Alerts & SLOs - Audit Trail Platform (ATP)

Measure what matters, alert intelligently — ATP defines Service Level Objectives (SLOs) for availability, latency, correctness, and freshness with error budgets, multi-window burn-rate alerts, golden signal monitoring, and actionable runbook-linked notifications to ensure reliability without alert fatigue.

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📋 Documentation Generation Plan

This document will be generated in 18 cycles. Current progress:

Cycle	Topics	Estimated Lines	Status
Cycle 1	SLO/SLI Fundamentals (1-2)	~3,000	⏳ Not Started
Cycle 2	ATP SLO Catalog (3-4)	~4,000	⏳ Not Started
Cycle 3	Error Budget Policies (5-6)	~3,000	⏳ Not Started
Cycle 4	Golden Signals Framework (7-8)	~4,000	⏳ Not Started
Cycle 5	Metrics Collection (9-10)	~4,500	⏳ Not Started
Cycle 6	Prometheus Configuration (11-12)	~4,000	⏳ Not Started
Cycle 7	Alert Rules Library (13-14)	~5,000	⏳ Not Started
Cycle 8	Burn-Rate Alerts (15-16)	~3,500	⏳ Not Started
Cycle 9	Alertmanager Routing (17-18)	~3,500	⏳ Not Started
Cycle 10	Multi-Window SLO Monitoring (19-20)	~3,000	⏳ Not Started
Cycle 11	Service-Specific Alerts (21-22)	~5,000	⏳ Not Started
Cycle 12	Alert Inhibition & Grouping (23-24)	~3,000	⏳ Not Started
Cycle 13	Dashboard Design (25-26)	~4,000	⏳ Not Started
Cycle 14	Alert Fatigue Prevention (27-28)	~3,000	⏳ Not Started
Cycle 15	On-Call Playbooks (29-30)	~3,500	⏳ Not Started
Cycle 16	SLO Reporting & Reviews (31-32)	~3,000	⏳ Not Started
Cycle 17	Testing & Validation (33-34)	~2,500	⏳ Not Started
Cycle 18	Best Practices & Governance (35-36)	~3,000	⏳ Not Started

Total Estimated Lines: ~64,500

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Purpose & Scope

This document defines ATP's complete alerting and SLO strategy, covering Service Level Indicators (SLIs), Service Level Objectives (SLOs), error budgets, alert rules, golden signals, Prometheus/Alertmanager configuration, burn-rate monitoring, dashboard design, and operational excellence for reliable, observable, and incident-responsive service delivery.

Why SLOs & Alerting for ATP?

1. Reliability: Define and measure what "healthy" means for each service
2. Objectivity: Quantitative metrics replace subjective health assessments
3. Prioritization: Focus engineering effort on what impacts users most
4. Accountability: Error budgets balance innovation vs. stability
5. Proactivity: Alerts detect issues before customers do
6. Actionability: Every alert links to runbook with clear remediation
7. Learning: SLO violations drive postmortems and improvements
8. Compliance: Tamper-evidence and audit integrity SLOs for regulatory requirements

ATP Reliability Principles

1. Define SLOs for User Experience
   - What do users care about? (latency, availability, correctness)
   - NOT: CPU usage, memory, disk I/O (symptoms, not user impact)

2. Measure SLIs (Service Level Indicators)
   - Actual measurements (request success rate, P95 latency, projection lag)
   - High-quality, low-cardinality metrics

3. Set Error Budgets
   - 99.9% SLO = 0.1% error budget = 43.2 min/month downtime
   - Spend budget on innovation; preserve budget for launches

4. Alert on SLO Burn Rate (Not Static Thresholds)
   - Multi-window burn (1h, 6h, 3d) detects fast/slow degradation
   - Avoid alert fatigue from transient blips

5. Actionable Alerts Only
   - Every alert → runbook link
   - Every alert → clear owner
   - Every alert → remediation steps

6. Continuous Improvement
   - Postmortem every SLO violation
   - Update SLOs quarterly
   - Refine alert rules based on false positive rate

Key Concepts

• SLI (Service Level Indicator): Quantitative measurement (e.g., request success rate, P95 latency)
• SLO (Service Level Objective): Target value for SLI (e.g., 99.9% availability, P95 <200ms)
• SLA (Service Level Agreement): Customer-facing contractual commitment (≥ SLO)
• Error Budget: Acceptable failures within SLO (e.g., 0.1% for 99.9% SLO)
• Burn Rate: Rate at which error budget is consumed (fast burn = alert)
• Golden Signals: Latency, Errors, Traffic, Saturation (Google SRE model)
• Multi-Window Alerts: Combine short + long windows to detect fast/slow burns

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Detailed Cycle Plan

CYCLE 1: SLO/SLI Fundamentals (~3,000 lines)

Topic 1: SLO/SLI Principles

What will be covered: - What are SLIs?

Service Level Indicator (SLI):
- Quantitative measurement of service behavior
- Expressed as a ratio or percentage
- Based on user experience (not infrastructure metrics)

Examples:
- Request success rate: (successful requests) / (total requests)
- Latency: Percentage of requests completing within threshold
- Availability: (uptime) / (total time)
- Freshness: Percentage of data within staleness threshold

• What are SLOs?

  Service Level Objective (SLO):
  - Target value for an SLI
  - Defines "healthy" vs. "unhealthy"
  - Time-bound (rolling 30 days typical)
  
  Examples:
  - Availability: 99.9% (over 30 days)
  - Latency: 95% of requests complete in <200ms
  - Correctness: 100% of audit records have valid integrity proofs
  - Freshness: 95% of projections lag <5 seconds
  

• SLO vs. SLA

  SLO (Internal Target):
  - Engineering goal
  - No penalties
  - Drives error budget
  - Example: 99.9% availability
  
  SLA (Customer Contract):
  - Legal commitment
  - Financial penalties if breached
  - Always ≤ SLO (buffer for safety)
  - Example: 99.5% availability (with credits if breached)
  
  Relationship:
  SLA (99.5%) ≤ SLO (99.9%) with 0.4% buffer
  

• Why SLOs Matter for ATP

  User Experience:
  - Users care about: Can I ingest events? Can I query them? Are they tamper-proof?
  - Users don't care about: CPU usage, pod count, cache hit rate
  
  Engineering Focus:
  - SLOs guide prioritization
  - Error budget allows innovation while protecting reliability
  - Violations trigger postmortems and improvements
  
  Compliance:
  - Audit integrity SLOs (100% tamper-detection)
  - Data retention SLOs (100% compliance)
  - Privacy SLOs (100% PII redaction in logs)
  

