network): MetricWhatExampleUtilization% resource busyCPU usage 78%SaturationQueue depth / backpressure12 requests queuedErrorsResource errors3 disk I/O errors

Golden Signals (Google SRE)

SignalMeaningSourceLatencyTime to serve requestsRED DurationTrafficDemand on the systemRED RateErrorsRate of failed requestsRED ErrorsSaturationHow "full" the service isUSE Saturation

Metric Types & When to Use Each

TypeUse CaseExampleCounterThings that only go upTotal requests, errors, bytes sentGaugeCurrent value that goes up/downActive connections, queue depth, temperatureHistogramDistribution of valuesRequest latency, response sizeSummaryPre-calculated percentilesClient-side latency (when you need exact percentiles) Rule: Use histograms over summaries in most cases — they're aggregatable across instances.

Naming Conventions

# Pattern: <namespace>_<subsystem>_<name>_<unit> http_server_request_duration_seconds http_server_requests_total db_pool_connections_active queue_messages_pending cache_hit_ratio # Rules: # 1. Use snake_case # 2. Include unit suffix (_seconds, _bytes, _total) # 3. _total suffix for counters # 4. Don't include label names in metric name # 5. Use base units (seconds not milliseconds, bytes not kilobytes)

Label Design Rules

RuleWhyExampleKeep cardinality <100 per labelHigh cardinality kills performancestatus="200" not status="200 OK"No user IDs as labelsUnbounded cardinalityUse log correlation insteadNo request paths with IDs/api/users/123 creates millions of seriesNormalize: /api/users/:idMax 5-7 labels per metricEach combo = a time series{method, path, status, service}

Instrumentation Checklist

application_metrics: # HTTP layer - http_request_duration_seconds: histogram {method, path, status} - http_request_size_bytes: histogram {method, path} - http_response_size_bytes: histogram {method, path} - http_requests_in_flight: gauge # Business logic - orders_processed_total: counter {status, payment_method} - order_value_dollars: histogram {payment_method} - user_signups_total: counter {source} # Dependencies - db_query_duration_seconds: histogram {query_type, table} - db_connections_active: gauge {pool} - db_connections_idle: gauge {pool} - cache_requests_total: counter {result: hit|miss} - external_api_duration_seconds: histogram {service, endpoint} - external_api_errors_total: counter {service, error_type} # Queue / async - queue_messages_published_total: counter {queue} - queue_messages_consumed_total: counter {queue, status} - queue_processing_duration_seconds: histogram {queue} - queue_depth: gauge {queue} - queue_consumer_lag: gauge {queue, consumer_group} infrastructure_metrics: # Node exporter / cAdvisor provides these automatically - cpu_usage_percent: gauge {instance} - memory_usage_bytes: gauge {instance} - disk_usage_bytes: gauge {instance, mount} - disk_io_seconds: counter {instance, device} - network_bytes: counter {instance, direction} - container_cpu_usage: gauge {pod, container} - container_memory_usage: gauge {pod, container}

Stack Recommendations

ComponentOptionsRecommendationCollectionPrometheus, OTEL Collector, Datadog AgentPrometheus (free) or OTEL Collector (vendor-neutral)StoragePrometheus, Thanos, Mimir, VictoriaMetricsVictoriaMetrics (best cost/perf) or Mimir (Grafana ecosystem)VisualizationGrafana, Datadog, New RelicGrafana (free, extensible)AlertingAlertmanager, Grafana Alerting, PagerDutyAlertmanager + PagerDuty routing

Trace Architecture

Client Request → API Gateway (root span) → Auth Service (child span) → Order Service (child span) → Database Query (child span) → Payment Service (child span) → Stripe API (child span) → Notification Service (child span) → Email Provider (child span)

