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Find out what stopped
Before changing restart settings, capture the last application log lines, the process exit status or termination signal, the service or container state, and relevant deployment events. These clues separate four different problems:
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- The Python process exited. Check its logs and exit status to see whether it completed normally or failed.
- The container stopped or restarted. Check the container’s state and restart history as well as the Python logs.
- An operator or deployment stopped it deliberately. A manual stop can be treated differently from a failure by the supervisor.
- The process is alive but unresponsive. A lifecycle restart policy may not notice a deadlock or stalled work unless a health check is configured.
The cause cannot be identified without the deployment details and logs. Exceptions, memory exhaustion, terminal logout, network loss, and other possibilities should be treated as hypotheses until the evidence points to one.
Choose the supervisor that matches the deployment
Give restart ownership to the layer that actually manages the service. Avoid stacking independent supervisors over the same process or container: competing restart decisions can make behavior difficult to predict. Docker explicitly warns against combining its restart policies with a host-level process manager for the same containers (Docker: Start containers automatically).
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| Where it runs | Lifecycle supervisor | Health behavior | Important caveat |
|---|---|---|---|
| Linux host service | systemd restart and, if configured, watchdog settings | A watchdog can detect missed keep-alive notifications | A deliberate service stop is not automatically undone. Choose behavior based on whether a clean exit should restart. |
| Docker container | Docker restart policy | Responds to container exits and daemon lifecycle | Policy activation and manual-stop behavior matter; do not layer a host manager over the same container’s Docker restart policy. |
| Kubernetes Pod | Pod restart policy and kubelet probes | Startup, liveness, and readiness probes have distinct actions | A poorly designed liveness check can trigger cascading failures; readiness failure removes traffic without restarting the process. |
Linux service: use systemd
For a Python program installed as a Linux service, systemd can restart a process according to its exit or termination behavior. Restart=on-failure covers nonzero exits, certain signal terminations, timeouts, and watchdog expiry. Restart=always also covers clean exits, so it is unsuitable if a clean exit means the work is finished. A deliberate systemctl stop is not automatically reversed by the restart setting. See the systemd.service documentation for current unit behavior.
Watchdog recovery requires more than enabling a setting: the service must send keep-alive notifications within the configured deadline. If restarts repeat, inspect the journal and configure the unit’s start-rate behavior so a failure loop is visible and bounded rather than silently obscuring the original fault.
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Docker container: choose one Docker restart policy
Docker provides no, on-failure, always, and unless-stopped. They differ in how they treat clean exits, manually stopped containers, and daemon restarts; select the one that matches the intended lifecycle rather than choosing the broadest option by default. Docker activates a restart policy only after it has observed the container running successfully for at least 10 seconds, according to its current documentation (Docker: Start containers automatically).
If the foreground command you used to attach to a container exits, that does not necessarily mean Docker stopped restarting the container. Check container status and logs to establish what happened rather than relying only on the terminal session.
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Kubernetes Pod: distinguish restart policy from probes
Kubernetes restart policy governs what happens after a container exits; probes let the kubelet assess whether it has started, is alive, or should receive traffic. A failed liveness probe eventually causes the kubelet to kill and restart the container under the Pod’s restart policy. Readiness failure marks the Pod not ready and removes it from service traffic without restarting the process. A startup probe gives a slow-initializing application time to start before liveness checks take effect. See Kubernetes: Liveness, Readiness, and Startup Probes.
Design health checks around the action they control
A probe should be inexpensive and test a condition relevant to its consequence. Use liveness for an internal failure from which restarting can help, readiness for whether the instance should receive traffic, and startup to protect legitimate initialization time. If a dependency is unavailable, the instance may need to stop receiving traffic while remaining healthy enough that restarting it would not solve the problem.
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Kubernetes warns that incorrect liveness probes can cause cascading failures. Its documented defaults are a periodSeconds of 10 seconds, timeoutSeconds of 1 second, failureThreshold of 3, and successThreshold of 1. These are defaults, not universal recommendations; tune them to observed startup and recovery behavior. For applications with lengthy initialization, use a startup probe rather than setting liveness thresholds so aggressively that healthy startup is mistaken for failure.
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Keep evidence through restarts
Automatic recovery is useful only if you can still find out why it happened. Log startup and shutdown, health-state changes, exceptions, and relevant context with Python’s standard logging facility. Send logs somewhere that persists across process or container restarts; otherwise, a restart loop can erase the evidence you need.
If a crash appears to involve a segmentation fault or other failure in native code, Python’s faulthandler documentation describes how to print Python and C stack traces. It is a diagnostic aid for native failures, not a fix for ordinary Python exceptions or missing process supervision.
Handle shutdown signals without blocking
Python runs signal handlers in the main thread of the main interpreter, even when a signal arrives in another thread, as the Python 3.14.8 signal documentation explains. Keep handlers minimal: avoid lock-based work or slow cleanup inside them, because locks in a signal handler can deadlock. Arrange for cleanup to happen in the normal application flow and ensure it cannot block indefinitely. The supervisor should own the decision to restart after the process exits.
Quick Recap
Use a diagnostic sequence before tuning restart behavior
- Collect evidence: save the final application logs, exit status or signal, service or container state, and deployment events.
- Classify the event: decide whether the process exited, the container restarted, a deliberate stop occurred, or the process is alive but unhealthy.
- Check the owning supervisor: inspect systemd status and journal, Docker container state and logs, or Kubernetes Pod events and probe results, as appropriate.
- Apply one recovery mechanism: configure the supervisor for the failure behavior you want, and add health checks only when they measure meaningful conditions.
- Verify the result: confirm that expected failures recover, deliberate stops behave as intended, and logs survive restarts.
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