• Google SRE SLO Framework

  1. Choose SLIs (what to measure)
     - Latency, availability, correctness, freshness
  
  2. Set SLO Targets (what's acceptable)
     - 99.9%, 99%, 95% (depends on criticality)
  
  3. Calculate Error Budget (room for failure)
     - 99.9% SLO = 0.1% error budget = 43.2 min/month
  
  4. Alert on Burn Rate (rate of budget consumption)
     - Fast burn (1h window) + Slow burn (6h window)
  
  5. Report & Review (quarterly SLO review)
     - Adjust SLOs based on actual performance
     - Balance user expectations vs. cost
  

Code Examples: - SLI calculation formulas - SLO definition templates - Error budget computation

Diagrams: - SLO framework - SLI vs. SLO vs. SLA relationship - Error budget visualization

Deliverables: - SLO fundamentals guide - SLI selection criteria - Error budget policies

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Topic 2: ATP SLI Taxonomy

What will be covered: - ATP Service Level Indicators (SLIs)

1. Request Success Rate
   Formula: (successful requests) / (total requests)
   Success: HTTP 2xx, 3xx
   Failure: HTTP 5xx, network errors, timeouts
   Scope: Per service (Gateway, Ingestion, Query)

2. Request Latency (Percentile)
   Measurement: P50, P95, P99 latency
   Threshold: <200ms (P95), <500ms (P99)
   Scope: Per service, per endpoint

3. Projection Freshness (Lag)
   Measurement: Event timestamp → projection updated timestamp
   Threshold: P95 <5s, P99 <10s
   Scope: Per projection type (timeline, actor, resource)

4. Availability (Uptime)
   Measurement: (healthy time) / (total time)
   Healthy: Health check returns 200 OK
   Unhealthy: Health check fails or service unreachable

5. Correctness (Integrity)
   Measurement: (records with valid proofs) / (total records)
   Target: 100% (zero tolerance for integrity failures)

6. Data Durability
   Measurement: (records successfully persisted) / (records ingested)
   Target: 99.999999999% (11 nines)

7. Compliance Rate
   Measurement: (compliant records) / (total records)
   Compliant: Retention, residency, classification applied
   Target: 100%

• SLI Categories by Impact

  User-Facing SLIs (Critical):
  - Ingestion success rate
  - Query latency
  - Export availability
  
  Internal SLIs (Important):
  - Projection lag
  - Outbox relay latency
  - DLQ depth
  
  Infrastructure SLIs (Supporting):
  - Database connection pool usage
  - Cache hit rate
  - Message bus throughput
  

Code Examples: - SLI calculation queries (Prometheus/KQL) - SLI recording rules - SLI dashboards

Diagrams: - SLI taxonomy - User-facing vs. internal SLIs - SLI measurement flow

Deliverables: - Complete SLI catalog - Calculation formulas - Measurement procedures

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CYCLE 2: ATP SLO Catalog (~4,000 lines)

Topic 3: Service-Level SLOs

What will be covered: - ATP SLO Definitions by Service

# Gateway Service
service: gateway
slos:
  - name: availability
    sli: request_success_rate
    target: 99.95%
    window: 30d
    error_budget: 21.6 min/month

  - name: latency_p95
    sli: http_request_duration_p95
    target: 200ms
    window: 30d

  - name: auth_success
    sli: authentication_success_rate
    target: 99.99%
    window: 30d

# Ingestion Service
service: ingestion
slos:
  - name: availability
    sli: ingest_success_rate
    target: 99.9%
    window: 30d
    error_budget: 43.2 min/month

  - name: latency_p95
    sli: ingest_duration_p95
    target: 500ms
    window: 30d

  - name: outbox_lag_p95
    sli: outbox_relay_latency_p95
    target: 5s
    window: 1h

  - name: data_durability
    sli: persist_success_rate
    target: 99.999999999%  # 11 nines
    window: 30d

# Query Service
service: query
slos:
  - name: availability
    sli: query_success_rate
    target: 99.9%
    window: 30d
    error_budget: 43.2 min/month

  - name: latency_p95
    sli: query_duration_p95
    target: 200ms
    window: 30d

  - name: latency_p99
    sli: query_duration_p99
    target: 500ms
    window: 30d

  - name: projection_freshness
    sli: projection_lag_p95
    target: 10s
    window: 1h

# Projection Service
service: projection
slos:
  - name: projection_lag_p95
    sli: projector_lag_seconds_p95
    target: 5s
    window: 1h

  - name: projection_lag_p99
    sli: projector_lag_seconds_p99
    target: 10s
    window: 1h

  - name: consumer_success_rate
    sli: projection_handler_success_rate
    target: 99.99%
    window: 30d

  - name: dlq_rate
    sli: dlq_messages_per_million
    target: <100 ppm
    window: 24h

# Export Service
service: export
slos:
  - name: availability
    sli: export_success_rate
    target: 99.5%
    window: 30d
    error_budget: 3.6 hours/month

  - name: ttfb_p95
    sli: export_time_to_first_byte_p95
    target: 30s
    window: 1h

  - name: completion_p95
    sli: export_completion_time_p95
    target: 5min
    window: 24h

# Integrity Service
service: integrity
slos:
  - name: correctness
    sli: integrity_verification_success_rate
    target: 100%  # Zero tolerance
    window: 30d

  - name: tamper_detection
    sli: tamper_detection_rate
    target: 100%  # Must detect all tampering
    window: 30d

  - name: seal_latency_p95
    sli: hash_chain_seal_latency_p95
    target: 2s
    window: 1h

  - name: kms_availability
    sli: kms_request_success_rate
    target: 99.99%
    window: 30d

# Policy Service
service: policy
slos:
  - name: decision_latency_p95
    sli: policy_decision_latency_p95
    target: 50ms
    window: 1h

  - name: cache_hit_rate
    sli: policy_cache_hit_ratio
    target: 95%
    window: 1h

# Search Service (Optional)
service: search
slos:
  - name: search_latency_p95
    sli: search_query_latency_p95
    target: 500ms
    window: 1h

  - name: indexer_lag_p95
    sli: search_indexer_lag_seconds_p95
    target: 10s
    window: 1h

# Admin Service
service: admin
slos:
  - name: availability
    sli: admin_api_success_rate
    target: 99%
    window: 30d
    error_budget: 7.2 hours/month

• SLO Priority Tiers

  Tier 1 (Critical - Zero Tolerance):
  - Integrity correctness: 100%
  - Tamper detection: 100%
  - Data durability: 99.999999999%
  - Compliance rate: 100%
  