OpenTelemetry Setup

Auto-instrumentation (Node.js): // tracing.ts — import BEFORE anything else import { NodeSDK } from '@opentelemetry/sdk-node'; import { getNodeAutoInstrumentations } from '@opentelemetry/auto-instrumentations-node'; import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http'; const sdk = new NodeSDK({ traceExporter: new OTLPTraceExporter({ url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT || 'http://localhost:4318/v1/traces', }), instrumentations: [getNodeAutoInstrumentations({ '@opentelemetry/instrumentation-http': { ignoreIncomingPaths: ['/health', '/ready'] }, '@opentelemetry/instrumentation-express': { enabled: true }, })], serviceName: process.env.OTEL_SERVICE_NAME || 'payment-api', }); sdk.start(); Custom spans for business logic: import { trace, SpanStatusCode } from '@opentelemetry/api'; const tracer = trace.getTracer('payment-service'); async function processPayment(order: Order) { return tracer.startActiveSpan('process-payment', async (span) => { span.setAttributes({ 'order.id': order.id, 'order.amount_cents': order.amountCents, 'payment.method': order.paymentMethod, }); try { const result = await chargeCard(order); span.setAttributes({ 'payment.status': result.status }); return result; } catch (err) { span.setStatus({ code: SpanStatusCode.ERROR, message: err.message }); span.recordException(err); throw err; } finally { span.end(); } }); }

Sampling Strategies

StrategyWhenConfigAlways OnDev/staging, low traffic (<100 rps)ratio: 1.0ProbabilisticModerate traffic (100-1000 rps)ratio: 0.1 (10%)Rate-limitedHigh traffic (>1000 rps)max_traces_per_second: 100Tail-basedWant all errors + slow requestsCollector-side: keep if error OR duration > p99Parent-basedRespect upstream decisionsIf parent sampled, child sampled Recommendation: Start with parent-based + probabilistic (10%). Add tail-based at the collector to capture all errors.

Context Propagation

HeaderStandardFormattraceparentW3C Trace Context00-{trace_id}-{span_id}-{flags}tracestateW3C Trace ContextVendor-specific key-value pairsb3Zipkin B3{trace_id}-{span_id}-{sampled} Rule: Use W3C Trace Context (traceparent) as primary. Support B3 for legacy Zipkin systems.

Trace Storage

VolumeSolutionRetention<50 GB/dayJaeger + Elasticsearch7 days50-500 GB/dayTempo + S314 days500+ GB/dayTempo + S3 with aggressive sampling7 daysBudget-constrainedJaeger + Badger (local disk)3 days

SLI Selection by Service Type

Service TypePrimary SLISecondary SLIMeasurementAPI / WebAvailability + LatencyError rateServer-side + syntheticData pipelineFreshness + CorrectnessThroughputPipeline timestamps + checksumsStorageDurability + AvailabilityLatencyChecksums + uptime monitoringStreamingThroughput + LatencyMessage loss rateConsumer lag + e2e latencyBatch jobsSuccess rate + FreshnessDurationJob scheduler metrics

SLO Definition Template

slo: name: "Payment API Availability" service: payment-api owner: payments-team sli: type: availability definition: "Proportion of non-5xx responses" measurement: | sum(rate(http_requests_total{service="payment-api",status!~"5.."}[5m])) / sum(rate(http_requests_total{service="payment-api"}[5m])) target: 99.95% # 21.9 min downtime/month window: rolling_30d error_budget: total_minutes: 21.9 # per 30 days burn_rate_alerts: - severity: critical burn_rate: 14.4x # Budget consumed in 2 hours short_window: 5m long_window: 1h - severity: warning burn_rate: 6x # Budget consumed in 5 days short_window: 30m long_window: 6h - severity: ticket burn_rate: 1x # Budget consumed in 30 days short_window: 6h long_window: 3d consequences: budget_remaining_above_50pct: "Normal development velocity" budget_remaining_20_to_50pct: "Prioritize reliability work" budget_remaining_below_20pct: "Feature freeze; reliability only" budget_exhausted: "All hands on reliability until budget recovers"

Common SLO Targets

Service TierAvailabilityp50 Latencyp99 LatencyMonthly DowntimeTier 0 (payments, auth)99.99%<100ms<500ms4.3 minTier 1 (core API)99.95%<200ms<1s21.9 minTier 2 (non-critical)99.9%<500ms<2s43.8 minTier 3 (internal tools)99.5%<1s<5s3.6 hoursBatch / pipeline99% (success rate)N/AN/AN/A

