moby--moby/docs/sources/userguide/usingdocker.md

page_title: Working with Containers page_description: Learn how to manage and operate Docker containers. page_keywords: docker, the docker guide, documentation, docker.io, monitoring containers, docker top, docker inspect, docker port, ports, docker logs, log, Logs

Working with Containers

In the last section of the Docker User Guide we launched our first containers. We launched two containers using the docker run command.

• Containers we ran interactively in the foreground.
• One container we ran daemonized in the background.

In the process we learned about several Docker commands:

• docker ps - Lists containers.
• docker logs - Shows us the standard output of a container.
• docker stop - Stops running containers.

Tip: Another way to learn about docker commands is our interactive tutorial.

The docker client is pretty simple. Each action you can take with Docker is a command and each command can take a series of flags and arguments.

# Usage:  [sudo] docker [flags] [command] [arguments] ..
# Example:
$ docker run -i -t ubuntu /bin/bash

Let's see this in action by using the docker version command to return version information on the currently installed Docker client and daemon.

$ sudo docker version

This command will not only provide you the version of Docker client and daemon you are using, but also the version of Go (the programming language powering Docker).

Client version: 0.8.0
Go version (client): go1.2

Git commit (client): cc3a8c8
Server version: 0.8.0

Git commit (server): cc3a8c8
Go version (server): go1.2

Last stable version: 0.8.0

Seeing what the Docker client can do

We can see all of the commands available to us with the Docker client by running the docker binary without any options.

$ sudo docker

You will see a list of all currently available commands.

Commands:
     attach    Attach to a running container
     build     Build an image from a Dockerfile
     commit    Create a new image from a container's changes
. . .

Seeing Docker command usage

You can also zoom in and review the usage for specific Docker commands.

Try typing Docker followed with a [command] to see the usage for that command:

$ sudo docker attach
Help output . . .

Or you can also pass the --help flag to the docker binary.

$ sudo docker images --help

This will display the help text and all available flags:

Usage: docker attach [OPTIONS] CONTAINER

Attach to a running container

  --no-stdin=false: Do not attach stdin
  --sig-proxy=true: Proxify all received signal to the process (even in non-tty mode)

None of the containers we've run did anything particularly useful though. So let's build on that experience by running an example web application in Docker.

Note: You can see a full list of Docker's commands here.

Running a Web Application in Docker

So now we've learnt a bit more about the docker client let's move onto the important stuff: running more containers. So far none of the containers we've run did anything particularly useful though. So let's build on that experience by running an example web application in Docker.

For our web application we're going to run a Python Flask application. Let's start with a docker run command.

$ sudo docker run -d -P training/webapp python app.py

Let's review what our command did. We've specified two flags: -d and -P. We've already seen the -d flag which tells Docker to run the container in the background. The -P flag is new and tells Docker to map any required network ports inside our container to our host. This lets us view our web application.

We've specified an image: training/webapp. This image is a pre-built image we've created that contains a simple Python Flask web application.

Lastly, we've specified a command for our container to run: python app.py. This launches our web application.

Note: You can see more detail on the docker run command in the command reference and the Docker Run Reference.

Viewing our Web Application Container

Now let's see our running container using the docker ps command.

$ sudo docker ps -l
CONTAINER ID  IMAGE                   COMMAND       CREATED        STATUS        PORTS                    NAMES
bc533791f3f5  training/webapp:latest  python app.py 5 seconds ago  Up 2 seconds  0.0.0.0:49155->5000/tcp  nostalgic_morse

You can see we've specified a new flag, -l, for the docker ps command. This tells the docker ps command to return the details of the last container started.

Note: The docker ps command only shows running containers. If you want to see stopped containers too use the -a flag.

We can see the same details we saw when we first Dockerized a container with one important addition in the PORTS column.

PORTS
0.0.0.0:49155->5000/tcp

When we passed the -P flag to the docker run command Docker mapped any ports exposed in our image to our host.

Note: We'll learn more about how to expose ports in Docker images when we learn how to build images.

In this case Docker has exposed port 5000 (the default Python Flask port) on port 49155.

