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AUFS was the first storage driver in use with Docker. As a result, it has a long and close history with Docker, is very stable, has a lot of real-world deployments, and has strong community support. AUFS has several features that make it a good choice for Docker. These features enable:
Despite its capabilities and long history with Docker, some Linux distributions do not support AUFS. This is usually because AUFS is not included in the mainline (upstream) Linux kernel.
The following sections examine some AUFS features and how they relate to Docker.
AUFS is a unification filesystem. This means that it takes multiple directories on a single Linux host, stacks them on top of each other, and provides a single unified view. To achieve this, AUFS uses a union mount.
AUFS stacks multiple directories and exposes them as a unified view through a single mount point. All of the directories in the stack, as well as the union mount point, must all exist on the same Linux host. AUFS refers to each directory that it stacks as a branch.
Within Docker, AUFS union mounts enable image layering. The AUFS storage driver
implements Docker image layers using this union mount system. AUFS branches
correspond to Docker image layers. The diagram below shows a Docker container
based on the
This diagram shows that each image layer, and the container layer, is
represented in the Docker hosts filesystem as a directory under
/var/lib/docker/. The union mount point provides the unified view of all
layers. As of Docker 1.10, image layer IDs do not correspond to the names of
the directories that contain their data.
AUFS also supports the copy-on-write technology (CoW). Not all storage drivers do.
Docker leverages AUFS CoW technology to enable image sharing and minimize the use of disk space. AUFS works at the file level. This means that all AUFS CoW operations copy entire files - even if only a small part of the file is being modified. This behavior can have a noticeable impact on container performance, especially if the files being copied are large, below a lot of image layers, or the CoW operation must search a deep directory tree.
Consider, for example, an application running in a container needs to add a single new value to a large key-value store (file). If this is the first time the file is modified, it does not yet exist in the container’s top writable layer. So, the CoW must copy up the file from the underlying image. The AUFS storage driver searches each image layer for the file. The search order is from top to bottom. When it is found, the entire file is copied up to the container’s top writable layer. From there, it can be opened and modified.
Larger files obviously take longer to copy up than smaller files, and files that exist in lower image layers take longer to locate than those in higher layers. However, a copy up operation only occurs once per file on any given container. Subsequent reads and writes happen against the file’s copy already copied-up to the container’s top layer.
The AUFS storage driver deletes a file from a container by placing a whiteout file in the container’s top layer. The whiteout file effectively obscures the existence of the file in the read-only image layers below. The simplified diagram below shows a container based on an image with three image layers.
file3 was deleted from the container. So, the AUFS storage driver placed
a whiteout file in the container’s top layer. This whiteout file effectively
file3 from the container by obscuring any of the original file’s
existence in the image’s read-only layers. This works the same no matter which
of the image’s read-only layers the file exists in.
You can only use the AUFS storage driver on Linux systems with AUFS installed. Use the following command to determine if your system supports AUFS.
$ grep aufs /proc/filesystems nodev aufs
This output indicates the system supports AUFS. Once you’ve verified your
system supports AUFS, you can must instruct the Docker daemon to use it. You do
this from the command line with the
$ sudo dockerd --storage-driver=aufs &
Alternatively, you can edit the Docker config file and add the
--storage-driver=aufs option to the
# Use DOCKER_OPTS to modify the daemon startup options. DOCKER_OPTS="--storage-driver=aufs"
Once your daemon is running, verify the storage driver with the
$ sudo docker info Containers: 1 Images: 4 Storage Driver: aufs Root Dir: /var/lib/docker/aufs Backing Filesystem: extfs Dirs: 6 Dirperm1 Supported: false Execution Driver: native-0.2 ...output truncated...
The output above shows that the Docker daemon is running the AUFS storage
driver on top of an existing
ext4 backing filesystem.
dockerd runs with the AUFS driver, the driver stores images and
containers within the Docker host’s local storage area under
Image layers and their contents are stored under
/var/lib/docker/aufs/diff/. With Docker 1.10 and higher, image layer IDs do
not correspond to directory names.
/var/lib/docker/aufs/layers/ directory contains metadata about how image
layers are stacked. This directory contains one file for every image or
container layer on the Docker host (though file names no longer match image
layer IDs). Inside each file are the names of the directories that exist below
it in the stack
The command below shows the contents of a metadata file in
/var/lib/docker/aufs/layers/ that lists the three directories that are
stacked below it in the union mount. Remember, these directory names do no map
to image layer IDs with Docker 1.10 and higher.
$ cat /var/lib/docker/aufs/layers/91e54dfb11794fad694460162bf0cb0a4fa710cfa3f60979c177d920813e267c d74508fb6632491cea586a1fd7d748dfc5274cd6fdfedee309ecdcbc2bf5cb82 c22013c8472965aa5b62559f2b540cd440716ef149756e7b958a1b2aba421e87 d3a1f33e8a5a513092f01bb7eb1c2abf4d711e5105390a3fe1ae2248cfde1391
The base layer in an image has no image layers below it, so its file is empty.
Running containers are mounted below
This is where the AUFS union mount point that exposes the container and all
underlying image layers as a single unified view exists. If a container is not
running, it still has a directory here but it is empty. This is because AUFS
only mounts a container when it is running. With Docker 1.10 and higher,
container IDs no longer correspond to directory names under
Container metadata and various config files that are placed into the running
container are stored in
/var/lib/docker/containers/<container-id>. Files in
this directory exist for all containers on the system, including ones that are
stopped. However, when a container is running the container’s log files are
also in this directory.
A container’s thin writable layer is stored in a directory under
/var/lib/docker/aufs/diff/. With Docker 1.10 and higher, container IDs no
longer correspond to directory names. However, the containers thin writable
layer still exists under here and is stacked by AUFS as the top writable layer
and is where all changes to the container are stored. The directory exists even
if the container is stopped. This means that restarting a container will not
lose changes made to it. Once a container is deleted, it’s thin writable layer
in this directory is deleted.
To summarize some of the performance related aspects already mentioned:
The AUFS storage driver is a good choice for PaaS and other similar use-cases where container density is important. This is because AUFS efficiently shares images between multiple running containers, enabling fast container start times and minimal use of disk space.
The underlying mechanics of how AUFS shares files between image layers and containers uses the systems page cache very efficiently.
The AUFS storage driver can introduce significant latencies into container write performance. This is because the first time a container writes to any file, the file has be located and copied into the containers top writable layer. These latencies increase and are compounded when these files exist below many image layers and the files themselves are large.
One final point. Data volumes provide the best and most predictable performance. This is because they bypass the storage driver and do not incur any of the potential overheads introduced by thin provisioning and copy-on-write. For this reason, you may want to place heavy write workloads on data volumes.