# Monitoring and Observability Setup
This template provides guidelines for implementing comprehensive monitoring and observability in the project.
## Monitoring Strategy
### Key Principles
1. **Comprehensive Coverage**: Monitor all critical system components
2. **Actionable Insights**: Focus on metrics that drive decisions
3. **Proactive Detection**: Identify issues before they impact users
4. **Contextual Alerting**: Provide sufficient context in alerts
5. **Holistic View**: Combine metrics, logs, and traces for complete visibility
### Monitoring Layers
1. **Infrastructure**: Servers, containers, cloud resources
2. **Application**: API endpoints, services, background jobs
3. **Business**: User actions, conversions, engagement
4. **Security**: Access patterns, anomalies, vulnerabilities
## Key Metrics to Track
### System Metrics
- **CPU Usage**: Average and peak utilization
- **Memory Usage**: Total, used, cached, buffer
- **Disk Usage**: Space, I/O operations, latency
- **Network**: Throughput, latency, error rates
- **Container Metrics**: Restarts, resource usage
### Application Metrics
- **Request Rate**: Requests per second
- **Error Rate**: Percentage of failed requests
- **Latency**: Response time (p50, p90, p95, p99)
- **Saturation**: How overloaded the system is
- **Apdex Score**: User satisfaction based on response time
### Business Metrics
- **Active Users**: Daily/monthly active users
- **Conversion Rate**: Percentage of users completing key actions
- **Session Duration**: Time spent in the application
- **Error Impact**: Number of users affected by errors
- **Feature Usage**: Adoption of specific features
### Database Metrics
- **Query Performance**: Execution time, slow queries
- **Connection Pool**: Utilization, wait time
- **Cache Hit Rate**: Effectiveness of database caching
- **Index Usage**: Proper utilization of indexes
- **Transaction Volume**: Number of transactions
## Logging Standards
### Log Levels
- **ERROR**: Exception conditions requiring immediate attention
- **WARN**: Unexpected situations that can be recovered from
- **INFO**: Important application events and milestones
- **DEBUG**: Detailed information for troubleshooting
- **TRACE**: Very detailed debugging information
### Log Format
Structured JSON format with the following fields:
```json
{
"timestamp": "2023-01-01T12:00:00.000Z",
"level": "INFO",
"service": "user-service",
"trace_id": "4f8b3e2a1c9d8e7f",
"span_id": "1a2b3c4d5e6f",
"user_id": "anonymous/user-id",
"message": "User logged in successfully",
"context": {
"request_id": "abcd1234",
"ip_address": "127.0.0.1",
"user_agent": "Mozilla/5.0..."
},
"additional_data": {
"key1": "value1",
"key2": "value2"
}
}
```
### Logging Best Practices
1. **Be Selective**: Log important events, not everything
2. **Include Context**: Add relevant context for troubleshooting
3. **Sensitive Data**: Never log passwords, tokens, or PII
4. **Consistency**: Use consistent formatting and levels
5. **Performance**: Consider logging impact on application performance
## Tracing Configuration
### Distributed Tracing Setup
1. **Instrumentation**: Auto-instrument frameworks and libraries
2. **Propagation**: Use standard headers (W3C Trace Context)
3. **Sampling**: Implement appropriate sampling strategy
4. **Service Map**: Visualize service dependencies
### Key Tracing Metrics
- **End-to-End Latency**: Total request processing time
- **Service Latency**: Time spent in each service
- **Database Calls**: Time spent in database operations
- **External Service Calls**: Time spent calling external APIs
- **Error Paths**: Trace paths that result in errors
## Alert Configuration
### Alert Severity Levels
1. **Critical**: Immediate action required (24/7)
2. **High**: Action required during business hours
3. **Medium**: Should be investigated within 1-2 days
4. **Low**: Informational, no immediate action required
### Alert Types
1. **Threshold-based**: Trigger when metric exceeds threshold
2. **Anomaly-based**: Trigger on unusual patterns
3. **Absence-based**: Trigger when expected data is missing
4. **Composite**: Trigger based on multiple conditions
