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

page_title: Managing Data in Containers page_description: How to manage data inside your Docker containers. page_keywords: Examples, Usage, volume, docker, documentation, user guide, data, volumes

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 to provide several useful features for persistent or shared data:

• Data volumes can be shared and reused between containers
• Changes to a data volume are made directly
• Changes to a data volume will not be included when you update an image
• Volumes persist until no containers use them

Adding a data volume

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

$ sudo 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.

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 own host into a container.

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

This will mount the local directory, /src/webapp, into the container as the /opt/webapp directory. 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 the portability and sharing purpose of it. As the host directory is, by its nature, host-dependent it might not work all hosts.

Docker defaults to a read-write volume but we can also mount a directory read-only.

$ sudo 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.

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.

$ sudo docker run -d -v /dbdata --name dbdata training/postgres

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

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

And another:

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

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.

$ sudo 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 until there are no containers still referencing those volumes. This allows you to upgrade, or effectively migrate data volumes between containers.

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:

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

Here's 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 to restore to the same container, or another that you've made elsewhere. Create a new container.

$ sudo docker run -v /dbdata --name dbdata2 ubuntu

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

$ sudo docker run --volumes-from dbdata2 -v $(pwd):/backup busybox tar xvf /backup/backup.tar

You can use this 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.