# Logging & Monitoring

## Overview
The Logging & Monitoring service will provide comprehensive observability capabilities for the stock-bot platform. It will collect, process, store, and visualize logs, metrics, and traces from all platform components, enabling effective operational monitoring, troubleshooting, and performance optimization.

## Planned Features

### Centralized Logging
- **Log Aggregation**: Collection of logs from all services
- **Structured Logging**: Standardized log format across services
- **Log Processing**: Parsing, enrichment, and transformation
- **Log Storage**: Efficient storage with retention policies
- **Log Search**: Advanced search capabilities with indexing

### Metrics Collection
- **System Metrics**: CPU, memory, disk, network usage
- **Application Metrics**: Custom application-specific metrics
- **Business Metrics**: Trading and performance indicators
- **SLI/SLO Tracking**: Service level indicators and objectives
- **Alerting Thresholds**: Metric-based alert configuration

### Distributed Tracing
- **Request Tracing**: End-to-end tracing of requests
- **Span Collection**: Detailed operation timing
- **Trace Correlation**: Connect logs, metrics, and traces
- **Latency Analysis**: Performance bottleneck identification
- **Dependency Mapping**: Service dependency visualization

### Alerting & Notification
- **Alert Rules**: Multi-condition alert definitions
- **Notification Channels**: Email, SMS, chat integrations
- **Alert Grouping**: Intelligent alert correlation
- **Escalation Policies**: Tiered notification escalation
- **On-call Management**: Rotation and scheduling

## Planned Integration Points

### Data Sources
- All platform microservices
- Infrastructure components
- Databases and storage systems
- Message bus and event streams
- External dependencies

### Consumers
- Operations team dashboards
- Incident management systems
- Capacity planning tools
- Automated remediation systems

## Planned Technical Implementation

### Technology Stack
- **Logging**: ELK Stack (Elasticsearch, Logstash, Kibana) or similar
- **Metrics**: Prometheus and Grafana
- **Tracing**: Jaeger or Zipkin
- **Alerting**: AlertManager or PagerDuty
- **Collection**: Vector, Fluentd, or similar collectors

### Architecture Pattern
- Centralized collection with distributed agents
- Push and pull metric collection models
- Sampling for high-volume telemetry
- Buffering for resilient data collection

## Development Guidelines

### Instrumentation Standards
- Logging best practices
- Metric naming conventions
- Trace instrumentation approach
- Cardinality management

### Performance Impact
- Sampling strategies
- Buffer configurations
- Resource utilization limits
- Batching recommendations

### Data Management
- Retention policies
- Aggregation strategies
- Storage optimization
- Query efficiency guidelines

## Implementation Roadmap
1. Core logging infrastructure
2. Basic metrics collection
3. Critical alerting capability
4. Distributed tracing
5. Advanced analytics and visualization