Understanding and troubleshooting Python processes that return "Killed"

For many Python developers and engineers, there’s a familiar yet frustrating situation where a process abruptly stops and simply returns “Killed” in the terminal. This ambiguous response, often given without further information, can be perplexing. However, in most cases, a process is killed due to memory limitations or system restrictions. In this article, we’ll look at common reasons why Python processes get terminated this way, and how to identify and address the underlying causes.

Why a process might be "Killed"

When a Python process is killed without further explanation, it’s typically due to the Out Of Memory (OOM) Killer—an operating system function that automatically terminates processes when memory limits are exceeded. In Linux, this occurs when the system’s RAM and swap space are full, and the kernel has no choice but to terminate memory-intensive processes to keep the system running. Processes with large memory footprints, such as those involving big data processing, machine learning models, or web servers, are most vulnerable.

Other potential causes for the “Killed” message can include reaching set limits on CPU time, virtual memory, or data size. These limits can be imposed by the system’s configuration, resource managers like ulimit, or cloud environments with resource constraints.

I recently encountered this while developing an AI application. My local development environment has a hefty 96GB of RAM, allowing me to run memory-intensive processes with ease and speed. However, when deploying on an Ubuntu server with only 7GB of RAM, the same processes struggled, frequently terminating unexpectedly. This is a common pitfall when moving applications from development environments with ample resources to more constrained production or testing environments. It highlights the importance of resource profiling, testing under realistic server conditions, and applying optimisations to ensure memory-intensive applications can run smoothly across diverse environments.

Step-by-step troubleshooting

If you encounter the “Killed” message, here’s how to investigate further and pinpoint the root cause.

1. Check system logs for out-of-memory events

The system’s kernel logs can reveal if the process was terminated by the OOM Killer. You can check these logs using the dmesg command to see recent kernel messages, or look in /var/log/syslog or /var/log/messages for additional clues.

sudo dmesg | tail -50

Look for messages like “Out of memory” or “Killed process” that indicate the OOM Killer was involved. If you see logs indicating that a Python process was killed due to memory constraints, you can be fairly certain that memory was the issue.

2. Monitor memory usage during execution

Running commands like top or htop during your process’s execution can give you real-time insights into memory consumption. Look at how much memory the process uses, particularly if it climbs close to your system’s RAM and swap limits. This is especially useful if the process only crashes after running for some time.

To launch htop, use:

htop

Within htop, observe the memory (MEM%) and swap (SWP%) sections. If these values approach 100%, the system is likely running out of memory, and the process may be killed.

3. Check swap space availability

If your server has low swap space, the system may run out of memory sooner than expected, prompting the OOM Killer. Swap space acts as overflow memory, allowing the system to continue functioning when physical RAM is low. To check your current swap space, use:

free -m

If swap space is low, adding more can help the system handle memory peaks without killing processes. You can add swap space by creating a swap file and enabling it with commands such as:

sudo fallocate -l 4G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile

To make this change permanent, add the swap file entry to /etc/fstab:

/swapfile swap swap defaults 0 0

4. Review ulimit settings

The ulimit command allows you to manage system resource limits for individual users and processes. Sometimes, these limits can be set too low, inadvertently causing processes to fail. To view your current limits, use:

ulimit -a

Check values such as max memory size (for memory limits), max CPU time (for time limits), and max data size. If these are set too low, consider adjusting them by editing /etc/security/limits.conf or by using ulimit commands for specific processes.

5. Review application code for memory inefficiencies

If your process routinely consumes too much memory, the code itself may be the source of the issue. Consider using memory-efficient data structures, such as generators instead of lists or reducing the use of large variables by keeping them within narrow scopes (e.g., within functions). These adjustments can help manage memory better, especially in applications processing large datasets.

Practical solutions to avoid unexpected kills

1. Use memory-efficient libraries: Libraries like pandas and numpy are excellent for data processing, but they can also consume significant memory. Look for more memory-efficient alternatives, or consider processing data in chunks if feasible.
2. Run the process with a memory profiler: Use tools like memory-profiler or guppy3 to identify which parts of the code are using the most memory. This helps you pinpoint potential inefficiencies and make targeted improvements.
3. Reduce parallel processing if needed: While using multiple threads or processes can improve performance, it also increases memory usage. If memory is an issue, consider reducing the degree of parallelism.
4. Set up alerts for memory usage: Monitoring tools like Prometheus, Grafana, or Datadog can alert you when memory usage is approaching critical levels, allowing you to take action before the process is killed.

Moving forward: Building resilient Python applications

Handling memory issues requires a mix of proactive and reactive approaches. By monitoring system resources, optimising code, and understanding the specific memory demands of your Python applications, you can minimise the chances of encountering a “Killed” message. Moreover, these strategies are particularly valuable in production environments, where memory efficiency translates into lower costs and improved performance.

In many cases, the steps outlined above will reveal why the system killed your process and guide you to the adjustments needed to avoid future issues. Whether it’s adding swap space, adjusting system limits, or refactoring code, each action you take will strengthen your application and improve its resilience under heavy load.