Metrics

The SDK offers two complementary metrics paths:

1. Prometheus RED/USE for HTTP (PrometheusMiddleware + make_prometheus_router) — listens to every request, increments http_requests_total + a latency histogram + http_requests_in_progress, and exposes it all on GET /metrics in Prometheus text format ready to be scraped by your Prometheus / Grafana / Datadog.
2. On-demand system snapshots (MetricsUtils) — collects CPU / memory / disk / NVIDIA GPU stats for a custom endpoint (internal debug page, /oncall, etc.). No built-in Prometheus exporter — the goal is the instant snapshot.

Use #1 in production always. Add #2 when you need to inspect the host where the app runs.

#1 Prometheus HTTP — [prometheus] extra

Install with [prometheus] (pulls prometheus-client). The middleware measures every request; the router serves the scrape endpoint.

uv add "tempest-fastapi-sdk[prometheus]>=0.89.0"

# src/api/app.py
from fastapi import FastAPI

from tempest_fastapi_sdk import (
    PrometheusMiddleware,
    make_prometheus_registry,
    make_prometheus_router,
)


def create_app() -> FastAPI:
    app = FastAPI(title="my-service")

    # Per-app registry — avoids collisions with other global prometheus-client users.
    registry = make_prometheus_registry()

    app.add_middleware(PrometheusMiddleware, registry=registry)
    app.include_router(make_prometheus_router(registry=registry))
    return app

Done. GET /metrics now returns something like:

# HELP http_requests_total Total HTTP requests
# TYPE http_requests_total counter
http_requests_total{method="GET",path="/api/users",status="200"} 142.0
http_requests_total{method="POST",path="/auth/signup",status="201"} 7.0
# HELP http_request_duration_seconds HTTP request latency
# TYPE http_request_duration_seconds histogram
http_request_duration_seconds_bucket{le="0.005",method="GET",path="/api/users"} 89.0
...
http_requests_in_progress{method="GET"} 2.0

Default buckets (DEFAULT_LATENCY_BUCKETS) cover 5ms → 10s — fits typical APIs. Override with PrometheusMiddleware(registry=..., latency_buckets=(0.001, 0.005, 0.025, 0.1, 0.5, 2, 10)) when your workload is more granular.

Path normalization

The path label uses the route template (/api/users/{user_id}), not the concrete path, so cardinality doesn't explode with unique UUIDs. That comes from FastAPI/Starlette — no config needed on your end.

Scrape config

prometheus.yml:

scrape_configs:
  - job_name: my-service
    metrics_path: /metrics
    static_configs:
      - targets: ["my-service:8000"]

Or via compose:

services:
  my-service:
    image: ...
    ports: ["8000:8000"]
  prometheus:
    image: prom/prometheus:latest
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
    ports: ["9090:9090"]

Business metrics — BusinessMetrics

The middleware covers HTTP (RED). For your domain metrics — orders, queue depth, job duration — use BusinessMetrics: a typed factory of Counter / Gauge / Histogram bound to the same registry, so it all shows up on the same GET /metrics.

import asyncio

from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine
from tempest_fastapi_sdk import (
    BaseRepository,
    BusinessMetrics,
    make_prometheus_registry,
)

from src.db.models import UserModel

# In a service the session comes from `db.get_session_context()`; here, SQLite.
session = AsyncSession(create_async_engine("sqlite+aiosqlite:///:memory:"))

elapsed = 0.42

repo = BaseRepository(session, model=UserModel)


registry = make_prometheus_registry()
metrics = BusinessMetrics(registry, namespace="shop")

orders = metrics.counter("orders_total", "Orders placed", labelnames=["status"])
queue = metrics.gauge("queue_depth", "Items in queue")
job = metrics.histogram("job_seconds", "Job duration", buckets=[0.1, 1, 10])

orders.labels(status="paid").inc()


async def main() -> None:
    """Run this example."""
    queue.set(await repo.count({"status": "pending"}))
    job.observe(elapsed)


asyncio.run(main())

Shows on /metrics as shop_orders_total{status="paid"}, shop_queue_depth, shop_job_seconds_bucket. Creating the same name twice returns the same metric (no Duplicated timeseries on reload/tests).