  Impact: Any violation = SEV-1 incident
  
  ---
  
  Tier 2 (High - Strict SLOs):
  - Gateway availability: 99.95%
  - Ingestion availability: 99.9%
  - Query availability: 99.9%
  
  Impact: Violations = SEV-2 incident, customer impact
  
  ---
  
  Tier 3 (Medium - Standard SLOs):
  - Export availability: 99.5%
  - Projection lag: P95 <5s
  - Search latency: P95 <500ms
  
  Impact: Violations = SEV-3, internal impact
  
  ---
  
  Tier 4 (Low - Best Effort):
  - Admin console availability: 99%
  - Batch job success rate: 95%
  
  Impact: Violations tracked, no immediate incident
  

Code Examples: - Complete SLO definitions (YAML configuration) - SLI query examples (PromQL, KQL) - SLO calculation scripts

Diagrams: - SLO hierarchy - Service SLO matrix - Tier classification

Deliverables: - Complete ATP SLO catalog - SLI definitions - Priority matrix

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Topic 4: Cross-Cutting SLOs

What will be covered: - Platform-Wide SLOs

End-to-End Latency (Ingest → Query):
- SLI: Time from event ingestion to queryable
- SLO: P95 <10s, P99 <30s
- Spans: Ingestion + Outbox + Projection + Index

Tenant Isolation Integrity:
- SLI: Cross-tenant query leak rate
- SLO: 0 (zero tolerance)
- Validation: Automated tests, RLS verification

PII Redaction Compliance:
- SLI: PII leaked in logs/metrics
- SLO: 0 (zero tolerance)
- Validation: Log scanning, compliance audits

Secret Management:
- SLI: Secrets in Key Vault (not env vars/code)
- SLO: 100%
- Validation: Secret scanning in CI/CD

• Composite SLOs
• Multiple SLIs combined (AND/OR logic)
• User journey SLOs (multi-service)

Code Examples: - Cross-cutting SLO definitions - Composite calculations - Journey mapping

Diagrams: - Platform SLO dependencies - Journey SLO flow

Deliverables: - Platform SLO catalog - Composite SLO guide - Journey mapping

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CYCLE 3: Error Budget Policies (~3,000 lines)

Topic 5: Error Budget Fundamentals

What will be covered: - What is an Error Budget?

Error Budget = Acceptable failure within SLO

Formula:
Error Budget = (1 - SLO) × Total Requests (or Time)

Examples:

99.9% Availability SLO (30 days):
- Allowed downtime = (1 - 0.999) × 43200 min
- Error budget = 43.2 minutes/month

99% Request Success Rate (1M requests/month):
- Allowed failures = (1 - 0.99) × 1,000,000
- Error budget = 10,000 failed requests/month

P95 Latency <200ms:
- Allowed slow requests = 5% of total
- Error budget = 50,000 slow requests/1M total

• Error Budget Consumption

  Budget Consumed By:
  - Service outages (downtime)
  - 5xx errors (server failures)
  - Slow requests (exceed latency threshold)
  - Projection lag (data staleness)
  - DLQ messages (processing failures)
  
  Budget NOT Consumed By:
  - 4xx errors (client errors)
  - Maintenance windows (scheduled)
  - Load test traffic (excluded)
  - Health check failures (synthetic probes)
  

• Error Budget Policies

  Budget Status: Healthy (0-50% consumed)
  Action: Normal operations
  - Continue feature development
  - Normal deployment cadence
  - Standard testing
  
  Budget Status: Warning (50-75% consumed)
  Action: Slow down, focus on reliability
  - Freeze non-critical features
  - Increase test coverage
  - Review recent changes
  - Add monitoring/alerts
  
  Budget Status: Critical (75-100% consumed)
  Action: Reliability freeze
  - Stop all feature work
  - All hands on stability
  - Root cause analysis for all SLO violations
  - Add redundancy/failover
  
  Budget Status: Exhausted (>100% consumed)
  Action: Incident mode
  - Declare SEV-1 incident
  - Engineering manager + VP engaged
  - Customer communication
  - Postmortem with action items
  - Process review (why did we miss this?)
  

• Budget Tracking

  # Error budget remaining (gauge)
  error_budget_remaining{service="ingestion", slo="availability"} 0.82
  # → 82% of budget remaining (18% consumed)
  
  # Burn rate (gauge, normalized to 1.0 = perfect burn)
  error_budget_burn_rate{service="ingestion", window="1h"} 5.2
  # → Consuming budget 5.2x faster than sustainable
  

Code Examples: - Error budget calculation - Budget tracking metrics - Budget consumption queries

Diagrams: - Error budget lifecycle - Budget consumption timeline - Budget policy decision tree

Deliverables: - Error budget policies - Tracking implementation - Policy enforcement procedures

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Topic 6: Error Budget-Driven Development

What will be covered: - Using Error Budget for Release Decisions

Pre-Release Check:

✅ SAFE TO RELEASE:
- Error budget >80% remaining
- No recent SLO violations (last 7 days)
- All tests passed
- Canary deployment planned

⚠️ CAUTION - EVALUATE CAREFULLY:
- Error budget 50-80% remaining
- Minor SLO violations in last 7 days
- Consider smaller canary (5% vs. 20%)
- Extended monitoring period

❌ DO NOT RELEASE:
- Error budget <50% remaining
- Recent SEV-1/SEV-2 incidents
- SLO violations in last 24 hours
- Focus on stability, not features

🚨 RELIABILITY FREEZE:
- Error budget <25% remaining
- Only hotfixes and reliability improvements
- VP Engineering approval required for any change

• Error Budget Review Meetings
• Weekly review for all services
• Quarterly SLO target review
• Postmortem action item tracking

Code Examples: - Budget decision automation - Release gate integration - Review meeting templates

Diagrams: - Budget-driven decision flow - Release gates

Deliverables: - Budget-driven development guide - Release decision criteria - Review procedures

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CYCLE 4: Golden Signals Framework (~4,000 lines)

Topic 7: Google SRE Golden Signals

What will be covered: - Four Golden Signals

1. Latency
   - How long does it take to service a request?
   - Measure: P50, P95, P99 (histograms)
   - Split by: Success vs. Error (errors often faster!)