Network port bindings are very configurable in Docker. In our last example the -P flag is a shortcut for -p 5000 that makes port 5000 inside the container to a high port (from the range 49000 to 49900) on the local Docker host. We can also bind Docker container's to specific ports using the -p flag, for example:

$ sudo docker run -d -p 5000:5000 training/webapp python app.py

This would map port 5000 inside our container to port 5000 on our local host. You might be asking about now: why wouldn't we just want to always use 1:1 port mappings in Docker containers rather than mapping to high ports? Well 1:1 mappings have the constraint of only being able to map one of each port on your local host. Let's say you want to test two Python applications: both bound to port 5000 inside your container. Without Docker's port mapping you could only access one at a time.

So let's now browse to port 49155 in a web browser to see the application.

.

Our Python application is live!

A Network Port Shortcut

Using the docker ps command to return the mapped port is a bit clumsy so Docker has a useful shortcut we can use: docker port. To use docker port we specify the ID or name of our container and then the port for which we need the corresponding public-facing port.

$ sudo docker port nostalgic_morse 5000
0.0.0.0:49155

In this case we've looked up what port is mapped externally to port 5000 inside the container.

Viewing the Web Application's Logs

Let's also find out a bit more about what's happening with our application and use another of the commands we've learnt, docker logs.

$ sudo docker logs -f nostalgic_morse
* Running on http://0.0.0.0:5000/
10.0.2.2 - - [23/May/2014 20:16:31] "GET / HTTP/1.1" 200 -
10.0.2.2 - - [23/May/2014 20:16:31] "GET /favicon.ico HTTP/1.1" 404 -

This time though we've added a new flag, -f. This causes the docker logs command to act like the tail -f command and watch the container's standard out. We can see here the logs from Flask showing the application running on port 5000 and the access log entries for it.

Looking at our Web Application Container's processes

In addition to the container's logs we can also examine the processes running inside it using the docker top command.

$ sudo docker top nostalgic_morse
PID                 USER                COMMAND
854                 root                python app.py

Here we can see our python app.py command is the only process running inside the container.

Inspecting our Web Application Container

Lastly, we can take a low-level dive into our Docker container using the docker inspect command. It returns a JSON hash of useful configuration and status information about Docker containers.

$ sudo docker inspect nostalgic_morse

Let's see a sample of that JSON output.

[{
    "ID": "bc533791f3f500b280a9626688bc79e342e3ea0d528efe3a86a51ecb28ea20",
    "Created": "2014-05-26T05:52:40.808952951Z",
    "Path": "python",
    "Args": [
       "app.py"
    ],
    "Config": {
       "Hostname": "bc533791f3f5",
       "Domainname": "",
       "User": "",
. . .

We can also narrow down the information we want to return by requesting a specific element, for example to return the container's IP address we would:

$ sudo docker inspect -f '{{ .NetworkSettings.IPAddress }}' nostalgic_morse
172.17.0.5

Stopping our Web Application Container

Okay we've seen web application working. Now let's stop it using the docker stop command and the name of our container: nostalgic_morse.

$ sudo docker stop nostalgic_morse
nostalgic_morse

We can now use the docker ps command to check if the container has been stopped.

$ sudo docker ps -l

Restarting our Web Application Container

Oops! Just after you stopped the container you get a call to say another developer needs the container back. From here you have two choices: you can create a new container or restart the old one. Let's look at starting our previous container back up.

$ sudo docker start nostalgic_morse
nostalgic_morse

Now quickly run docker ps -l again to see the running container is back up or browse to the container's URL to see if the application responds.

Note: Also available is the docker restart command that runs a stop and then start on the container.

Removing our Web Application Container

Your colleague has let you know that they've now finished with the container and won't need it again. So let's remove it using the docker rm command.

$ sudo docker rm nostalgic_morse
Error: Impossible to remove a running container, please stop it first or use -f
2014/05/24 08:12:56 Error: failed to remove one or more containers

What's happened? We can't actually remove a running container. This protects you from accidentally removing a running container you might need. Let's try this again by stopping the container first.

$ sudo docker stop nostalgic_morse
nostalgic_morse
$ sudo docker rm nostalgic_morse
nostalgic_morse

And now our container is stopped and deleted.

Note: Always remember that deleting a container is final!

Next steps

Until now we've only used images that we've downloaded from Docker Hub now let's get introduced to building and sharing our own images.

Go to Working with Docker Images.