Error Budget Tracking

# Weekly error budget review template error_budget_review: week: "2026-W08" service: payment-api slo_target: 99.95% budget: total_minutes_this_period: 21.9 consumed_minutes: 8.2 remaining_minutes: 13.7 remaining_percent: 62.6% incidents_consuming_budget: - date: "2026-02-18" duration_minutes: 5.1 cause: "Database connection pool exhaustion" preventable: true action: "Increase pool size + add saturation alert" - date: "2026-02-20" duration_minutes: 3.1 cause: "Upstream payment provider timeout" preventable: false action: "Add circuit breaker with fallback" velocity_decision: "Normal — 62.6% budget remaining" reliability_work_this_week: - "Add connection pool saturation alert" - "Implement circuit breaker for payment provider"

Alert Quality Principles

Every alert must be actionable — if no one needs to act, it's not an alert Every alert needs a runbook — linked directly in the alert annotation Symptom-based over cause-based — alert on "users can't checkout" not "CPU high" Multi-window burn rate — not static thresholds (see SLO alerts above) Alert on absence, not just presence — "no orders in 15 min" catches silent failures

Alert Severity Levels

SeverityResponse TimeChannelWhoExampleP0 — Critical<5 minPage (PagerDuty/Opsgenie)On-call engineerPayment system downP1 — High<30 minPage during business hours, Slack 24/7On-callError rate >5% for 10 minP2 — Medium<4 hoursSlack channelTeamp99 latency degraded 2xP3 — LowNext business dayTicket auto-createdTeam backlogDisk usage >80%InfoN/ADashboard onlyNo oneDeploy completed

Alerting Anti-Patterns

Anti-PatternProblemFixStatic CPU/memory thresholdsNoisy, not user-impactingUse SLO-based burn rate alertsAlert per instance50 instances = 50 alerts for same issueAggregate: alert on service-level error rateNo deduplicationSame alert fires 100 timesGroup by service + alert name; set repeat intervalMissing runbookEngineer gets paged, doesn't know what to doEvery alert links to a runbookThreshold too sensitiveFires on brief spikesUse for: 5m to require sustained conditionToo many P0sAlert fatigue → ignoring real incidentsAudit monthly; demote or remove noisy alerts

Alert Template (Prometheus Alertmanager)

groups: - name: payment-api-slo rules: - alert: PaymentAPIHighErrorRate expr: | ( sum(rate(http_requests_total{service="payment-api",status=~"5.."}[5m])) / sum(rate(http_requests_total{service="payment-api"}[5m])) ) > 0.01 for: 5m labels: severity: critical service: payment-api team: payments annotations: summary: "Payment API error rate {{ $value | humanizePercentage }} (>1%)" description: "5xx error rate has exceeded 1% for 5 minutes" runbook: "https://wiki.internal/runbooks/payment-api-errors" dashboard: "https://grafana.internal/d/payment-api" - alert: PaymentAPINoTraffic expr: | sum(rate(http_requests_total{service="payment-api"}[15m])) == 0 for: 5m labels: severity: critical service: payment-api annotations: summary: "Payment API receiving zero traffic for 5 minutes" runbook: "https://wiki.internal/runbooks/payment-api-no-traffic" - alert: PaymentAPILatencyHigh expr: | histogram_quantile(0.99, sum(rate(http_request_duration_seconds_bucket{service="payment-api"}[5m])) by (le) ) > 2 for: 10m labels: severity: warning annotations: summary: "Payment API p99 latency {{ $value }}s (>2s for 10min)" runbook: "https://wiki.internal/runbooks/payment-api-latency"