2. Traffic (Throughput)
   - How much demand is the system handling?
   - Measure: Requests/second, events/second
   - Split by: Endpoint, tenant class, region

3. Errors
   - What is the rate of failed requests?
   - Measure: Error rate (5xx / total), exception count
   - Split by: Error type, endpoint

4. Saturation
   - How "full" is the system?
   - Measure: CPU %, memory %, queue depth, connection pool usage
   - Alert before hitting limits (e.g., 80% threshold)

• ATP Golden Signals by Service

  Gateway:
  - Latency: http_request_duration_seconds{route,status} (histogram)
  - Traffic: http_requests_total{route,status} (counter)
  - Errors: http_requests_total{status=~"5.."} (counter)
  - Saturation: http_server_active_requests (gauge)
  
  Ingestion:
  - Latency: ingest_append_latency_seconds (histogram)
  - Traffic: ingest_requests_total{result} (counter)
  - Errors: ingest_errors_total{reason} (counter)
  - Saturation: outbox_pending_events (gauge), db_connection_pool_active (gauge)
  
  Query:
  - Latency: query_duration_seconds{route} (histogram)
  - Traffic: query_requests_total{route} (counter)
  - Errors: query_errors_total{route,reason} (counter)
  - Saturation: query_cache_size_bytes (gauge), query_concurrent_requests (gauge)
  
  Projection:
  - Latency: projection_handler_duration_seconds{model} (histogram)
  - Traffic: projection_events_processed_total{model} (counter)
  - Errors: projection_errors_total{model,reason} (counter)
  - Saturation: projection_lag_seconds{model} (gauge), consumer_queue_depth{subscription} (gauge)
  
  Export:
  - Latency: export_job_duration_seconds (histogram)
  - Traffic: export_jobs_total{result} (counter)
  - Errors: export_jobs_failed_total{reason} (counter)
  - Saturation: export_queue_depth (gauge), export_bandwidth_usage_mbps (gauge)
  
  Integrity:
  - Latency: integrity_verify_latency_seconds{target} (histogram)
  - Traffic: integrity_operations_total{operation} (counter)
  - Errors: integrity_verification_failures_total{reason} (counter)
  - Saturation: integrity_pending_verifications (gauge), kms_request_queue_depth (gauge)
  
  Policy:
  - Latency: policy_decision_latency_seconds{mode} (histogram)
  - Traffic: policy_decisions_total{mode,result} (counter)
  - Errors: policy_errors_total{reason} (counter)
  - Saturation: policy_cache_size_bytes (gauge), policy_cache_hit_ratio (gauge)
  

Code Examples: - Complete golden signals implementation (all services) - Metric instrumentation code - Query examples

Diagrams: - Golden signals framework - Signal hierarchy

Deliverables: - Golden signals catalog - Instrumentation guide - Query library

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Topic 8: ATP-Specific Signals

What will be covered: - Audit Trail-Specific Metrics

Tamper Detection:
- tamper_scans_total{result}
- tamper_anomalies_total{type,severity}
- hash_chain_verification_failures_total

Compliance:
- retention_policy_violations_total{tenant}
- data_residency_violations_total{tenant,region}
- pii_redaction_failures_total
- legal_hold_active_count

Multi-Tenancy:
- tenant_isolation_tests_total{result}
- cross_tenant_query_attempts_total (should be 0)
- tenant_quota_exceeded_total{tenant,resource}

Data Lifecycle:
- records_ingested_total{tenant,classification}
- records_archived_total{tenant,tier}
- records_purged_total{tenant,reason}
- records_exported_total{tenant,format}

Code Examples: - ATP-specific metrics - Compliance monitoring - Security metrics

Deliverables: - ATP signals catalog - Compliance metrics - Security dashboards

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CYCLE 5: Metrics Collection (~4,500 lines)

Topic 9: OpenTelemetry Metrics

What will be covered: - OTel Metrics API

// Service instrumentation (C#)
public class IngestionMetrics
{
    private readonly Meter _meter;
    private readonly Counter<long> _ingestRequestsTotal;
    private readonly Histogram<double> _ingestLatencySeconds;
    private readonly Gauge<long> _outboxPendingEvents;

    public IngestionMetrics(IMeterFactory meterFactory)
    {
        _meter = meterFactory.Create("ATP.Ingestion", "1.0.0");

        // Counter: Total ingestion requests
        _ingestRequestsTotal = _meter.CreateCounter<long>(
            name: "ingest.requests.total",
            unit: "requests",
            description: "Total number of ingestion requests");

        // Histogram: Ingestion latency
        _ingestLatencySeconds = _meter.CreateHistogram<double>(
            name: "ingest.latency.seconds",
            unit: "s",
            description: "Ingestion request duration");

        // Gauge: Outbox pending events
        _outboxPendingEvents = _meter.CreateObservableGauge<long>(
            name: "outbox.pending.events",
            observeValue: () => GetOutboxPendingCount(),
            unit: "events",
            description: "Number of pending outbox events");
    }

    public void RecordIngestionRequest(string result, string tenantClass)
    {
        _ingestRequestsTotal.Add(1, 
            new KeyValuePair<string, object>("result", result),
            new KeyValuePair<string, object>("tenant_class", tenantClass));
    }

    public void RecordIngestionLatency(double durationSeconds, string result)
    {
        _ingestLatencySeconds.Record(durationSeconds,
            new KeyValuePair<string, object>("result", result));
    }

    private long GetOutboxPendingCount()
    {
        // Query outbox repository
        return _outboxRepository.GetPendingCount();
    }
}

• Metric Naming Conventions

  Pattern: {namespace}.{subsystem}.{name}.{unit}
  
  Examples:
  - atp.ingest.requests.total (counter)
  - atp.ingest.latency.seconds (histogram)
  - atp.query.cache.hit.ratio (gauge)
  - atp.projection.lag.seconds (gauge)
  
  Labels (low cardinality):
  - service (gateway, ingestion, query, ...)
  - route (/api/v1/ingest, /api/v1/query, ...)
  - result (success, failure, timeout, ...)
  - tenant_class (small, medium, large, enterprise)
  - region (us-east, eu-west, il-central)
  
  Avoid high-cardinality labels:
  - ❌ tenant_id (thousands of values)
  - ❌ user_id (millions of values)
  - ❌ trace_id (unique per request)
  - ✅ tenant_class (small set of buckets)
  

• Histogram Buckets

  // Latency histogram buckets (milliseconds)
  var latencyBuckets = new double[]
  {
      0.001, 0.005, 0.01, 0.025, 0.05,   // 1ms, 5ms, 10ms, 25ms, 50ms
      0.1, 0.25, 0.5, 1.0, 2.5,          // 100ms, 250ms, 500ms, 1s, 2.5s
      5.0, 10.0, 30.0, 60.0              // 5s, 10s, 30s, 60s
  };
  
