Are you setting up monitoring from scratch?
├─ YES → Start with "1. Design Metrics Strategy"
└─ NO → Do you have an existing issue?
    ├─ YES → Go to "9. Troubleshooting & Analysis"
    └─ NO → Are you improving existing monitoring?
        ├─ Alerts → Go to "3. Alert Design"
        ├─ Dashboards → Go to "4. Dashboard & Visualization"
        ├─ SLOs → Go to "5. SLO & Error Budgets"
        ├─ Tool selection → Read references/tool_comparison.md
        └─ Using Datadog? High costs? → Go to "7. Datadog Cost Optimization & Migration"

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**→ Script**: [scripts/analyze_metrics.py](scripts/analyze_metrics.py)

---

{
  "timestamp": "2024-10-28T14:32:15Z",
  "level": "error",
  "message": "Payment processing failed",
  "service": "payment-service",
  "request_id": "550e8400-e29b-41d4-a716-446655440000",
  "user_id": "user123",
  "order_id": "ORD-456",
  "error_type": "GatewayTimeout",
  "duration_ms": 5000
}

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**→ Script**: [scripts/log_analyzer.py](scripts/log_analyzer.py)

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**Checks for**:
- Alert naming conventions
- Required labels (severity, team)
- Required annotations (summary, description, runbook_url)
- PromQL expression quality
- 'for' clause to prevent flapping

**→ Script**: [scripts/alert_quality_checker.py](scripts/alert_quality_checker.py)

┌─────────────────────────────────────┐
│  Overall Health (Single Stats)      │
│  [Requests/s] [Error%] [P95 Latency]│
└─────────────────────────────────────┘
┌─────────────────────────────────────┐
│  Request Rate & Errors (Graphs)     │
└─────────────────────────────────────┘
┌─────────────────────────────────────┐
│  Latency Distribution (Graphs)      │
└─────────────────────────────────────┘
┌─────────────────────────────────────┐
│  Resource Usage (Graphs)            │
└─────────────────────────────────────┘

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**Supports**:
- Web applications (requests, errors, latency, resources)
- Kubernetes (pods, nodes, resources, network)
- Databases (PostgreSQL, MySQL)

**→ Script**: [scripts/dashboard_generator.py](scripts/dashboard_generator.py)

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**→ Script**: [scripts/slo_calculator.py](scripts/slo_calculator.py)

from opentelemetry import trace

tracer = trace.get_tracer(__name__)

@tracer.start_as_current_span("process_order")
def process_order(order_id):
    span = trace.get_current_span()
    span.set_attribute("order.id", order_id)

    try:
        result = payment_service.charge(order_id)
        span.set_attribute("payment.status", "success")
        return result
    except Exception as e:
        span.set_status(trace.Status(trace.StatusCode.ERROR))
        span.record_exception(e)
        raise

class ErrorSampler(Sampler):
    def should_sample(self, parent_context, trace_id, name, **kwargs):
        attributes = kwargs.get('attributes', {})

        if attributes.get('error', False):
            return Decision.RECORD_AND_SAMPLE

        if trace_id & 0xFF < 3:  # ~1%
            return Decision.RECORD_AND_SAMPLE

        return Decision.DROP

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**What it checks**:
- Infrastructure host count and cost
- Custom metrics usage and high-cardinality metrics
- Log ingestion volume and trends
- APM host usage
- Unused or noisy monitors
- Container vs VM optimization opportunities

**→ Script**: [scripts/datadog_cost_analyzer.py](scripts/datadog_cost_analyzer.py)

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**→ Full Translation Guide**: [references/dql_promql_translation.md](references/dql_promql_translation.md)

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**Checks for**:
- ✓ Returns 200 status code
- ✓ Response time < 1 second
- ✓ Returns JSON format
- ✓ Contains 'status' field
- ✓ Includes version/build info
- ✓ Checks dependencies
- ✓ Disables caching

**→ Script**: [scripts/health_check_validator.py](scripts/health_check_validator.py)

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GET /logs-*/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "level": "error" } },
        { "range": { "@timestamp": { "gte": "now-1h" } } }
      ]
    }
  }
}

{job="app", level="error"} |= "error" | json

fields @timestamp, level, message
| filter level = "error"
| filter @timestamp > ago(1h)