How to Keep Docker Containers Running Continuously

As developers build complex applications with Docker, they often need containers to keep running instead of stopping after finishing their default command. For example, you may want a containerized database or cache server to persist without downtime for testing. This guide covers several methods to keep Docker containers running indefinitely in the background until explicitly stopped.

Why Keep Containers Running?

Here are some common use cases where developers need containers to run continuously rather than shutting down:

Integration Testing Environments

When testing applications that connect to databases, queues, web services etc. it‘s useful to keep these dependencies running to detect issues. Stopping and restarting containers causes state loss.

Debugging Live Applications

Debugging complex issues often requires inspecting apps in a running state. Keeping containers up simplifies troubleshooting without reproduction steps.

Analyzing Performance Under Load

Performance testing involves benchmarking apps under continuous load. Keeping containers running helps monitor metrics like CPU usage over time.

Reusing Stateful Components

Stateful components like databases should run continuously across deployments to reuse data. Persistent volumes can assist with retaining state.

Developing Microservices Apps

Microservices based apps comprise of many small, distributed services. Running containers continuously mimics production environments.

These are just some examples – there are many cases where keeping a Docker container running indefinitely is necessary. The two main methods are outlined next.

Method 1: Add tail -f in Docker Command

The simplest way to keep a container running is to override its command, executing an infinite loop instead of a default command:

docker run --name mycontainer myimage tail -f /dev/null

Here, tail -f /dev/null will run indefinitely inside mycontainer, keeping it running in the background until you stop it.

Some advantages of this method:

• Simple – Just append this command and avoid entry point overrides. Easy troubleshooting.
• Lightweight – No need to install utilities or write wrapper scripts.
• Cross-platform – Works on Linux, Windows and Mac containers.
• Scalable – Containers run independent of host state.

The main disadvantage is that tail -f consumes a process inside the container, limiting what other commands you can run simultaneously without overriding the entrypoint again.

Variations and Advanced Usage

There are some variations of this method for advanced use cases:

• Log file monitoring:

  docker run --name logger myapp tail -F /var/log/myapp/access.log

• Combining with docker exec to run additional processes:

  docker exec -it logger bash

• Chaining with docker commit to persist container state:

  docker commit logger myapp:v2

So while keeping a container alive with tail -f is simple, there are ways to further leverage such containers.

Method 2: Use sleep infinity

Another option is to invoke sleep infinity instead of the default command:

docker run --name mycontainer myimage sleep infinity

This essentially executes an infinite sleep process, keeping mycontainer running perpetually.

Benefits of using sleep infinity:

• Lightweight – Avoids spinning processes and wasted resources.
• Flexible – Main process is not occupied, allowing other commands to run.
• Graceful shutdown – Handles SIGTERM properly to stop container.

The drawback is that sleep infinity is not a built-in Bash command – the Docker image needs to contain a shell that supports it already.

Comparing the Two Methods

Metric	tail -f	sleep infinity
Simple to use	Yes	Varies
Portable across images	Yes	No
Flexibility for other processes	Low	High
Performance	High usage	Low usage
Ability to gracefully stop	Limited	High

As we can see, both options have certain tradeoffs. Using tail -f is widely compatible but consumes resources. sleep infinity is lightweight but needs image support.

Keeping Containers Running In Production

For production grade applications, alternatives like Docker Compose and Kubernetes are recommended instead of keeping containers manually running in this fashion.

However for testing, debugging and experimentation purposes, the methods shared here are very convenient to persist containers as required.

Some best practices to follow:

• Name containers with informative names using --name
• Monitor resource usage with tools like docker stats
• Limit lifetime to avoid unbounded growth
• Watch logs for issues or crashes
• Use restart policies to revive crashed containers
• Try to be cloud-agnostic instead of relying on host state

Troubleshooting: Handling Crashes and High Load

When keeping Docker containers running continuously, you may encounter issues like:

• Containers crashing due to bugs
• Containers being killed due to high memory/CPU
• Host machine instability due to load

Some troubleshooting tips:

• Check container logs with docker logs for crash stack traces
• Consider using restart policies via --restart flag
• Add swap space if host memory appears insufficient
• Profile CPU/memory to identify resource bottlenecks
• Use Docker monitoring solutions like Datadog to gain visibility

Bottlenecks are common, so plan limits accordingly when persisting containers.

Distinguishing Containers vs. Services

Note the methods described here keep individual Docker containers running. This differs from the concept of "services" in container orchestrators like Kubernetes.

Services represent groups of containers implementing a microservice. Orchestrators manage container lifecycles automatically.

So remember this guide focuses on administering individual containers, which complements higher level services.

Immutable Infrastructure Best Practices

Docker containers are designed to be ephemeral, running then shutting down. Techniques like keeping containers running seems contrary to practices like immutable container design.

However, pragmatic use cases sometimes require containers to persist. The methods here allow this flexibility, facilitated by Docker‘s commit feature to version container changes.

Despite keeping containers running, aim to follow immutable practices like avoiding in-place changes. Strike a practical balance for your needs.

Wrapping Up

This guide covered several techniques to keep Docker containers running continuously for scenarios like testing and debugging apps where downtime needs to be avoided.

Key points:

• Use tail -f to keep containers running via infinite loop
• Leverage sleep infinity for lightweight background persistence
• Monitor resource usage and logs of long-running containers
• Employ restart policies to revive crashed containers automatically
• Distinguish containers from services in architectures like microservices
• Balance immutable infrastructure best practices pragmatically

Now you should be equipped to keep containers running smoothly for your troubleshooting, testing and development needs.

The same concepts can be applied to keep processes running in non-container environments as well. Feel free to provide any feedback for improving this guide.