Python Q & A


How to run Python scripts as a service or daemon?

Running a Python script as a service or daemon ensures it operates in the background, starts automatically with the system, and remains running without the need for an active user session. Here’s a high-level overview of the methods and considerations for doing this:


  1. Linux Systems (Systemd): On modern Linux distributions, `systemd` is the default init system, which manages system processes. To run a Python script as a service:

   – Create a systemd service file (e.g., `my_service.service`) in `/etc/systemd/system/`.

   – In this file, specify details like the path to the Python interpreter, the path to your script, user permissions, and more.

   – Reload systemd configurations with `sudo systemctl daemon-reload`.

   – Start your service with `sudo systemctl start my_service.service` and enable it to run on boot using `sudo systemctl enable my_service.service`.


  1. Linux Systems (Upstart, SysVinit): Older Linux distributions might use Upstart or SysVinit. For these systems, different scripts and configurations are required. Tools like `supervisord` can also be used to manage and daemonize Python processes.


  1. Windows Systems: To run a Python script as a service on Windows:

   – Use tools like `NSSM` (the Non-Sucking Service Manager) or `pywin32` to create a Windows service.

   – Once set up, you can start, stop, or configure the service via the Windows Services Management Console.


  1. Cross-platform solutions:

   – Supervisord: This is a popular tool to control and monitor processes. It’s platform-agnostic, making it a versatile choice for managing Python scripts as daemons.

   – Python-daemon: A library that provides a way to implement a well-behaved Unix daemon process directly in Python code.


  1. Logging and Monitoring: When running scripts as services or daemons, interactive debugging is not possible. It’s essential to implement proper logging to capture any runtime issues. Monitoring the service’s health, restart behavior, and resource utilization will also be crucial in a production environment.


While the process for daemonizing Python scripts varies across operating systems, the key is to ensure that your script runs reliably in the background, restarts on failures, and properly logs any issues for diagnosis.

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Senior Software Engineer with 7+ yrs Python experience. Improved Kafka-S3 ingestion, GCP Pub/Sub metrics. Proficient in Flask, FastAPI, AWS, GCP, Kafka, Git