moby--moby/docs/userguide/dockervolumes.md

Managing data in containers

So far we've been introduced to some basic Docker concepts, seen how to work with Docker images as well as learned about networking and links between containers. In this section we're going to discuss how you can manage data inside and between your Docker containers.

We're going to look at the two primary ways you can manage data in Docker.

• Data volumes, and
• Data volume containers.

Data volumes

A data volume is a specially-designated directory within one or more containers that bypasses the Union File System. Data volumes provide several useful features for persistent or shared data:

• Volumes are initialized when a container is created. If the container's base image contains data at the specified mount point, that existing data is copied into the new volume upon volume initialization.
• Data volumes can be shared and reused among containers.
• Changes to a data volume are made directly.
• Changes to a data volume will not be included when you update an image.
• Data volumes persist even if the container itself is deleted.

Data volumes are designed to persist data, independent of the container's life cycle. Docker therefore never automatically delete volumes when you remove a container, nor will it "garbage collect" volumes that are no longer referenced by a container.

Adding a data volume

You can add a data volume to a container using the -v flag with the docker create and docker run command. You can use the -v multiple times to mount multiple data volumes. Let's mount a single volume now in our web application container.

$ docker run -d -P --name web -v /webapp training/webapp python app.py

This will create a new volume inside a container at /webapp.

Note: You can also use the VOLUME instruction in a Dockerfile to add one or more new volumes to any container created from that image.

Docker volumes default to mount in read-write mode, but you can also set it to be mounted read-only.

$ docker run -d -P --name web -v /opt/webapp:ro training/webapp python app.py

Locating a volume

You can locate the volume on the host by utilizing the 'docker inspect' command.

$ docker inspect web

The output will provide details on the container configurations including the volumes. The output should look something similar to the following:

...
"Volumes": {
    "/webapp": "/var/lib/docker/volumes/fac362...80535"
},
"VolumesRW": {
    "/webapp": true
}
...

You will notice in the above 'Volumes' is specifying the location on the host and 'VolumesRW' is specifying that the volume is read/write.

Mount a host directory as a data volume

In addition to creating a volume using the -v flag you can also mount a directory from your Docker daemon's host into a container.

Note: If you are using Boot2Docker, your Docker daemon only has limited access to your OS X/Windows filesystem. Boot2Docker tries to auto-share your /Users (OS X) or C:\Users (Windows) directory - and so you can mount files or directories using docker run -v /Users/<path>:/<container path> ... (OS X) or docker run -v /c/Users/<path>:/<container path ... (Windows). All other paths come from the Boot2Docker virtual machine's filesystem.

$ docker run -d -P --name web -v /src/webapp:/opt/webapp training/webapp python app.py

This will mount the host directory, /src/webapp, into the container at /opt/webapp.

Note: If the path /opt/webapp already exists inside the container's image, its contents will be replaced by the contents of /src/webapp on the host to stay consistent with the expected behavior of mount

When using Boot2Docker on Windows through git bash, there might be an issue with the way the source directory name is parsed. You can fix it by using a double slash at the beginning of the source directory name as explained in issue #12751

This is very useful for testing, for example we can mount our source code inside the container and see our application at work as we change the source code. The directory on the host must be specified as an absolute path and if the directory doesn't exist Docker will automatically create it for you.

Note: This is not available from a Dockerfile due to the portability and sharing purpose of built images. The host directory is, by its nature, host-dependent, so a host directory specified in a Dockerfile probably wouldn't work on all hosts.

Docker volumes default to mount in read-write mode, but you can also set it to be mounted read-only.

$ docker run -d -P --name web -v /src/webapp:/opt/webapp:ro training/webapp python app.py

Here we've mounted the same /src/webapp directory but we've added the ro option to specify that the mount should be read-only.

Mount a host file as a data volume

The -v flag can also be used to mount a single file - instead of just directories - from the host machine.

$ docker run --rm -it -v ~/.bash_history:/.bash_history ubuntu /bin/bash

This will drop you into a bash shell in a new container, you will have your bash history from the host and when you exit the container, the host will have the history of the commands typed while in the container.

Note: Many tools used to edit files including vi and sed --in-place may result in an inode change. Since Docker v1.1.0, this will produce an error such as "sed: cannot rename ./sedKdJ9Dy: Device or resource busy". In the case where you want to edit the mounted file, it is often easiest to instead mount the parent directory.

Creating and mounting a data volume container

If you have some persistent data that you want to share between containers, or want to use from non-persistent containers, it's best to create a named Data Volume Container, and then to mount the data from it.

Let's create a new named container with a volume to share. While this container doesn't run an application, it reuses the training/postgres image so that all containers are using layers in common, saving disk space.

$ docker create -v /dbdata --name dbdata training/postgres /bin/true

You can then use the --volumes-from flag to mount the /dbdata volume in another container.

$ docker run -d --volumes-from dbdata --name db1 training/postgres

And another:

$ docker run -d --volumes-from dbdata --name db2 training/postgres

In this case, if the postgres image contained a directory called /dbdata then mounting the volumes from the dbdata container hides the /dbdata files from the postgres image. The result is only the files from the dbdata container are visible.

You can use multiple --volumes-from parameters to bring together multiple data volumes from multiple containers.

You can also extend the chain by mounting the volume that came from the dbdata container in yet another container via the db1 or db2 containers.

$ docker run -d --name db3 --volumes-from db1 training/postgres

If you remove containers that mount volumes, including the initial dbdata container, or the subsequent containers db1 and db2, the volumes will not be deleted. To delete the volume from disk, you must explicitly call docker rm -v against the last container with a reference to the volume. This allows you to upgrade, or effectively migrate data volumes between containers.

Note: Docker will not warn you when removing a container without providing the -v option to delete its volumes. If you remove containers without using the -v option, you may end up with "dangling" volumes; volumes that are no longer referenced by a container. Dangling volumes are difficult to get rid of and can take up a large amount of disk space. We're working on improving volume management and you can check progress on this in pull request #14214

Backup, restore, or migrate data volumes

Another useful function we can perform with volumes is use them for backups, restores or migrations. We do this by using the --volumes-from flag to create a new container that mounts that volume, like so:

$ docker run --volumes-from dbdata -v $(pwd):/backup ubuntu tar cvf /backup/backup.tar /dbdata

Here we've launched a new container and mounted the volume from the dbdata container. We've then mounted a local host directory as /backup. Finally, we've passed a command that uses tar to backup the contents of the dbdata volume to a backup.tar file inside our /backup directory. When the command completes and the container stops we'll be left with a backup of our dbdata volume.

You could then restore it to the same container, or another that you've made elsewhere. Create a new container.

$ docker run -v /dbdata --name dbdata2 ubuntu /bin/bash

Then un-tar the backup file in the new container's data volume.

$ docker run --volumes-from dbdata2 -v $(pwd):/backup ubuntu cd /dbdata && tar xvf /backup/backup.tar

You can use the techniques above to automate backup, migration and restore testing using your preferred tools.

Next steps

Now we've learned a bit more about how to use Docker we're going to see how to combine Docker with the services available on Docker Hub including Automated Builds and private repositories.

Go to Working with Docker Hub.