Datadog Validator

Automated validation of Datadog telemetry configuration across your services.

A pure-code validation tool that checks whether your services are properly instrumented with Datadog telemetry—validating both local configuration files and runtime telemetry visibility.

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Quick Start

# Install
git clone https://github.com/your-org/datadog-validator
cd datadog-validator
make install

# Check local configuration (no API required)
datadog-validator check-config --service my-service --service-dir ./my-service

# Validate runtime telemetry (requires Datadog API)
export DD_API_KEY="your-api-key"
export DD_APP_KEY="your-app-key"
datadog-validator validate --service my-service --envs production

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What It Does

Local Configuration Validation

• Dockerfile - Git metadata, hardcoded variables
• Helm Charts - Datadog labels, environment variables
• Dependencies - APM library versions
• Service Inference - Deprecated pattern detection

Runtime Telemetry Validation

• Metrics - Service metrics flowing to Datadog
• Logs - Log correlation configured
• Error Tracking - Error capture enabled
• APM Traces - Trace data flowing

Output Formats

• Terminal - Human-readable with colors and status indicators
• JSON - Machine-readable for CI/CD automation

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Core Principles

• Validation Only - Reports issues, never modifies files
• Pure Code - No AI, deterministic validation logic
• CI/CD First - Exit codes and JSON output for automation
• Fast - Local checks complete in <1 second

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Documentation

User Documentation

• docs/README.md - Overview and quick start
• docs/USER_GUIDE.md - Complete usage guide
• docs/INTEGRATION.md - CI/CD integration patterns
• docs/TROUBLESHOOTING.md - Common issues and solutions

Developer Documentation

• docs/ARCHITECTURE.md - System design and module boundaries
• docs/CONTRIBUTING.md - How to add validators and extend the tool
• docs/CHANGELOG.md - Version history and release notes

AI Assistant Context

• AGENTS.md - Guidance for AI assistants working with this codebase

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Real-World Results

Validation results from actual services:

communicator service:

• 77.8% pass rate (7/9 checks)
• Well-configured modern service
• Minor improvements needed

webhooks service:

• 44.4% pass rate (4/9 checks)
• Legacy configuration needing updates
• Multiple deprecated patterns identified

See examples/VALIDATION_FINDINGS.md for detailed analysis.

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Development

# Install with development dependencies
make install

# Run tests
make test

# Run linting and formatting
make check

# Run specific test
uv run pytest tests/test_dockerfile_validator.py -v

See docs/CONTRIBUTING.md for detailed development guide.

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Project Structure

datadog-validator/
├── docs/                      # Comprehensive documentation
│   ├── README.md             # Documentation overview
│   ├── USER_GUIDE.md         # Complete usage guide
│   ├── ARCHITECTURE.md       # System design
│   ├── INTEGRATION.md        # CI/CD patterns
│   ├── TROUBLESHOOTING.md    # Common issues
│   ├── CONTRIBUTING.md       # Development guide
│   └── CHANGELOG.md          # Version history
├── src/
│   └── datadog_validator/
│       ├── cli.py            # CLI commands
│       ├── api_client.py     # Datadog API client
│       ├── validators/       # Local configuration validators
│       └── output/           # Output formatters
├── tests/                    # Test suite
├── examples/                 # Real-world validation examples
├── AGENTS.md                 # AI assistant guidance
└── README.md                 # This file

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Support

• Documentation: Start with docs/README.md
• Issues: Report bugs via GitHub Issues
• Contributing: See docs/CONTRIBUTING.md

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License

MIT

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Built with the Amplifier philosophy: Ruthless simplicity, pure code solutions, and clear separation of concerns.