### Effective Alert Design
1. **Actionable**: Clear what action is needed
2. **Relevant**: Alert the right team or person
3. **Contextual**: Include sufficient diagnostic information
4. **Prioritized**: Indicate urgency and importance
5. **Documented**: Link to runbook or documentation
## Dashboard Setup
### Dashboard Types
1. **Overview**: High-level system health
2. **Service-Specific**: Detailed metrics for each service
3. **Business**: Key business metrics and KPIs
4. **On-Call**: Critical metrics for incident response
5. **SLO/SLA**: Service level objectives and compliance
### Dashboard Best Practices
1. **Simplicity**: Focus on key metrics, avoid clutter
2. **Consistency**: Use consistent layout and visualization
3. **Context**: Include sufficient context for interpretation
4. **Interactivity**: Allow drill-down into detailed metrics
5. **Time Range**: Support different time ranges for analysis
## Monitoring Tools Integration
### Infrastructure Setup
```yaml
# Prometheus configuration example
global:
scrape_interval: 15s
scrape_configs:
- job_name: 'application'
metrics_path: '/metrics'
static_configs:
- targets: ['application:8080']
- job_name: 'database'
static_configs:
- targets: ['database-exporter:9187']
- job_name: 'node-exporter'
static_configs:
- targets: ['node-exporter:9100']
```
### Application Instrumentation
#### Python Example (with Prometheus Client)
```python
from prometheus_client import Counter, Histogram, start_http_server
import time
# Create metrics
REQUEST_COUNT = Counter('app_requests_total', 'Total app requests', ['method', 'endpoint', 'status'])
REQUEST_LATENCY = Histogram('app_request_latency_seconds', 'Request latency', ['method', 'endpoint'])
# Start metrics server
start_http_server(8000)
# Example instrumentation
def process_request(request):
start_time = time.time()
# Process request here
status = 200
# Record metrics
REQUEST_COUNT.labels(request.method, request.path, status).inc()
REQUEST_LATENCY.labels(request.method, request.path).observe(time.time() - start_time)
```
#### JavaScript Example (with Prometheus Client)
```javascript
const promClient = require('prom-client');
// Create a Registry to register metrics
const register = new promClient.Registry();
// Enable default metrics
promClient.collectDefaultMetrics({ register });
// Create custom metrics
const httpRequestCounter = new promClient.Counter({
name: 'http_requests_total',
help: 'Total number of HTTP requests',
labelNames: ['method', 'endpoint', 'status'],
registers: [register]
});
const httpRequestDuration = new promClient.Histogram({
name: 'http_request_duration_seconds',
help: 'HTTP request duration in seconds',
labelNames: ['method', 'endpoint'],
buckets: [0.01, 0.05, 0.1, 0.5, 1, 5],
registers: [register]
});
// Example middleware for Express
function metricsMiddleware(req, res, next) {
const start = Date.now();
res.on('finish', () => {
const duration = (Date.now() - start) / 1000;
httpRequestCounter.inc({ method: req.method, endpoint: req.path, status: res.statusCode });
httpRequestDuration.observe({ method: req.method, endpoint: req.path }, duration);
});
next();
}
// Metrics endpoint
app.get('/metrics', async (req, res) => {
res.set('Content-Type', register.contentType);
res.end(await register.metrics());
});
```
### Log Aggregation Setup
#### Fluentd Configuration Example
```
<source>
@type forward
port 24224
bind 0.0.0.0
</source>
<filter **>
@type parser
key_name log
reserve_data true
<parse>
@type json
</parse>
</filter>
<match **>
@type elasticsearch
host elasticsearch
port 9200
logstash_format true
logstash_prefix fluentd
<buffer>
@type file
path /var/log/fluentd-buffers/
flush_mode interval
flush_interval 5s
</buffer>
</match>
```
## Incident Response
### Incident Severity Levels
1. **SEV1**: Critical service outage affecting all users
2. **SEV2**: Partial service outage affecting many users
3. **SEV3**: Degraded service affecting some users
4. **SEV4**: Minor issue affecting few users
### Incident Response Workflow
1. **Detection**: Automated alert or manual report
2. **Triage**: Assess severity and impact
3. **Response**: Investigate and mitigate