  // Request size histogram buckets (bytes)
  var sizeBuckets = new double[]
  {
      1024, 4096, 16384, 65536,          // 1KB, 4KB, 16KB, 64KB
      262144, 1048576, 4194304,          // 256KB, 1MB, 4MB
      16777216, 67108864                 // 16MB, 64MB
  };
  

Code Examples: - Complete metrics instrumentation (all services) - Metric naming standards - Histogram configuration

Diagrams: - Metrics collection flow - OTel pipeline

Deliverables: - Metrics instrumentation guide - Naming conventions - Histogram configurations

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Topic 10: Metrics Export & Storage

What will be covered: - OTel Collector Configuration

# otel-collector-config.yaml
receivers:
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
      http:
        endpoint: 0.0.0.0:4318

  prometheus:
    config:
      scrape_configs:
      - job_name: 'kubernetes-pods'
        kubernetes_sd_configs:
        - role: pod
        relabel_configs:
        - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
          action: keep
          regex: true

processors:
  batch:
    timeout: 10s
    send_batch_size: 1024

  resource:
    attributes:
    - key: cloud.provider
      value: azure
      action: insert
    - key: deployment.environment
      from_attribute: ENVIRONMENT
      action: insert

exporters:
  azuremonitor:
    connection_string: "${APPINSIGHTS_CONNECTION_STRING}"

  prometheusremotewrite:
    endpoint: https://prometheus.atp.example.com/api/v1/write
    auth:
      authenticator: bearertokenauth

service:
  pipelines:
    metrics:
      receivers: [otlp, prometheus]
      processors: [batch, resource]
      exporters: [azuremonitor, prometheusremotewrite]

• Prometheus Storage
• Azure Monitor Metrics
• Retention Policies

Code Examples: - Collector configuration - Storage setup - Retention policies

Deliverables: - Collection configuration - Storage setup - Retention guide

---

CYCLE 6: Prometheus Configuration (~4,000 lines)

Topic 11: Prometheus Setup

What will be covered: - Prometheus Deployment (Kubernetes)

# Prometheus StatefulSet
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: prometheus
  namespace: monitoring
spec:
  serviceName: prometheus
  replicas: 2
  selector:
    matchLabels:
      app: prometheus
  template:
    metadata:
      labels:
        app: prometheus
    spec:
      serviceAccountName: prometheus
      containers:
      - name: prometheus
        image: prom/prometheus:v2.48.0
        args:
        - '--config.file=/etc/prometheus/prometheus.yml'
        - '--storage.tsdb.path=/prometheus'
        - '--storage.tsdb.retention.time=30d'
        - '--storage.tsdb.retention.size=50GB'
        - '--web.enable-lifecycle'
        ports:
        - containerPort: 9090
        volumeMounts:
        - name: config
          mountPath: /etc/prometheus
        - name: storage
          mountPath: /prometheus
      volumes:
      - name: config
        configMap:
          name: prometheus-config
  volumeClaimTemplates:
  - metadata:
      name: storage
    spec:
      accessModes: ["ReadWriteOnce"]
      resources:
        requests:
          storage: 100Gi

• Prometheus Configuration

  # prometheus.yml
  global:
    scrape_interval: 15s
    evaluation_interval: 15s
    external_labels:
      cluster: atp-prod-useast
      environment: production
  
  # Alertmanager integration
  alerting:
    alertmanagers:
    - static_configs:
      - targets:
        - alertmanager:9093
  
  # Alert rule files
  rule_files:
  - /etc/prometheus/rules/atp-*.yml
  
  # Scrape configs
  scrape_configs:
  # Kubernetes pods with prometheus.io/scrape annotation
  - job_name: 'kubernetes-pods'
    kubernetes_sd_configs:
    - role: pod
    relabel_configs:
    - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
      action: keep
      regex: true
    - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
      action: replace
      target_label: __metrics_path__
      regex: (.+)
    - source_labels: [__address__, __meta_kubernetes_pod_annotation_prometheus_io_port]
      action: replace
      regex: ([^:]+)(?::\d+)?;(\d+)
      replacement: $1:$2
      target_label: __address__
  
  # OTel Collector
  - job_name: 'otel-collector'
    static_configs:
    - targets: ['otel-collector:8889']
  
  # Kubernetes nodes
  - job_name: 'kubernetes-nodes'
    kubernetes_sd_configs:
    - role: node
  
  # Azure Monitor (if using federated metrics)
  - job_name: 'azure-monitor'
    static_configs:
    - targets: ['azure-monitor-exporter:9090']
  

Code Examples: - Complete Prometheus setup - Scrape configurations - Service discovery

Diagrams: - Prometheus architecture - Scrape flow

Deliverables: - Prometheus deployment guide - Configuration templates - Service discovery setup

---

Topic 12: Recording Rules

What will be covered: - Pre-Aggregation with Recording Rules

# Prometheus recording rules
groups:
- name: atp_sli_recordings
  interval: 30s
  rules:

  # Ingestion success rate (5m window)
  - record: sli:ingest_success_rate:5m
    expr: |
      sum(rate(ingest_requests_total{result="success"}[5m]))
      /
      sum(rate(ingest_requests_total[5m]))

  # Ingestion P95 latency (5m window)
  - record: sli:ingest_latency_p95:5m
    expr: |
      histogram_quantile(0.95,
        sum(rate(ingest_latency_seconds_bucket[5m])) by (le)
      )

  # Query success rate (5m window)
  - record: sli:query_success_rate:5m
    expr: |
      sum(rate(query_requests_total{status=~"2.."}[5m]))
      /
      sum(rate(query_requests_total[5m]))

  # Projection lag (current)
  - record: sli:projection_lag_seconds:current
    expr: |
      max(projection_lag_seconds) by (model, tenant_class, region)

  # Error budget remaining (30d window)
  - record: slo:error_budget_remaining:30d
    expr: |
      1 - (
        (1 - sli:ingest_success_rate:30d) / (1 - 0.999)
      )

Code Examples: - Recording rule library - SLI aggregations - Error budget calculations

Deliverables: - Recording rules catalog - Aggregation guide

---

CYCLE 7: Alert Rules Library (~5,000 lines)

Topic 13: Alert Rule Structure

What will be covered: - Prometheus Alert Rule Anatomy

groups:
- name: atp_ingestion_alerts
  interval: 30s
  rules:

