v0.4.00: Dynamic Plugin System & Advanced Features

[laplaque]

Epic: Custom Metrics Collection and Registration

Epic Issue: #20
Depends on: #23 (v0.3.00: Basic Built-in Metric Sources)

Overview

Implement dynamic loading capabilities, advanced metric aggregation, and enhanced configuration management for production readiness.

Deliverables

1. Dynamic Plugin Loader (common/plugin_loader.py)

• Runtime discovery and loading of custom metric source plugins
• Plugin validation and compatibility checking
• Secure plugin sandboxing and error isolation
• Plugin lifecycle management (load/unload/reload)
• Support for both Python modules and external executables

2. Advanced Metric Aggregation (common/aggregators/)

• Time-window aggregation (sliding windows, tumbling windows)
• Statistical aggregations (percentiles, moving averages)
• Cross-metric calculations and derived metrics
• Memory-efficient aggregation algorithms
• Configurable aggregation rules via TOML

3. Enhanced Configuration Management

• Configuration hot-reloading without restart
• Environment variable interpolation
• Configuration inheritance and composition
• Validation with detailed error reporting
• Configuration migration utilities

4. Database Integration Source (common/metric_sources/database_source.py)

• SQL query execution for metric collection
• Support for PostgreSQL, MySQL, SQLite, SQL Server
• Connection pooling and query optimization
• Parameterized queries for security
• Query result transformation and mapping

5. Production Features

• Circuit breaker pattern for failing sources
• Graceful degradation and fallback mechanisms
• Comprehensive logging and debugging tools
• Performance monitoring and bottleneck detection
• Memory usage optimization

Configuration Example

[custom_metrics]
plugin_directories = ["/opt/plugins", "./local_plugins"]
aggregation_window = "5m"

[[custom_metrics.sources]]
name = "database_metrics"
type = "database"
connection_string = "postgresql://user:pass@localhost/db"
interval = 60
queries = [
    {name = "active_users", sql = "SELECT COUNT(*) FROM users WHERE last_login > NOW() - INTERVAL '1 hour'", otel_type = "gauge"},
    {name = "transaction_volume", sql = "SELECT COUNT(*) FROM transactions WHERE created_at > $1", params = ["1h_ago"], otel_type = "counter"}
]

[[custom_metrics.aggregators]]
source = "api_health.response_time"
type = "percentile"
percentiles = [50, 95, 99]
window = "5m"

Success Criteria

• Dynamic plugin system fully functional
• Production-grade reliability features
• Advanced aggregation capabilities
• 90%+ test coverage
• Performance optimizations documented
• Migration path from v0.3.00 established

Milestone

Target: v0.4.00