Runbook Template

• # Runbook: PaymentAPIHighErrorRate
• ## What This Alert Means
• The payment API is returning >1% 5xx errors over a 5-minute window.
• Users are likely failing to complete checkouts.
• ## Impact
• Users cannot process payments
• Revenue loss: ~$X per minute (based on average traffic)
• SLO: Payment API availability (target: 99.95%)
• ## Immediate Actions
• 1. Check the error dashboard: [link]
• 2. Check recent deploys: `kubectl rollout history deployment/payment-api`
• 3. Check upstream dependencies:
• - Database: [dashboard link]
• - Stripe API: [status page]
• - Redis cache: [dashboard link]
• 4. Check application logs:
• kubectl logs -l app=payment-api --since=10m | jq 'select(.level=="error")'
• ## Common Causes & Fixes
• | Cause | Diagnosis | Fix |
• |-------|-----------|-----|
• | Bad deploy | Errors started at deploy time | `kubectl rollout undo deployment/payment-api` |
• | DB connection exhaustion | `db_connections_active` at max | Restart pods (rolling) + increase pool size |
• | Stripe outage | Stripe status page red | Enable fallback payment processor |
• | Memory leak | Memory climbing, OOMKilled events | Rolling restart + investigate |
• ## Escalation
• If unresolved after 15 min: page payment team lead
• If revenue impact >$10K: page VP Engineering
• If Stripe outage: communicate to support team for customer messaging
• ## Resolution
• Confirm error rate <0.1% for 10 min
• Post in #incidents: root cause + duration + impact
• Schedule post-mortem if downtime >5 min

Dashboard Hierarchy

L1: Executive / Business Dashboard (non-technical stakeholders) ↓ L2: Service Overview Dashboard (on-call, quick triage) ↓ L3: Service Deep-Dive Dashboard (debugging specific service) ↓ L4: Infrastructure Dashboard (resource-level details)

L1: Business Dashboard

panels: - title: "Revenue per Minute" type: stat query: "sum(rate(orders_total{status='completed'}[5m])) * avg(order_value_dollars)" - title: "Active Users (5min)" type: stat query: "count(count by (user_id) (http_requests_total{...}[5m]))" - title: "Checkout Success Rate" type: gauge query: "sum(rate(checkout_total{status='success'}[1h])) / sum(rate(checkout_total[1h]))" thresholds: [95, 98, 99.5] - title: "Error Budget Remaining" type: gauge query: "1 - (error_budget_consumed / error_budget_total)"

L2: Service Overview Dashboard

Every service gets one of these with identical layout: row_1_traffic: - "Request Rate (rps)" — timeseries, by status code - "Error Rate (%)" — timeseries, threshold line at SLO - "Active Requests" — gauge row_2_latency: - "Latency Distribution" — heatmap - "p50 / p95 / p99" — timeseries, threshold lines - "Latency by Endpoint" — table, sorted by p99 row_3_dependencies: - "Downstream Latency" — timeseries per dependency - "Downstream Error Rate" — timeseries per dependency - "Database Query Duration" — timeseries by query type row_4_resources: - "CPU Usage" — timeseries per pod - "Memory Usage" — timeseries per pod - "Pod Restarts" — stat row_5_business: - "Business Metric 1" — service-specific - "Business Metric 2" — service-specific

Dashboard Rules

Time range default: last 1 hour — most debugging happens in recent time Variable selectors at top: environment, service, instance Consistent color coding: green=good, yellow=degraded, red=bad across all dashboards Link alerts to dashboards — every alert annotation includes dashboard URL No more than 15 panels per dashboard — split into L3 if needed Include "as of" timestamp — so screenshots in incidents are unambiguous Dashboard as code — store Grafana JSON in git, provision via API

Incident Severity Classification

SeverityCriteriaResponseCommunicationSEV-1Service down, data loss risk, security breachAll hands, war roomStatus page update every 15 minSEV-2Degraded service, SLO at risk, partial outageOn-call + backupStatus page update every 30 minSEV-3Minor degradation, workaround existsOn-call during hoursInternal Slack updateSEV-4Cosmetic, low impactNext sprintNone

