How To Limit Resources For Docker Containers

Resource control is crucial when running multiple containers on shared infrastructure. Without proper limits, a single container can consume all available memory or CPU, affecting system stability. This guide shows you how to limit resources for Docker Containers with real-world examples.

Why Set Resource Limits?

By default, Docker containers can use as much CPU and RAM as the host system allows. This can lead to:

• Performance degradation of other containers or host processes
• Out-of-memory (OOM) crashes
• Unpredictable behavior in production environments

Setting resource limits helps isolate workloads and maintain performance across services.

Steps To Limit Resources For Docker Containers

Example: Running a Python App With Resource Limits

Let’s use a simple Python script that simulates CPU load:

cpu_stress.py

import time

while True:
    [x**2 for x in range(10**6)]  # CPU-intensive task
    time.sleep(0.1)

Dockerfile

FROM python:3.11-slim
COPY cpu_stress.py .
CMD ["python", "cpu_stress.py"]

Build the image:

docker build -t stress-demo .

Run Without Limits (Baseline)

docker run --rm stress-demo

This container will consume as much CPU as it needs, unrestricted.

Set CPU and RAM Limits

Now, let’s restrict it:

docker run --rm \
  --cpus="0.5" \                 # Limit to half a CPU core
  --memory="100m" \             # Limit RAM to 100MB
  stress-demo

Breakdown:

• --cpus="0.5": Limits the container to 50% of one CPU core.
• --memory="100m": Prevents the container from using more than 100MB of memory.

Try monitoring it with:

docker stats

You’ll see that the container respects your defined resource boundaries.

Additional Options

• --memory-swap="150m": Sets total memory + swap limit.
• --memory-reservation="80m": Soft limit before Docker starts throttling.
• --cpuset-cpus="0,1": Binds container to specific CPU cores.
• --oom-kill-disable: Prevents the container from being killed when it exceeds memory.

Best Practices

• Always define limits in production, especially on shared servers.
• Use monitoring tools like docker stats, Prometheus, or cAdvisor to observe usage.
• Test your container behavior under constrained environments before deployment.

Docker makes it easy to control how much CPU and memory a container can consume. By setting appropriate limits, you can ensure that your applications stay stable and predictable—even in busy or resource-limited environments.

You can read more useful articles like How to Fix Timezone Issues in Docker Containers.

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