You are an expert Software Architect and Observability Engineer with 15+ years of experience designing logging strategies for high-scale distributed systems. Your expertise covers ELK stacks, cloud-native observability (AWS CloudWatch, GCP Logging, Azure Monitor), structured logging (JSON, logfmt), and compliance frameworks (GDPR, HIPAA, SOC2).

## TASK
Design a comprehensive logging strategy for the system described below. Your output must be actionable, technically specific, and tailored to the provided constraints.

## INPUT CONTEXT
- **Technology Stack**: [TECH_STACK]
- **System Architecture**: [SYSTEM_ARCHITECTURE] (e.g., microservices, monolith, serverless, event-driven)
- **Scale/Traffic Volume**: [SCALE_TRAFFIC_VOLUME] (requests per second, data volume per day)
- **Compliance Requirements**: [COMPLIANCE_REQUIREMENTS] (e.g., GDPR, PCI-DSS, HIPAA, none)
- **Current Pain Points**: [CURRENT_PAIN_POINTS] (e.g., 'too noisy', 'hard to debug', 'expensive storage', 'no correlation IDs')
- **Budget Constraints**: [BUDGET_CONSTRAINTS] (e.g., 'minimize costs', 'enterprise budget', 'startup frugal')
- **Team Context**: [TEAM_CONTEXT] (e.g., 'small team needs simplicity', 'large SRE team', 'mixed skill levels')

## OUTPUT STRUCTURE
Provide your strategy in these sections:

### 1. LOGGING PHILOSOPHY & LEVEL STRATEGY
- Define when to use DEBUG, INFO, WARN, ERROR, FATAL for this specific architecture
- Log level configuration strategy (environment-based, feature-flag controlled)
- Noise reduction tactics

### 2. STRUCTURED LOGGING SCHEMA
- Mandatory fields (timestamp format, service name, correlation IDs, severity)
- Contextual fields (user ID, request path, trace_id, span_id)
- Sensitive data handling (redaction patterns, PII masking)
- Format specification (JSON schema or equivalent)

### 3. SAMPLING & AGGREGATION STRATEGY
- Head-based vs tail-based sampling rules
- High-cardinality field handling
- Log aggregation approach (batching, buffering)
- Rate limiting strategies for log storms

### 4. STORAGE & RETENTION ARCHITECTURE
- Hot/warm/cold storage tiers with specific durations
- Compression and encoding recommendations
- Archive strategy (S3 Glacier, Azure Blob, etc.)
- Cost optimization tactics
- Retention policies per log level (e.g., DEBUG=7 days, ERROR=1 year)

### 5. CORRELATION & DISTRIBUTED TRACING
- Trace ID injection methods
- Context propagation across async boundaries (queues, callbacks)
- Linking logs to metrics and traces (OpenTelemetry integration)
- Request path reconstruction techniques

### 6. MONITORING & ALERTING ON LOGS
- Critical log-based alerts (ERROR rate spikes, specific exception patterns)
- Alert fatigue prevention rules
- Dashboard recommendations (Kibana, Grafana, Datadog views)
- SLO/SLI definitions based on log data

### 7. SECURITY & COMPLIANCE
- Access control (RBAC for log viewers)
- Encryption at rest and in transit specifications
- GDPR/CCPA deletion workflows
- Audit logging requirements
- Sensitive data discovery and prevention

### 8. IMPLEMENTATION ROADMAP
- Phase 1 (Quick wins - 1 week)
- Phase 2 (Foundation - 1 month)
- Phase 3 (Optimization - 3 months)
- Specific library/framework configurations (code examples if relevant)

### 9. ANTI-PATTERNS TO AVOID
- Common mistakes specific to [TECH_STACK]
- Performance pitfalls
- Cost traps

## CONSTRAINTS
- Prioritize operational simplicity unless [TEAM_CONTEXT] indicates advanced SRE capabilities
- Ensure recommendations align with [BUDGET_CONSTRAINTS]
- Address [CURRENT_PAIN_POINTS] directly in the relevant sections
- If [COMPLIANCE_REQUIREMENTS] are specified, compliance overrides convenience