Incident Roles

RoleResponsibilityWhoIncident Commander (IC)Owns the incident. Coordinates. Makes decisions.On-call leadTechnical LeadDiagnoses and fixes. Communicates technical status to IC.Senior engineerCommunications LeadUpdates status page, Slack, stakeholders.Product/supportScribeDocuments timeline, actions, decisions in real-time.Anyone available

Incident Response Workflow

1. DETECT - Alert fires → on-call paged - Customer report → support escalates - Internal discovery → engineer reports 2. TRIAGE (first 5 minutes) - Confirm the issue is real (not false alert) - Classify severity (SEV-1 through SEV-4) - Open incident channel: #inc-YYYY-MM-DD-short-description - Assign roles (IC, Tech Lead, Comms) 3. MITIGATE (next 5-30 minutes) - Goal: STOP THE BLEEDING, not find root cause - Options (try in order): a. Rollback last deploy b. Scale up / restart pods c. Toggle feature flag off d. Redirect traffic / enable fallback e. Manual data fix - Document every action with timestamp 4. STABILIZE - Confirm mitigation is working (metrics back to normal) - Monitor for 15-30 min for recurrence - Update status page: "Monitoring fix" 5. RESOLVE - Confirm all metrics healthy for 30+ min - Update status page: "Resolved" - Schedule post-mortem (within 48 hours for SEV-1/2) - Send internal summary to stakeholders

Incident Channel Template

📋 Incident: Payment API 5xx Errors 🔴 Severity: SEV-2 🕐 Started: 2026-02-22 14:23 UTC 👤 IC: @alice 🔧 Tech Lead: @bob 📢 Comms: @charlie Status: MITIGATING Impact: ~5% of checkout requests failing Customer-facing: Yes Timeline: 14:23 — Alert fired: PaymentAPIHighErrorRate 14:25 — IC assigned: @alice, confirmed real via dashboard 14:28 — Tech Lead: error logs show connection pool exhaustion post-deploy 14:31 — Rolled back deployment v2.3.1 → v2.3.0 14:35 — Error rate dropping, monitoring 14:50 — Error rate <0.1%, marking resolved

Blameless Post-Mortem Template

post_mortem: title: "Payment API Connection Pool Exhaustion" date: "2026-02-22" severity: SEV-2 duration: 27 minutes (14:23 — 14:50 UTC) authors: ["@alice", "@bob"] reviewers: ["@engineering-leads"] status: action_items_in_progress summary: | A deployment at 14:15 introduced a connection leak in the payment API. Connection pool was exhausted by 14:23, causing 5xx errors for ~5% of checkout requests. Rolled back at 14:31; recovered by 14:50. impact: user_impact: "~340 users saw checkout failures over 27 minutes" revenue_impact: "$2,100 estimated (based on average order value × failed checkouts)" slo_impact: "Consumed 5.1 min of 21.9 min monthly error budget (23%)" data_impact: "No data loss. 12 orders failed; users could retry successfully." timeline: - time: "14:15" event: "Deploy v2.3.1 rolled out (3/3 pods updated)" - time: "14:23" event: "PaymentAPIHighErrorRate alert fired" - time: "14:25" event: "IC assigned, confirmed via dashboard" - time: "14:28" event: "Root cause identified: new ORM query not releasing connections" - time: "14:31" event: "Rollback initiated: v2.3.1 → v2.3.0" - time: "14:35" event: "Error rate declining" - time: "14:50" event: "Resolved: error rate <0.1% sustained" root_cause: | The v2.3.1 deploy introduced a new database query in the order validation path. The query used a raw connection instead of the pool's managed client, so connections were acquired but never released. Under load, the pool exhausted within 8 minutes. contributing_factors: - "No integration test for connection pool behavior under load" - "Connection pool saturation metric existed but had no alert" - "Code review didn't catch raw connection usage" what_went_well: - "Alert fired within 8 minutes of deploy" - "IC assigned in 2 minutes" - "Root cause identified in 3 minutes (clear in logs)" - "Rollback executed cleanly" what_went_wrong: - "8-minute detection gap after deploy" - "No canary deployment to catch before full rollout" - "Connection pool saturation had no alert" action_items: - action: "Add connection pool saturation alert (>80% for 2 min)" owner: "@bob" priority: P1 due: "2026-02-25" status: in_progress ticket: "ENG-1234" - action: "Enable canary deployments for payment-api" owner: "@alice" priority: P1 due: "2026-03-01" ticket: "ENG-1235" - action: "Add linting rule: no raw DB connections in application code" owner: "@charlie" priority: P2 due: "2026-03-07" ticket: "ENG-1236" - action: "Load test payment-api connection pool in staging" owner: "@bob" priority: P2 due: "2026-03-07" ticket: "ENG-1237" lessons_learned: - "Resource saturation metrics need alerts, not just dashboards" - "Canary deployments are mandatory for Tier 0 services" - "ORM abstractions don't guarantee connection safety — review raw queries"

