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Build a lightweight monitor in two layers: add a small health endpoint to your Python service, then run a separate check that polls it and sends an alert only when a failure persists. Use liveness to report whether the process is running and readiness to report whether it can serve traffic. Add Prometheus and an external probe when you need history, broader visibility, or checks that do not depend on the application reporting its own condition.
Choose what the monitor needs to detect
A health endpoint is only useful when its meaning is clear. A liveness check answers whether the process is alive; a readiness check answers whether it can handle useful requests, including whether required dependencies are available. Neither automatically proves that users can reach the service through DNS, a proxy, or their own network.
For a small deployment, begin with a cheap endpoint and an independent polling loop. Keep the first version focused on the service’s most important failure modes. Add metrics collection and external probes only if they answer a real operational question.
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The route and response format below are implementation choices, not a Prometheus-defined schema. Adapt the names and framework to your application. Keep the liveness route independent of slow dependency calls; otherwise a temporary database issue can make a healthy process appear dead. Put dependency checks in readiness, and give them sensible timeouts.
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- Easy to use, no configuration required, plug and play (for new and configuration unchanged raspberry pi systems). Instructions was provided.
from flask import Flask, jsonify
app = Flask(__name__)
def required_dependencies_ready():
# Replace this with fast, bounded checks for dependencies
# that must work before this service can accept traffic.
return True
@app.get("/health/live")
def live():
return jsonify(status="ok"), 200
@app.get("/health/ready")
def ready():
if not required_dependencies_ready():
return jsonify(status="not_ready"), 503
return jsonify(status="ready"), 200Do not return credentials, internal exception details, or customer data in health responses. If the service has no dependencies that determine readiness, the readiness route can simply report that the application is ready.
Poll the endpoint and alert on sustained failures
A minimal monitor can make an HTTP request on a fixed interval, track consecutive failures, and notify when a configured threshold is reached. The example uses environment variables for the target and webhook destination; replace the notification function with an approved email, chat, or incident-management integration. Install the HTTP dependency with python -m pip install requests.
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import os
import time
import requests
HEALTH_URL = os.environ["HEALTH_URL"]
ALERT_WEBHOOK = os.environ["ALERT_WEBHOOK"]
INTERVAL_SECONDS = int(os.getenv("INTERVAL_SECONDS", "15"))
FAILURES_TO_ALERT = int(os.getenv("FAILURES_TO_ALERT", "3"))
failures = 0
alert_open = False
def send_alert(message):
response = requests.post(
ALERT_WEBHOOK,
json={"text": message},
timeout=5,
)
response.raise_for_status()
while True:
try:
response = requests.get(HEALTH_URL, timeout=3)
response.raise_for_status()
failures = 0
if alert_open:
send_alert(f"RECOVERED: {HEALTH_URL} is healthy again")
alert_open = False
except requests.RequestException as exc:
failures += 1
if failures >= FAILURES_TO_ALERT and not alert_open:
send_alert(
f"ALERT: {HEALTH_URL} failed {failures} checks; "
f"last error: {type(exc).__name__}"
)
alert_open = True
time.sleep(INTERVAL_SECONDS)This is intentionally small, but it needs operational care before production use. A process restart clears its in-memory failure count and alert state; a long-running production monitor should persist or externalize state if restart behavior matters. Configure request and webhook timeouts, prevent overlapping runs, and decide how to retry or report notification failures. Ensure the webhook message includes the affected service and a useful diagnostic destination without leaking secrets. Tune the poll interval and failure count to balance detection speed against transient blips and check load; there is no universal threshold.
Decide between built-in checks and Prometheus
A standalone loop is enough when you need a simple up/down signal and can operate the polling and notification process yourself. Prometheus adds historical metrics, rule evaluation, and a dedicated alert-routing layer, at the cost of more components to configure and maintain.
