91 lines
3 KiB
Markdown
91 lines
3 KiB
Markdown
# Logging & Monitoring
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## Overview
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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.
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## Planned Features
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### Centralized Logging
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- **Log Aggregation**: Collection of logs from all services
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- **Structured Logging**: Standardized log format across services
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- **Log Processing**: Parsing, enrichment, and transformation
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- **Log Storage**: Efficient storage with retention policies
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- **Log Search**: Advanced search capabilities with indexing
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### Metrics Collection
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- **System Metrics**: CPU, memory, disk, network usage
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- **Application Metrics**: Custom application-specific metrics
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- **Business Metrics**: Trading and performance indicators
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- **SLI/SLO Tracking**: Service level indicators and objectives
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- **Alerting Thresholds**: Metric-based alert configuration
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### Distributed Tracing
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- **Request Tracing**: End-to-end tracing of requests
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- **Span Collection**: Detailed operation timing
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- **Trace Correlation**: Connect logs, metrics, and traces
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- **Latency Analysis**: Performance bottleneck identification
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- **Dependency Mapping**: Service dependency visualization
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### Alerting & Notification
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- **Alert Rules**: Multi-condition alert definitions
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- **Notification Channels**: Email, SMS, chat integrations
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- **Alert Grouping**: Intelligent alert correlation
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- **Escalation Policies**: Tiered notification escalation
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- **On-call Management**: Rotation and scheduling
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## Planned Integration Points
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### Data Sources
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- All platform microservices
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- Infrastructure components
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- Databases and storage systems
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- Message bus and event streams
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- External dependencies
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### Consumers
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- Operations team dashboards
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- Incident management systems
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- Capacity planning tools
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- Automated remediation systems
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## Planned Technical Implementation
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### Technology Stack
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- **Logging**: ELK Stack (Elasticsearch, Logstash, Kibana) or similar
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- **Metrics**: Prometheus and Grafana
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- **Tracing**: Jaeger or Zipkin
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- **Alerting**: AlertManager or PagerDuty
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- **Collection**: Vector, Fluentd, or similar collectors
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### Architecture Pattern
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- Centralized collection with distributed agents
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- Push and pull metric collection models
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- Sampling for high-volume telemetry
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- Buffering for resilient data collection
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## Development Guidelines
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### Instrumentation Standards
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- Logging best practices
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- Metric naming conventions
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- Trace instrumentation approach
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- Cardinality management
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### Performance Impact
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- Sampling strategies
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- Buffer configurations
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- Resource utilization limits
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- Batching recommendations
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### Data Management
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- Retention policies
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- Aggregation strategies
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- Storage optimization
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- Query efficiency guidelines
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## Implementation Roadmap
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1. Core logging infrastructure
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2. Basic metrics collection
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3. Critical alerting capability
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4. Distributed tracing
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5. Advanced analytics and visualization
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