Post-Mortem Meeting Agenda (60 minutes)

1. (5 min) Context setting — IC reads the summary 2. (15 min) Timeline walkthrough — what happened, when, by whom 3. (15 min) Root cause deep-dive — 5 Whys exercise 4. (5 min) What went well — celebrate good response 5. (15 min) Action items — assign owners, priorities, due dates 6. (5 min) Wrap-up — review date for action item check-in

5 Whys Exercise

Problem: 5xx errors in payment API Why 1: Database connections were exhausted Why 2: A new query acquired connections without releasing them Why 3: The query used a raw connection instead of the pool manager Why 4: The ORM's raw query API doesn't auto-release (by design) Why 5: We don't have a linting rule or code review checklist item for this Root cause: Missing guard against raw connection usage in application code Systemic fix: Linting rule + connection pool saturation alerting

On-Call Structure

on_call: rotation: weekly handoff_day: Monday 10:00 UTC primary: response_time: 5 minutes (SEV-1/2), 30 minutes (SEV-3) escalation_after: 15 minutes no-ack secondary: response_time: 15 minutes (SEV-1), 1 hour (SEV-2/3) escalation_after: 30 minutes no-ack manager_escalation: trigger: SEV-1 unresolved after 30 minutes handoff_checklist: - Review open incidents and active alerts - Check error budget status for all services - Read post-mortems from previous week - Verify PagerDuty schedule and contact info - Test alert routing (send test page)

On-Call Health Metrics

MetricHealthyNeeds AttentionUnhealthyPages per week<55-15>15After-hours pages per week<22-5>5False positive rate<10%10-30%>30%Mean time to acknowledge<5 min5-15 min>15 minMean time to resolve<30 min30-120 min>120 minToil ratio (manual vs automated)<30%30-60%>60%

Weekly On-Call Review Template

on_call_review: week: "2026-W08" engineer: "@bob" incidents: total: 7 sev_1: 0 sev_2: 1 sev_3: 4 false_positives: 2 after_hours: 3 time_spent: incident_response: "4.5 hours" toil_automation: "2 hours" runbook_updates: "1 hour" improvements_made: - "Silenced noisy disk alert on dev servers" - "Added auto-remediation for pod restart threshold" improvements_needed: - "Cache expiry alert fires every Tuesday at 03:00 — needs investigation" - "Payment retry logic needs circuit breaker (caused 3 alerts)" handoff_notes: | Watch payment-api p99 latency — it's been creeping up since Wednesday. Stripe changed their sandbox endpoints; staging may throw errors.

Chaos Principles

Start with a hypothesis: "If X fails, the system should Y" Run in production (start small — one instance, one AZ) Minimize blast radius with automatic rollback Build confidence incrementally: staging → canary → production

Chaos Experiment Template

chaos_experiment: name: "Payment DB failover" hypothesis: "If the primary database becomes unavailable, traffic should failover to the replica within 30 seconds with <1% error rate spike" steady_state: - metric: "checkout_success_rate" expected: ">99.5%" - metric: "db_query_duration_p99" expected: "<200ms" injection: type: "network_partition" target: "payment-db-primary" duration: "5 minutes" blast_radius: "single AZ" abort_conditions: - "checkout_success_rate < 95% for > 60 seconds" - "revenue_per_minute drops > 50%" - "any SEV-1 incident declared" results: failover_time: "22 seconds" error_spike: "0.3% for 25 seconds" hypothesis_confirmed: true follow_up_actions: - "Document failover behavior in runbook" - "Add failover time as SLI (target: <30s)"

