fastapi-metrics 0.3.13

Zero-config metrics for FastAPI apps - perfect for solo devs and MVPs

FastAPI Metrics - Project Specification

Project Vision

Zero-config metrics and observability for FastAPI apps targeting indie devs, startups, and no-code platforms. No Prometheus/Grafana infrastructure required.

Core Value Proposition

• 5-minute setup: One import, one line of code
• No infrastructure: SQLite/memory storage, no containers
• Business metrics: Track revenue, users, features - not just HTTP metrics
• Query API: JSON endpoints for custom dashboards
• Cost tracking: Auto-detect OpenAI/Anthropic API costs
• No-code ready: Retool/Bubble can consume the API directly

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Target Users

1. Indie developers building MVPs
2. Early-stage startups (pre-Series A)
3. No-code platform backends (Bubble, Retool, Make.com)
4. Developers prototyping without DevOps resources

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Technical Architecture

Package Structure

fastapi-metrics/
├── fastapi_metrics/
│   ├── __init__.py
│   ├── core.py              # Main Metrics class
│   ├── middleware.py        # Request tracking middleware
│   ├── storage/
│   │   ├── base.py          # Storage interface
│   │   ├── memory.py        # In-memory storage
│   │   ├── sqlite.py        # SQLite storage
│   │   └── redis.py         # Redis storage (optional)
│   ├── collectors/
│   │   ├── http.py          # HTTP request metrics
│   │   ├── system.py        # CPU, memory, disk
│   │   ├── business.py      # Custom business metrics
│   │   └── cost.py          # LLM API cost tracking
│   ├── health/
│   │   ├── checks.py        # Health check implementations
│   │   └── endpoints.py     # Health endpoints
│   ├── query.py             # Metrics query engine
│   └── exporters/
│       ├── json.py          # JSON export
│       ├── prometheus.py    # Prometheus format
│       └── csv.py           # CSV export
├── tests/
├── examples/
├── docs/
└── pyproject.toml

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Phase 1: Core Functionality ⏳

1.1 Basic Setup & Middleware

• ✅ Main Metrics class initialization
• ✅ FastAPI app integration
• ✅ Request tracking middleware
• ✅ Response time calculation
• ✅ Status code tracking

1.2 Storage Backends

• ✅ Abstract storage interface
• ✅ In-memory storage (dict-based)
• ✅ SQLite storage with tables:
  • http_requests (timestamp, endpoint, method, status, latency_ms)
  • custom_metrics (timestamp, metric_name, value, labels)
  • system_metrics (timestamp, cpu, memory, disk)

1.3 HTTP Metrics Collection

• ✅ Requests per endpoint
• ✅ Requests per status code
• ✅ Requests per method
• ✅ Latency percentiles (p50, p95, p99)
• ✅ Error rate calculation
• ✅ Active requests counter

1.4 Query API Endpoints

• ✅ GET /metrics - Current snapshot (JSON)
• ✅ GET /metrics/query - Time-series queries
  • Query params: from, to, metric, group_by, endpoint
• ✅ GET /metrics/endpoints - Per-endpoint stats
• ✅ GET /metrics/export?format=csv|prometheus

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Phase 2: Health Checks & System Metrics ⏳

2.1 Health Check System

• ✅ Health check base class
• ✅ Built-in checks:
  • ✅ Database connectivity
  • ✅ Redis connectivity
  • ✅ Disk space
  • ✅ Memory usage
  • ✅ Custom check support
• ✅ Health endpoints:
  • ✅ GET /health - Simple status
  • ✅ GET /health/live - Liveness probe
  • ✅ GET /health/ready - Readiness probe with checks

2.2 System Metrics

• ✅ CPU usage tracking
• ✅ Memory usage tracking
• ✅ Disk usage tracking
• ✅ Uptime tracking
• ✅ System metrics endpoint

2.3 Additional

• ✅Make Redis storage first-class (not optional)
• ✅ Add proper K8s health checks (Phase 2 should be Phase 1)
• ✅ Add deployment examples for multi-instance setups
• ✅ Position as "lightweight alternative to Prometheus that scales"