  # High Error Rate
  - alert: IngestionHighErrorRate
    expr: |
      (
        sum(rate(ingest_errors_total[5m])) by (region)
        /
        sum(rate(ingest_requests_total[5m])) by (region)
      ) > 0.05
    for: 5m
    labels:
      severity: critical
      service: ingestion
      team: platform
      tier: tier2
    annotations:
      summary: "High ingestion error rate in {{ $labels.region }}"
      description: "Ingestion error rate is {{ $value | humanizePercentage }} (threshold: 5%)"
      runbook: "https://runbooks.atp.example.com/ingestion-high-error-rate"
      dashboard: "https://grafana.atp.example.com/d/ingestion"
      impact: "Users unable to ingest audit events"
      action: "Check database connectivity, Service Bus health, and recent deployments"

• Alert Rule Best Practices

  1. Clear Naming
     - Pattern: {Service}{Condition}{Metric}
     - Example: IngestionHighErrorRate, QueryHighLatency
  
  2. Appropriate "for" Duration
     - Transient spikes: 5-10 minutes
     - Persistent issues: 15-30 minutes
     - Avoid: <1 minute (too noisy)
  
  3. Meaningful Labels
     - severity: critical | warning | info
     - service: Name of affected service
     - team: Owner team
     - tier: SLO tier (tier1, tier2, tier3)
  
  4. Actionable Annotations
     - summary: One-line description
     - description: Details with context
     - runbook: Link to remediation steps
     - dashboard: Link to relevant dashboard
     - impact: User/business impact
     - action: Immediate next steps
  
  5. Threshold Selection
     - Based on SLO targets (not arbitrary)
     - Supported by historical data
     - Reviewed quarterly
  

Code Examples: - Alert rule templates - Complete alert library (50+ rules) - Best practice examples

Diagrams: - Alert rule structure - Alert lifecycle

Deliverables: - Alert rule library (all ATP services) - Naming conventions - Template guide

---

Topic 14: ATP Alert Catalog

What will be covered: - Complete Alert Inventory

# TIER 1 ALERTS (Critical - SEV-1)

- alert: IntegrityVerificationFailure
  expr: integrity_verification_failures_total > 0
  for: 1m
  labels:
    severity: critical
    tier: tier1
  annotations:
    summary: "Integrity verification failure detected"
    runbook: "runbooks/tamper-investigation"

- alert: TamperDetected
  expr: tamper_anomalies_total{severity="critical"} > 0
  for: 1m
  labels:
    severity: critical
    tier: tier1

- alert: CrossTenantDataLeak
  expr: cross_tenant_query_attempts_total > 0
  for: 1m
  labels:
    severity: critical
    tier: tier1

- alert: ComplianceViolation
  expr: |
    retention_policy_violations_total > 0
    OR data_residency_violations_total > 0
  for: 5m
  labels:
    severity: critical
    tier: tier1

# TIER 2 ALERTS (High - SEV-2)

- alert: GatewayDown
  expr: up{service="gateway"} == 0
  for: 2m
  labels:
    severity: critical
    tier: tier2

- alert: IngestionHighErrorRate
  expr: |
    (
      sum(rate(ingest_errors_total[5m]))
      /
      sum(rate(ingest_requests_total[5m]))
    ) > 0.05
  for: 5m
  labels:
    severity: critical
    tier: tier2

- alert: QueryHighLatency
  expr: |
    histogram_quantile(0.95,
      sum(rate(query_latency_seconds_bucket[5m])) by (le)
    ) > 0.5
  for: 10m
  labels:
    severity: warning
    tier: tier2

- alert: ProjectionLagHigh
  expr: projection_lag_seconds > 30
  for: 10m
  labels:
    severity: warning
    tier: tier2

# TIER 3 ALERTS (Medium - SEV-3)

- alert: DLQBacklogGrowing
  expr: dlq_depth{subscription=~".*"} > 100
  for: 30m
  labels:
    severity: warning
    tier: tier3

- alert: CacheHitRateLow
  expr: query_cache_hit_ratio < 0.7
  for: 1h
  labels:
    severity: info
    tier: tier3

(50+ alerts documented with thresholds, durations, labels, runbooks)

Code Examples: - Complete alert rule files (all services) - Alert categorization - Threshold rationale

Deliverables: - Complete alert catalog - Alert rule files - Categorization guide

---

CYCLE 8: Burn-Rate Alerts (~3,500 lines)

Topic 15: Multi-Window Burn Rate

What will be covered: - Burn Rate Concept

Burn Rate = Rate of error budget consumption

Perfect Burn Rate = 1.0
- Consuming budget at sustainable rate
- Will reach 100% budget consumed at end of SLO window

Fast Burn Rate > 1.0
- Consuming budget faster than sustainable
- Will exhaust budget before end of window
- Example: Burn rate of 14.4 = exhaust budget in 2 hours (30d window)

Slow Burn Rate < 1.0
- Consuming budget slower than expected
- Good! Under budget

• Multi-Window Burn Rate Alerts

  # Google SRE Multi-Window Burn Rate
  # 99.9% SLO = 0.1% error budget over 30 days
  
  groups:
  - name: atp_burn_rate_alerts
    rules:
  
    # Page (2% budget in 1 hour = very fast burn)
    - alert: IngestionErrorBudgetBurnFast
      expr: |
        (
          sum(rate(ingest_errors_total[1h]))
          /
          sum(rate(ingest_requests_total[1h]))
        ) > (0.001 * 14.4)
        AND
        (
          sum(rate(ingest_errors_total[5m]))
          /
          sum(rate(ingest_requests_total[5m]))
        ) > (0.001 * 14.4)
      for: 2m
      labels:
        severity: critical
        tier: tier2
        alert_type: burn_rate
        window: fast
      annotations:
        summary: "Ingestion error budget burning fast"
        description: "At current rate, will exhaust 2% of monthly budget in 1 hour"
        runbook: "runbooks/burn-rate-response"
  
    # Ticket (5% budget in 6 hours = moderate burn)
    - alert: IngestionErrorBudgetBurnModerate
      expr: |
        (
          sum(rate(ingest_errors_total[6h]))
          /
          sum(rate(ingest_requests_total[6h]))
        ) > (0.001 * 6)
        AND
        (
          sum(rate(ingest_errors_total[30m]))
          /
          sum(rate(ingest_requests_total[30m]))
        ) > (0.001 * 6)
      for: 15m
      labels:
        severity: warning
        tier: tier2
        alert_type: burn_rate
        window: moderate
      annotations:
        summary: "Ingestion error budget burning at moderate rate"
  