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| Approach | What it provides | Trade-off |
|---|---|---|
| Python health route plus polling loop | Simple liveness/readiness checks and direct notification, implemented in the service and monitor. | Minimal setup, but history, routing, and monitor reliability are your responsibility. |
| Python client plus Prometheus | Metrics exposed by the application, scraped and retained by Prometheus; rules can alert on symptoms. | Provides metrics and rule-based alerting but requires Prometheus configuration and operations. Prometheus scrape intervals are configurable, not fixed requirements. |
| Prometheus plus Blackbox Exporter | External HTTP, HTTPS, DNS, TCP, ICMP, or gRPC probes, with success and timing metrics. | Tests reachability from the probe’s vantage point, but adds another service to deploy and maintain. |
Add metrics with the official Python client
If you want time-series data rather than only pass/fail notifications, install the official Prometheus Python client with python -m pip install prometheus-client. Its quick-start documentation demonstrates defining a metric and starting an HTTP metrics server. The client also exposes Linux process metrics, including CPU and memory information.
Prometheus collects by scraping configured metrics endpoints. Follow the Prometheus getting-started guide to configure a target. Its sample configuration uses a 15-second global scrape interval and a 5-second interval for an example target; those are illustrative settings, not recommended values for every service. Choose the interval and timeout to match the detection delay you need and the cost of running checks.
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Use application metrics to describe symptoms such as request errors or latency, rather than trying to encode every internal detail. Keep metric labels low-cardinality: do not use user IDs, request IDs, or other values that can create an unbounded number of time series.
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Add an external probe for the user-facing path
An in-process route cannot prove that the entire service is reachable from outside its process. Use an external check when you need to test a public URL, DNS resolution, a network port, or another path users rely on. Prometheus Blackbox Exporter supports HTTP, HTTPS, DNS, TCP, ICMP, and gRPC probes; its results include probe_success and timing metrics.
Best Value
- 5-inch 800*480 resolution capacitive touch screen, IPS type, good viewing angle.
- The MIPI DSI interface directly outputs, plug and play, no driver installation required.
- As a touchscreen monitor, compatible with Raspberry Pi 5 / 4B / 3B+ / 3B / 3A+ / 2B / 1B+ / 1A+. (No HDMI. Not compatible with any other devices.)
- Supports for Raspbian OS 2 points to zoom the page(old version), for Ubuntu/Kali/Win10 IoT (single-touch only). Support PWM backlight brightness adjustment.
- Easy to use -> No configuration required (for new and configuration unchanged systems). Provide detailed usage documentation.
Place the probe where it can observe the boundary you care about. A probe inside the same network may not detect an internet-facing routing issue; a probe outside the application host may detect reachability problems the application’s own health route cannot report. Keep internal readiness checks and external probes complementary rather than treating either as complete coverage.
Configure actionable alerts and test delivery
Prometheus recommends keeping alerting simple, focusing on symptoms, and avoiding pages that require no action. In practice, alert on sustained failed checks, excessive latency, or elevated error rates that represent user impact. Include the service, condition, how long it has persisted, and where to investigate. Select thresholds using the service’s normal behavior and the cost of delayed detection.
With Prometheus, alert rules are evaluated by Prometheus and sent to Alertmanager. The Alertmanager documentation describes routing and receiver configuration; the Prometheus tutorial includes examples for email, webhooks, PagerDuty, and Slack. A direct webhook in a small script is simpler, while Alertmanager is useful when you need centralized routing or grouping across alerts.
Test the whole chain: cause a controlled check failure, confirm that the rule or polling loop detects it, and verify that the intended recipient receives a usable notification. Alertmanager documents GET and HEAD /-/healthy and GET and HEAD /-/ready; the health endpoint returns HTTP 200, while readiness returns 200 when it is ready to respond to queries. Monitoring the monitor and notification path helps expose failures that could otherwise hide an outage.
Protect endpoints and configuration
Metrics and health responses can reveal operational details. Restrict access to trusted networks or apply appropriate authentication and network controls. The Python client’s HTTP exposition server uses HTTP by default; its HTTP server documentation explains HTTPS configuration with a certificate and key, as well as optional mutual TLS requiring a client certificate.
Quick Recap
- Keep endpoints, webhook destinations, credentials, and thresholds in environment configuration or a secret manager rather than source code.
- Avoid secrets or sensitive customer data in metric labels, health responses, and alert text.
- Set explicit timeouts for dependency checks, health polling, and notification requests.
- Expose only the endpoints that the monitor needs, and consider separating readiness information from public-facing routes.
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