A comment in https://github.com/docker/docker/issues/6354#issuecomment-74160215 brought to light that the "Managing Data in containers" section contained an incorrect (or confusing) line; "Volumes persist until no containers use them" Which implies that volumes are automatically removed if they are no longer referenced by a container. This pull-request attempts to add some information explaining that volumes are never automatically removed by Docker and adds some extra hints on working with data volumes. Signed-off-by: Sebastiaan van Stijn <github@gone.nl>
8.2 KiB
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:
- Volumes are initialized when a container is created
- 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
- 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 will 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.
$ 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 aDockerfile
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 Docker daemon's host into a container.
Note: If you are using Boot2Docker, your Docker daemon only has limited access to your OSX/Windows filesystem. Boot2Docker tries to auto-share your
/Users
(OSX) orC:\Users
(Windows) directory - and so you can mount files or directories usingdocker run -v /Users/<path>:/<container path> ...
(OSX) ordocker run -v /c/Users/<path>:/<container path ...
(Windows). All other paths come from the Boot2Docker virtual machine's filesystem.
$ sudo 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 ofmount
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 aDockerfile
probably wouldn't work on 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.
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.
$ sudo 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
andsed --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.
$ sudo docker create -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. 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 #8484
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 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.
$ sudo docker run -v /dbdata --name dbdata2 ubuntu /bin/bash
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 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.