No magic

The returned objects are the real prometheus_client Counter/Gauge/Histogram — .inc() / .set() / .observe() / .labels(...) behave as upstream documents. BusinessMetrics only binds them to the registry + namespace, hiding nothing.

#2 System snapshots — [metrics] extra

MetricsUtils collects CPU, memory, disk and NVIDIA GPU usage via psutil + pynvml. Every method has a sync and an async variant (the async wrapper runs the same code via asyncio.to_thread). GPU sampling gracefully degrades to [] when pynvml or NVIDIA drivers are missing.

Install with [metrics].

# src/api/routers/system.py
from typing import Any

from fastapi import APIRouter

from tempest_fastapi_sdk import MetricsUtils

router = APIRouter()


@router.get("/system-metrics")
async def system_metrics() -> dict[str, Any]:
    """JSON snapshot. NOT the Prometheus endpoint — that one is /metrics."""
    snapshot = await MetricsUtils.snapshot_async(disk_paths=["/", "/data"])
    return snapshot.to_dict()

Don't mount this at /metrics

This endpoint is not the Prometheus one — mounting it on the same path collides with make_prometheus_router when both are active. Use /system-metrics, /admin/sysinfo, or some restricted oncall prefix.

MetricsUtils.cpu(interval=...) blocks the event loop

The sync call spends interval seconds sampling — the cpu_async wrapper avoids the block by running in a thread. Always prefer MetricsUtils.snapshot_async() from handlers.

Individual collectors

import asyncio

from tempest_fastapi_sdk import MetricsUtils


async def main() -> None:
    """Run this example."""
    snapshot = await MetricsUtils.snapshot_async(disk_paths=["/"])

    print(snapshot.cpu.percent, snapshot.memory.percent)
    for disk in snapshot.disks:
        print(disk.path, disk.percent)
    for gpu in snapshot.gpus:
        print(gpu.name, gpu.utilization_percent, gpu.memory_used_bytes)


asyncio.run(main())

Individual collectors are also available: MetricsUtils.cpu(interval=...), MetricsUtils.memory(), MetricsUtils.disk(path), MetricsUtils.disks(paths), MetricsUtils.gpus() — plus their *_async variants. Each one returns a typed dataclass (CPUMetrics, MemoryMetrics, DiskMetrics, GPUMetrics, SystemMetrics) with a to_dict() helper for JSON serialization.

One disk: use disk_async, not disks_async([path])

The two variants treat a bad path in opposite ways, and the difference shows up exactly on a metrics endpoint:

import asyncio

from tempest_fastapi_sdk import DiskMetrics, MetricsUtils


async def main() -> None:
    """Read the same missing path both ways."""
    empty = await MetricsUtils.disks_async(["/mnt/gone"])
    print(empty)  # [] — the error became a log line, the payload a gap

    try:
        disk: DiskMetrics = await MetricsUtils.disk_async("/mnt/gone")
    except FileNotFoundError as exc:
        print(f"propagated: {exc}")


asyncio.run(main())

disks_async logs and skips because the list is plural — one bad path should not take the other four down. For a single path that becomes silent absence: the dashboard shows "no disk" where there was a permission error or a mount that vanished. Callers passing several paths and wanting to fail ask for disks(paths, strict=True) (or disks_async(paths, strict=True)).

Recap

• Path #1 ([prometheus]) — PrometheusMiddleware + make_prometheus_router expose RED/USE series (http_requests_total, http_request_duration_seconds, http_requests_in_progress) on a scrape-ready GET /metrics. This is what you turn on in production.
• Path #2 ([metrics]) — MetricsUtils gives an instant CPU / memory / disk / GPU snapshot on a custom endpoint. No Prometheus exporter — it's the on-demand photo of the host.

Next steps

• Observability — how to combine metrics with request-id, structured logging, and the typed HTTPClient.
• Logging — structured logging, per-level files, and the /logs endpoint.