4. **Resolution**: Implement fix and verify
5. **Postmortem**: Document and learn
### Runbook Template
```markdown
# [Incident Type] Runbook
## Overview
[Brief description of the incident type]
## Detection
- **Alert Conditions**: [What triggers the alert]
- **Dashboard**: [Link to relevant dashboard]
- **Symptoms**: [Observable symptoms]
## Triage
1. [Initial investigation step]
2. [Impact assessment]
3. [Severity determination]
## Response
### 1. [Initial Response]
- [Step 1]
- [Step 2]
- [Step 3]
### 2. [Secondary Response]
- [Step 1]
- [Step 2]
- [Step 3]
### 3. [Escalation Process]
- **Tier 1**: [Who to contact and when]
- **Tier 2**: [Who to contact and when]
- **Tier 3**: [Who to contact and when]
## Verification
- [How to verify the issue is resolved]
- [What metrics to check]
## Communication
- **Internal**: [Who to inform and how]
- **External**: [Customer communication if needed]
## Follow-up
- [Postmortem process]
- [Preventive measures]
```
## SLO and SLA Monitoring
### Service Level Indicators (SLIs)
1. **Availability**: Percentage of successful requests
2. **Latency**: Response time percentiles
3. **Throughput**: Requests per second
4. **Error Rate**: Percentage of failed requests
5. **Saturation**: Resource utilization
### Service Level Objectives (SLOs)
1. **Availability SLO**: 99.9% successful requests
2. **Latency SLO**: 95% of requests under 200ms
3. **Error Rate SLO**: Less than 0.1% error rate
### Error Budget Monitoring
1. **Budget Calculation**: 100% - SLO = Error Budget
2. **Consumption Rate**: How quickly budget is being used
3. **Alerting**: Notify when budget is being consumed too quickly
## Implementation Plan
### Phase 1: Basic Monitoring
1. Set up infrastructure monitoring
2. Implement basic application metrics
3. Configure centralized logging
4. Create essential dashboards
5. Set up critical alerts
### Phase 2: Enhanced Observability
1. Implement distributed tracing
2. Add business metrics
3. Develop comprehensive dashboards
4. Set up detailed alerting
5. Create initial runbooks
### Phase 3: Advanced Monitoring
1. Define and implement SLOs
2. Set up error budget monitoring
3. Implement anomaly detection
4. Create comprehensive runbooks
5. Establish regular monitoring review process
## Tool-Specific Configurations
### Prometheus/Grafana Setup
```yaml
# docker-compose.yml example
version: '3'
services:
prometheus:
image: prom/prometheus
volumes:
- ./prometheus.yml:/etc/prometheus/prometheus.yml
ports:
- "9090:9090"
grafana:
image: grafana/grafana
depends_on:
- prometheus
ports:
- "3000:3000"
volumes:
- grafana-data:/var/lib/grafana
node-exporter:
image: prom/node-exporter
ports:
- "9100:9100"
restart: always
volumes:
grafana-data:
```
### ELK Stack Setup
```yaml
# docker-compose.yml example
version: '3'
services:
elasticsearch:
image: docker.elastic.co/elasticsearch/elasticsearch:7.10.0
environment:
- discovery.type=single-node
ports:
- "9200:9200"
volumes:
- es-data:/usr/share/elasticsearch/data
logstash:
image: docker.elastic.co/logstash/logstash:7.10.0
depends_on:
- elasticsearch
volumes:
- ./logstash.conf:/usr/share/logstash/pipeline/logstash.conf
kibana:
image: docker.elastic.co/kibana/kibana:7.10.0
depends_on:
- elasticsearch
ports:
- "5601:5601"
volumes:
es-data:
```
### Jaeger Tracing Setup
```yaml
# docker-compose.yml example
version: '3'
services:
jaeger:
image: jaegertracing/all-in-one:latest
ports:
- "6831:6831/udp"
- "16686:16686"
environment:
- COLLECTOR_ZIPKIN_HTTP_PORT=9411
```
## Maintenance and Optimization
### Regular Maintenance Tasks
1. **Alert Review**: Review and tune alert thresholds
2. **Dashboard Update**: Keep dashboards relevant and current
3. **Log Rotation**: Ensure proper log retention and rotation
4. **Storage Optimization**: Monitor and optimize storage usage
5. **Performance Tuning**: Reduce monitoring overhead
### Monitoring for the Monitoring System
1. **Self-monitoring**: Monitor the monitoring infrastructure
2. **Health Checks**: Regular health checks on monitoring components
3. **Failover Testing**: Test monitoring system resilience
4. **Capacity Planning**: Ensure monitoring system scalability