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

Core logging infrastructure
Basic metrics collection
Critical alerting capability
Distributed tracing
Advanced analytics and visualization

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