Chaos Engineering Maturity Levels

LevelWhat You TestTools1: ManualKill a pod, see what happenskubectl delete pod2: AutomatedScheduled pod kills, network delaysChaos Monkey, Litmus3: Game DaysMulti-failure scenarios with team exerciseCustom scripts + coordination4: ContinuousAutomated chaos in production with auto-rollbackGremlin, Chaos Mesh

Cost Drivers (Ranked)

#DriverTypical % of BillOptimization1Log volume40-60%Reduce verbosity, drop DEBUG, sample repetitive2Metric cardinality15-25%Drop unused metrics, limit labels3Trace volume10-20%Sampling, tail-based sampling4Retention10-15%Tiered storage (hot → warm → cold)5Query cost5-10%Optimize dashboard queries, set max scan limits

Cost Reduction Checklist

cost_optimization: logs: - action: "Drop DEBUG/TRACE in production" savings: "30-50% of log volume" - action: "Sample health check logs (1:100)" savings: "5-15% of log volume" - action: "Deduplicate identical error bursts" savings: "10-20% during incidents" - action: "Move logs older than 7 days to S3/cold storage" savings: "60-80% of storage cost" - action: "Drop request/response body logging" savings: "20-40% of log volume" metrics: - action: "Audit unused metrics (no dashboard, no alert)" savings: "10-30% of series" - action: "Reduce histogram bucket count (default 11 → 8)" savings: "~27% of histogram series" - action: "Remove high-cardinality labels" savings: "Variable — can be massive" - action: "Increase scrape interval for non-critical metrics (15s → 60s)" savings: "75% of data points for those metrics" traces: - action: "Implement tail-based sampling" savings: "80-95% of trace volume" - action: "Drop internal health check traces" savings: "5-20% of trace volume" - action: "Reduce span attribute size (truncate long strings)" savings: "10-30% of trace storage" general: - action: "Review and right-size retention policies quarterly" - action: "Set query timeouts and result limits on dashboards" - action: "Use recording rules for expensive queries"

Monthly Cost Review Template

observability_cost_review: month: "February 2026" total_cost: "$X,XXX" breakdown: logs: { volume: "X TB", cost: "$X", pct: "X%" } metrics: { series: "X million", cost: "$X", pct: "X%" } traces: { volume: "X TB", cost: "$X", pct: "X%" } infrastructure: { instances: X, cost: "$X", pct: "X%" } cost_per: request: "$0.000X" service: "$X average" engineer: "$X per engineer" optimizations_applied: [] optimizations_planned: [] budget_status: "on_track | over_budget | under_budget"

Correlation: Connecting the Three Pillars

Every log line includes: trace_id, span_id Every trace span includes: service, operation Every metric includes: service label Correlation paths: Alert fires (metric) → Click → Dashboard (metric) → Filter by time window → Trace search (same service + time) → Find failing trace → Logs (filter by trace_id) → See exact error Support ticket (user report) → Find request_id in logs → Extract trace_id → View full trace → Identify slow span → Check span's service metrics → Confirm pattern

Synthetic Monitoring

synthetic_checks: - name: "Checkout flow" type: browser frequency: 5m locations: [us-east, eu-west, ap-southeast] steps: - navigate: "https://app.example.com/products" - click: "Add to Cart" - click: "Checkout" - assert: "Order confirmation page loads in <3s" alert_on: "2 consecutive failures from same location" - name: "API health" type: api frequency: 1m endpoints: - url: "https://api.example.com/health" expected_status: 200 max_latency_ms: 500 - url: "https://api.example.com/v1/products?limit=1" expected_status: 200 max_latency_ms: 1000