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Phase 3: Business Metrics & Cost Tracking ⏳

3.1 Custom Business Metrics

• ✅ metrics.track(name, value, **labels) API
• ✅ Counter metrics
• ✅ Gauge metrics
• ✅ Histogram metrics
• ✅ Label/tag support for segmentation

3.2 LLM Cost Tracking

• ✅ Auto-detect OpenAI API calls
• ✅ Auto-detect Anthropic API calls
• ✅ Token usage tracking
• ✅ Cost calculation (using current pricing)
• ✅ Cost by model/endpoint
• ✅ GET /metrics/costs endpoint

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Phase 4: Advanced Features ⏳

4.1 Retention & Cleanup

• Configurable retention period
• Automatic data cleanup job
• Aggregation for old data (hourly → daily)

4.2 Alerting (Simple)

• Threshold-based alerts
• Webhook notifications
• Email notifications (optional)

4.3 Export Formats

• Prometheus format
• CSV export
• JSON export with timestamps

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Phase 5: Documentation & Examples ⏳

5.1 Documentation

• README with quick start
• Full API documentation
• Configuration guide
• Storage backend comparison
• Kubernetes deployment guide

5.2 Examples

• Basic FastAPI app
• With database health checks
• Custom business metrics
• LLM cost tracking
• Retool integration example
• No-code tool integration guide

5.3 Testing

• Unit tests (80%+ coverage)
• Integration tests
• Performance benchmarks

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API Design (MVP)

Basic Usage

from fastapi import FastAPI
from fastapi_metrics import Metrics

app = FastAPI()

# Minimal setup
metrics = Metrics(
    app,
    storage="sqlite://metrics.db",  # or "memory://"
    retention_hours=24,
)

# Track custom metrics anywhere
@app.post("/payment")
def payment(amount: float, user_id: int):
    metrics.track("revenue", amount, user_id=user_id)
    metrics.track("payment_count", 1)
    return {"status": "ok"}

Query Examples

GET /metrics
GET /metrics/query?metric=revenue&from=24h&group_by=hour
GET /metrics/endpoints
GET /metrics/costs
GET /health/ready

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Key Decisions

Storage Strategy

• Default: SQLite (single file, no setup)
• Optional: Redis (for distributed systems)
• Fallback: In-memory (testing/development)

Data Model

• Store raw events initially
• Aggregate on query (Phase 1)
• Pre-aggregate for performance (Phase 4)

Performance

• Async middleware (non-blocking)
• Background workers for aggregation
• Configurable batch writes
• Query result caching

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Success Metrics

• < 5 lines of code to set up
• < 1ms overhead per request
• Works with SQLite (no external deps)
• Query API returns in < 100ms
• Retool/Bubble integration works out-of-box

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Non-Goals (v1)

• ❌ Replace Prometheus/Grafana for large scale
• ❌ Distributed tracing
• ❌ Log aggregation
• ❌ APM-level profiling
• ❌ Built-in UI (JSON API only)

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Dependencies (Keep Minimal)

[dependencies]
fastapi = ">=0.100.0"
pydantic = ">=2.0.0"
aiosqlite = ">=0.19.0"  # Async SQLite
psutil = ">=5.9.0"      # System metrics
httpx = ">=0.24.0"      # For health checks (optional)

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Progress Tracker

✅ Completed

• Phase 1: Core functionality
• Phase 2: Health checks
• Phase 3: Business metrics
• Phase 4: Advanced features

🚧 In Progress

• Phase 5: Documentation

⏳ Todo

• Phase 6: Additional Features

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Quick Start Command (Future)

pip install fastapi-metrics

Development Setup (Future)

git clone https://github.com/yourusername/fastapi-metrics
cd fastapi-metrics
poetry install
poetry run pytest

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Notes & Decisions Log

• 2025-12-10: Initial spec created
• Target: Indie devs who want metrics without infrastructure
• Focus: Simplicity over enterprise features
• SQLite as default storage for zero-config experience

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