Build a lightweight monitor in layers: add a fast health endpoint to your Python service, poll it from a separate process, and notify someone only when a failure persists. Use liveness to report whether the process is running and readiness to report whether it can serve traffic. If you need metrics history or checks from outside the service, add Prometheus and its exporters rather than making the health endpoint do everything.
Choose the checks your monitor needs
A health check is useful only when you know what boundary it tests. An endpoint inside the application can report process state and critical dependency readiness, but a successful response does not prove that DNS, a reverse proxy, an outside network path, or the full user journey works.
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| Approach | What it checks | What it adds |
|---|---|---|
| In-process health endpoint | Application state and, if you implement it, readiness of required dependencies. | Little infrastructure; the application must remain able to answer the check. |
| External HTTP check | A service request as seen from the monitoring host, including network reachability and the response it receives. | Detects some failures invisible to an internal endpoint, but still tests only the chosen path. |
| Prometheus with Python metrics | Metrics exposed by the process and collected by Prometheus. | Time-series history and rule-based alerting, plus a Prometheus server to configure and operate. |
| Prometheus Blackbox Exporter | External HTTP, HTTPS, DNS, TCP, ICMP, or gRPC probes; results include success and timing metrics. | External probing through a separately operated exporter. |
These approaches can be combined. Keep a cheap internal endpoint for application state and add external checks for the network or service behavior users depend on.
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Use separate paths or clearly separate fields for liveness and readiness. Liveness should answer whether the process is running; readiness should answer whether it can handle useful traffic. A minimal JSON response might look like {"status":"ready"}, with an HTTP 200 response when ready and a non-2xx status when not ready. That payload and status-code choice are implementation decisions, not a Prometheus-mandated schema.
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Make the check fast and predictable. Include only dependencies whose failure means the service cannot do its job, and use short timeouts for dependency checks. Avoid expensive queries or work that can cause a failing dependency to slow every health request. If a required dependency is unavailable, readiness can fail while liveness remains successful; restarting a healthy process will not necessarily repair an outage elsewhere.
Poll the endpoint and alert on sustained failure
For a small deployment, a separate Python process can poll the health URL, apply a timeout, count consecutive failures, and send a notification when the chosen failure condition is met. Keeping the poller outside the application process matters: if the application is stuck or unavailable, an in-process loop cannot reliably report that fact.
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- Configure the target and timeout. Keep the endpoint and credentials outside source code where appropriate. Choose a timeout short enough to detect a stalled request without treating ordinary response variation as an outage.
- Poll at a deliberate interval. Balance the detection delay you can accept against the load generated by checks. No single interval suits every service.
- Require persistence before notifying. Alert after a sustained failure or repeated failed checks rather than sending a page for every missed poll. Decide how recovery is detected and whether to send a recovery notice.
- Include actionable context. Identify the service, the observed condition, how long it has lasted, and where to investigate or what action to take.
- Test delivery end to end. Trigger a controlled test and verify that the intended recipient receives it. A passing application health check does not prove that notification delivery works.
Prometheus recommends keeping alerting simple, focusing on symptoms that matter to users, allowing slack for brief blips, and avoiding pages that require no action. Apply the same principle to a standalone poller: alert on sustained failed checks, latency, or error rates that warrant action, and choose thresholds based on normal service behavior and the cost of missing an outage—not a universal number.
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Add Prometheus when metrics and history are useful
The official Python client can expose application metrics over HTTP. Install it with pip install prometheus-client; its quick start shows how to start an HTTP metrics server and define a metric such as a Summary. The client also documents Linux process metrics, including CPU and memory information. See the Prometheus Python client documentation.
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Prometheus collects metrics by scraping configured endpoints. Its getting-started configuration uses a 15-second global scrape interval and a 5-second interval for its example target; those are example settings, not recommended defaults for every service. Set scrape intervals and timeouts to match the detection delay you need and the work your checks impose. The Prometheus getting-started guide explains the collection setup.
In this arrangement, the Python application exposes metrics, Prometheus stores and evaluates them, and alert rules identify conditions worth action. For a service expected to be reachable from outside its process, the Blackbox Exporter guide describes probing common network protocols and the resulting probe_success and timing metrics.
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Route alerts and check the monitoring path
Prometheus evaluates alert rules and sends firing alerts to Alertmanager, which can route notifications to receivers such as email, webhooks, PagerDuty, or Slack. The examples are documented in the Prometheus alerting overview. Routing and grouping can be useful when several services or teams share alert infrastructure; for a single small monitor, a direct notification path may require less setup.
Monitoring infrastructure can fail independently of the application. Alertmanager documents GET and HEAD /-/healthy health checks, which return HTTP 200, and GET and HEAD /-/ready readiness checks, which return HTTP 200 when it is ready to respond to queries. See the Alertmanager management API. Also test that the monitoring system can reach its targets and that a test notification reaches its destination.
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Protect metrics and keep alerts useful
The Python client’s HTTP exposition server accepts HTTP by default. Its documentation describes HTTPS configuration with a certificate and key, with optional mutual TLS that requires a client certificate. If metrics are reachable beyond a trusted local network, restrict access and select transport security appropriate to the deployment; TLS alone does not replace network access controls. See the Python client HTTPS documentation.
Quick Recap
- Keep endpoint addresses, credentials, thresholds, and notification destinations in configuration rather than embedding secrets in code.
- Do not put secrets or sensitive customer data in metric labels or alert messages.
- Give each check one clear job: process liveness, readiness, or an external user-visible path.
- Keep the initial alert set small and add rules only when they lead to a useful response.
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