    # Warning (10% budget in 3 days = slow burn)
    - alert: IngestionErrorBudgetBurnSlow
      expr: |
        (
          sum(rate(ingest_errors_total[3d]))
          /
          sum(rate(ingest_requests_total[3d]))
        ) > (0.001 * 1.0)
        AND
        (
          sum(rate(ingest_errors_total[6h]))
          /
          sum(rate(ingest_requests_total[6h]))
        ) > (0.001 * 1.0)
      for: 1h
      labels:
        severity: info
        tier: tier2
        alert_type: burn_rate
        window: slow
  

• Burn Rate Thresholds (Google SRE) | Alert Severity | Budget Consumed | Time Window | Burn Rate Multiple | |----------------|-----------------|-------------|-------------------| | Page | 2% in 1 hour | 1h + 5m | 14.4x | | Ticket | 5% in 6 hours | 6h + 30m | 6x | | Warning | 10% in 3 days | 3d + 6h | 1x |

Code Examples: - Burn rate alert rules (all services) - Threshold calculations - Multi-window logic

Diagrams: - Burn rate visualization - Multi-window detection - Alert severity mapping

Deliverables: - Burn rate alert library - Threshold guide - Configuration templates

---

Topic 16: Latency Burn Rate

What will be covered: - Latency-Based Error Budgets

SLO: 95% of requests complete in <200ms
Error Budget: 5% can exceed 200ms

Burn Rate:
- Measure: % of requests >200ms
- Target: ≤5%
- Fast burn: >10% exceeding (2x budget rate)

• Latency Burn Alert

  - alert: QueryLatencyBudgetBurn
    expr: |
      (
        sum(rate(query_latency_seconds_bucket{le="0.2"}[1h]))
        /
        sum(rate(query_latency_seconds_count[1h]))
      ) < 0.95
    for: 5m
    labels:
      severity: warning
  

Code Examples: - Latency burn rate rules - Quantile calculations

Deliverables: - Latency budget guide - Alert configurations

---

CYCLE 9: Alertmanager Routing (~3,500 lines)

Topic 17: Alertmanager Configuration

What will be covered: - Alertmanager Setup

# alertmanager.yml
global:
  resolve_timeout: 5m

  # PagerDuty integration
  pagerduty_url: https://events.pagerduty.com/v2/enqueue

  # Slack integration
  slack_api_url: https://hooks.slack.com/services/T00000000/B00000000/XXXXXXXXXXXX

# Alert routing tree
route:
  group_by: ['alertname', 'service', 'region']
  group_wait: 10s
  group_interval: 5m
  repeat_interval: 4h
  receiver: 'default'

  routes:
  # Critical alerts (SEV-1) → PagerDuty + Slack
  - match:
      severity: critical
    receiver: 'pagerduty-critical'
    routes:
    - match:
        tier: tier1
      receiver: 'pagerduty-tier1'
      group_wait: 0s
      repeat_interval: 1h

  # Warning alerts (SEV-2/SEV-3) → Slack only
  - match:
      severity: warning
    receiver: 'slack-ops'

  # Info alerts → Slack low-priority
  - match:
      severity: info
    receiver: 'slack-notifications'
    group_interval: 1h
    repeat_interval: 24h

# Inhibition rules (suppress dependent alerts)
inhibit_rules:
# If service is down, suppress all its latency/error alerts
- source_match:
    alertname: 'ServiceDown'
  target_match_re:
    alertname: '(HighLatency|HighErrorRate).*'
  equal: ['service', 'region']

# If database is down, suppress all service database alerts
- source_match:
    alertname: 'DatabaseDown'
  target_match_re:
    alertname: '.*DatabaseConnection.*'
  equal: ['region']

# Receivers
receivers:
- name: 'default'
  slack_configs:
  - channel: '#atp-alerts-default'
    title: 'ATP Alert: {{ .GroupLabels.alertname }}'

- name: 'pagerduty-tier1'
  pagerduty_configs:
  - service_key: '<tier1-integration-key>'
    severity: '{{ .GroupLabels.severity }}'
    description: '{{ .CommonAnnotations.summary }}'
    details:
      firing: '{{ template "pagerduty.default.instances" . }}'
      num_firing: '{{ .Alerts.Firing | len }}'
      num_resolved: '{{ .Alerts.Resolved | len }}'
    client: 'ATP Alertmanager'
    client_url: '{{ .CommonAnnotations.dashboard }}'

- name: 'pagerduty-critical'
  pagerduty_configs:
  - service_key: '<critical-integration-key>'

- name: 'slack-ops'
  slack_configs:
  - channel: '#atp-ops'
    title: '⚠️ {{ .GroupLabels.alertname }}'
    text: '{{ .CommonAnnotations.description }}'
    actions:
    - type: button
      text: 'View Runbook'
      url: '{{ .CommonAnnotations.runbook }}'
    - type: button
      text: 'View Dashboard'
      url: '{{ .CommonAnnotations.dashboard }}'

- name: 'slack-notifications'
  slack_configs:
  - channel: '#atp-notifications'
    title: 'ℹ️ {{ .GroupLabels.alertname }}'

Code Examples: - Complete Alertmanager configuration - Routing logic - Receiver configurations

Diagrams: - Alert routing tree - Inhibition flow - Multi-receiver routing

Deliverables: - Alertmanager setup guide - Routing configuration - Receiver templates

---

Topic 18: Alert Destinations

What will be covered: - PagerDuty Integration - Service keys (per team/severity) - Escalation policies - Incident auto-creation - Auto-resolve on recovery

• Slack Integration
• Channel routing (#atp-incidents, #atp-ops, #atp-notifications)
• Alert formatting
• Interactive buttons (runbook, dashboard, acknowledge)

• Thread-based updates

• Jira Integration

• Auto-create tickets for SEV-2+
• Link to PagerDuty incident

• Auto-close on resolution

• Webhook Integration

• Custom automation (auto-remediation)
• Runbook execution
• External SIEM (Splunk, Azure Sentinel)

Code Examples: - Integration configurations - Webhook templates - Auto-remediation scripts

Diagrams: - Destination integration - Alert flow

Deliverables: - Integration guide - Configuration templates

---

CYCLE 10: Multi-Window SLO Monitoring (~3,000 lines)

Topic 19: Multi-Window Burn Rate Implementation

What will be covered: - Why Multi-Window?

