v0.2.00: Foundation & Multiple OpenTelemetry Metric Types

[laplaque]

Epic: Custom Metrics Collection and Registration

Epic Issue: #20

Overview

Establish the core infrastructure and support for all OpenTelemetry metric types beyond the current Observable Gauge limitation.

Deliverables

1. Enhanced OTel Connector (common/otel_connector.py)

• Add support for Counter, UpDownCounter, Histogram, and Observable Gauge
• Implement metric type validation and appropriate creation methods
• Add error handling for unsupported metric configurations

2. Metric Source Base Classes (common/metric_sources/)

• MetricSourceBase - Abstract base class for all metric sources
• StaticMetricSource - For simple value-based metrics
• CallbackMetricSource - For function-based metric collection

3. Basic Metric Source Registry (common/metric_registry.py)

• Registration and discovery system for metric sources
• Type checking and validation framework
• Basic lifecycle management (register/unregister)

4. Enhanced Configuration Support

• Extend toml_utils.py to parse [custom_metrics] sections
• Add validation for metric type and source configurations
• Implement configuration merging (defaults + custom)

5. Updated Base Sensor Integration

• Modify base_sensor.py to use registry-based metric collection
• Maintain backward compatibility with existing process metrics
• Add hooks for custom metric integration

Configuration Example

[custom_metrics]
enabled = true

[[custom_metrics.metrics]]
name = "heap_memory"
source_type = "static"
otel_type = "gauge"
unit = "bytes"
value_callback = "get_heap_memory"

[[custom_metrics.metrics]]
name = "request_count"
source_type = "static"
otel_type = "counter"
unit = "1"
value_callback = "get_request_count"

Success Criteria

• All 4 OpenTelemetry metric types supported and tested
• Plugin architecture foundation established
• Backward compatibility maintained
• 80%+ test coverage for new components
• Performance impact < 5% vs current implementation

Milestone

Target: v0.2.00