Feature Flag Observability

# Correlate feature flags with metrics feature_flag_monitoring: - flag: "new_checkout_flow" metrics_to_compare: - "checkout_conversion_rate" # by flag variant - "checkout_error_rate" - "checkout_latency_p99" alerts: - "If error rate for new variant > 2x control, auto-disable flag"

Observability Maturity Model

DimensionLevel 1Level 2Level 3Level 4LoggingUnstructured logsStructured JSON, centralizedCorrelated with tracesAutomated log analysisMetricsBasic infra metricsRED/USE for servicesSLO-based with error budgetsPredictive (anomaly detection)TracingNo tracingKey services instrumentedFull distributed tracingTrace-driven testingAlertingStatic thresholdsMulti-signal alertsBurn-rate based on SLOsAuto-remediationIncident ResponseAd hocDefined process + rolesPost-mortems with action trackingChaos engineering in prodCulture"Ops team handles it"Shared ownership (you build it, you run it)SLO-driven development velocityReliability as a feature

Quality Scoring Rubric (0-100)

DimensionWeight0510Logging quality15%Unstructured, no correlationStructured JSON, missing fieldsFull schema, trace correlation, PII scrubbingMetrics coverage15%No metricsRED or USE, not bothRED + USE + business metrics + customTracing completeness10%No tracingKey servicesFull path, sampling strategy, tail-basedSLO maturity15%No reliability targetsInformal targetsSLOs with error budgets, burn-rate alerts, weekly reviewAlert quality15%Noisy/missingActionable, some runbooksSLO-based, full runbooks, low false positiveIncident response10%Ad hocDefined processFull process, roles, post-mortems, chaos engineeringDashboard design10%No dashboardsBasic panelsHierarchical L1-L4, consistent, linked to alertsCost efficiency10%Unknown costTrackedOptimized, reviewed monthly, within budget 90-100: World-class. Teach others. 70-89: Production-ready. Fill specific gaps. 50-69: Functional but fragile. <50: Significant reliability risk.

10 Observability Commandments

Structured or it didn't happen — unstructured logs are technical debt Correlate everything — trace_id connects logs, traces, and metrics Alert on symptoms, not causes — users don't care about CPU, they care about latency Every alert gets a runbook — no runbook = no alert SLOs drive velocity — error budgets decide when to ship vs stabilize Dashboards have hierarchy — executives don't need pod CPU graphs Blameless post-mortems always — blame prevents learning Cost is a feature — observability that bankrupts you isn't observability You build it, you run it — the team that ships code owns its observability Practice failure — chaos engineering builds confidence

12 Natural Language Commands

CommandWhat It Does"Audit our observability"Run the /16 health check, score each dimension, prioritize gaps"Design logging for [service]"Generate structured log schema with context fields for the service"Set up metrics for [service]"Create RED + USE + business metric instrumentation plan"Create SLOs for [service]"Define SLIs, targets, error budgets, and burn-rate alert rules"Design alerts for [service]"Create alert rules with severity, thresholds, and runbook templates"Build dashboard for [service]"Design L2 service overview dashboard with panel specifications"Write a runbook for [alert]"Generate structured runbook with diagnosis steps and fixes"Run post-mortem for [incident]"Generate blameless post-mortem document with timeline and action items"Set up on-call for [team]"Design rotation, escalation policy, handoff checklist"Plan chaos experiment for [scenario]"Design experiment with hypothesis, injection, abort conditions"Optimize observability costs"Audit current spend, identify top savings, create reduction plan"Design tracing for [system]"Create OpenTelemetry instrumentation plan with sampling strategy

⚡ Level Up Your Observability

This skill gives you the methodology. For industry-specific implementation patterns: SaaS companies: AfrexAI SaaS Context Pack ($47) — includes SaaS-specific SLOs, multi-tenant monitoring, and usage-based billing observability Fintech: AfrexAI Fintech Context Pack ($47) — compliance audit logging, transaction monitoring, fraud detection signals Healthcare: AfrexAI Healthcare Context Pack ($47) — HIPAA audit trails, PHI access logging, uptime requirements

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