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.

这一章介绍如何在docker容器之间管理数据

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

在容器中管理数据的2个主要方法

• Data volumes, and
• Data volume containers.

Data volumes

数据卷

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:

数据卷是一个专门UFS的专门目录，它可以提供很多有用的特性或者共享数据

• Data volumes can be shared and reused between containers  数据卷可以在容器之间共享和重用
• Changes to a data volume are made directly 对数据卷的改变是立马生效
• 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
to the portability and sharing purpose of it. As the host directory is, by its nature, host-dependent, a host directory specified in a Dockerfile 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 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.

$ sudo docker run -d -v /dbdata --name dbdata training/postgres echo Data-only container for 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 /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 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.

Managing Data in Containers