Single Window Problem:
- Short window (5m): Noisy, alerts on transient blips
- Long window (30d): Slow, misses fast-burning issues

Multi-Window Solution:
- Combine short + long windows
- Short window detects active problem
- Long window confirms sustained burn
- Both must fire to alert (AND logic)

Example:
Alert if:
- Error rate >threshold in last 1 hour (long window)
AND
- Error rate >threshold in last 5 minutes (short window)

Benefits:
- Fast detection (5m window)
- Low false positives (1h confirmation)
- Balanced sensitivity

• Google SRE Multi-Window Burn Rates (Complete implementation for all ATP SLOs)

Code Examples: - Multi-window alert rules - Burn rate calculations

Deliverables: - Multi-window guide - Implementation library

---

Topic 20: SLO Dashboard Design

What will be covered: - SLO Overview Dashboard - Error Budget Visualization - Burn Rate Graphs - Historical SLO Compliance

Code Examples: - Grafana dashboard JSON

Deliverables: - Dashboard templates

---

CYCLE 11: Service-Specific Alerts (~5,000 lines)

Topic 21: Per-Service Alert Libraries

What will be covered: - Ingestion Service Alerts (15+ alerts) - Query Service Alerts (12+ alerts) - Projection Service Alerts (10+ alerts) - Export Service Alerts (8+ alerts) - Integrity Service Alerts (10+ alerts) - Policy Service Alerts (6+ alerts) - Gateway Service Alerts (12+ alerts) - Admin Service Alerts (5+ alerts)

Code Examples: - Service-specific alert rules (complete)

Deliverables: - Service alert libraries (8 services)

---

Topic 22: Infrastructure Alerts

What will be covered: - Kubernetes Alerts - Database Alerts - Message Bus Alerts - Storage Alerts - Network Alerts

Code Examples: - Infrastructure alert rules

Deliverables: - Infrastructure alert library

---

CYCLE 12: Alert Inhibition & Grouping (~3,000 lines)

Topic 23: Alert Inhibition

What will be covered: - Dependency-Based Inhibition - Parent-Child Relationships - Cascading Failure Prevention

Code Examples: - Inhibition rules

Deliverables: - Inhibition guide

---

Topic 24: Alert Grouping & Deduplication

What will be covered: - Grouping Strategy - Deduplication Windows - Fingerprint Generation

Code Examples: - Grouping configuration

Deliverables: - Grouping guide

---

CYCLE 13: Dashboard Design (~4,000 lines)

Topic 25: Grafana Dashboards

What will be covered: - Dashboard Architecture - ATP Operations Dashboard - Service Health Dashboards (all services) - SLO Compliance Dashboards

Code Examples: - Complete dashboard library

Deliverables: - Dashboard templates (10+ dashboards)

---

Topic 26: Azure Monitor Workbooks

What will be covered: - Workbook Templates - KQL Queries - Application Map - Performance Analysis

Code Examples: - Workbook definitions

Deliverables: - Workbook library

---

CYCLE 14: Alert Fatigue Prevention (~3,000 lines)

Topic 27: Reducing False Positives

What will be covered: - Threshold Tuning - Duration Optimization - Silence Management - Alert Review Process

Code Examples: - Tuning procedures

Deliverables: - Fatigue prevention guide

---

Topic 28: Alert Quality Metrics

What will be covered: - Alert Precision & Recall - Mean Time to Acknowledge - False Positive Rate

Code Examples: - Quality tracking

Deliverables: - Quality metrics guide

---

CYCLE 15: On-Call Playbooks (~3,500 lines)

Topic 29: Runbook Integration

What will be covered: - Alert-to-Runbook Mapping - Playbook Templates - Diagnostic Procedures

Code Examples: - Playbook library

Deliverables: - Runbook catalog

---

Topic 30: Auto-Remediation

What will be covered: - Safe Auto-Remediation - Remediation Scripts - Validation & Rollback

Code Examples: - Auto-remediation playbooks

Deliverables: - Automation guide

---

CYCLE 16: SLO Reporting & Reviews (~3,000 lines)

Topic 31: SLO Reporting

What will be covered: - Monthly SLO Reports - Error Budget Consumption - Trend Analysis - Stakeholder Communication

Code Examples: - Report generation

Deliverables: - Reporting guide

---

Topic 32: SLO Review Process

What will be covered: - Quarterly SLO Review - Target Adjustments - New SLO Proposals

Code Examples: - Review procedures

Deliverables: - Review process guide

---

CYCLE 17: Testing & Validation (~2,500 lines)

Topic 33: Alert Testing

What will be covered: - Alert Rule Validation - Synthetic Incidents - Chaos Testing for Alerts

Code Examples: - Testing procedures

Deliverables: - Alert testing guide

---

Topic 34: SLO Validation

What will be covered: - SLO Metric Validation - Error Budget Calculation Verification

Code Examples: - Validation scripts

Deliverables: - Validation guide

---

CYCLE 18: Best Practices & Governance (~3,000 lines)

Topic 35: SLO Best Practices

What will be covered: - Choosing Good SLIs - Setting Realistic SLOs - Avoiding Common Pitfalls

Deliverables: - Best practices guide

---

Topic 36: Alert Governance

What will be covered: - Alert Review Process - Alert Ownership - Alert Lifecycle Management

Deliverables: - Governance guide

---

Summary of Deliverables

Across all 18 cycles, this documentation will provide:

1. SLO/SLI Framework: Fundamentals, ATP taxonomy, tier classification
2. ATP SLO Catalog: Complete SLOs for all 8 services + platform SLOs
3. Error Budgets: Policies, tracking, budget-driven development
4. Golden Signals: Latency, errors, traffic, saturation for all services
5. Metrics: Collection, naming, OTel instrumentation, Prometheus storage
6. Alert Rules: 70+ rules covering availability, latency, correctness, freshness
7. Burn-Rate Alerts: Multi-window fast/moderate/slow burn detection
8. Alertmanager: Routing, inhibition, grouping, deduplication
9. Dashboards: Grafana SLO dashboards, Azure Monitor workbooks
10. Operational Excellence: Fatigue prevention, playbooks, reporting, governance

---

Related Documentation

• Runbook: Operational procedures and incident response
• Progressive Rollout: Deployment strategies
• Observability: Tracing, logging, monitoring
• Kubernetes: K8s monitoring and health checks
• Architecture: System design and SLOs
• Quality Gates: CI/CD quality enforcement

---

This alerts & SLOs guide provides complete definitions, implementations, and operational procedures for measuring and maintaining ATP reliability through Service Level Objectives, error budgets, golden signal monitoring, intelligent alerting with burn-rate detection, actionable runbooks, comprehensive dashboards, and continuous improvement processes for delivering predictable, compliant, and tamper-evident audit trail services at scale.