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:
- Fast container startup times.
- Efficient use of storage.
- Efficient use of memory.
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.
## Image layering and sharing with AUFS
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 *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 `ubuntu:latest` image.
This diagram shows the relationship between the Docker image layers and the AUFS branches (directories) in `/var/lib/docker/aufs`. Each image layer and the container layer correspond to an AUFS branch (directory) in the Docker host's local storage area. The union mount point gives the unified view of all layers.
AUFS also supports the copy-on-write technology (CoW). Not all storage drivers do.
## Container reads and writes with AUFS
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.
## Deleting files with the AUFS storage driver
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 image's lower, read-only layers. The simplified
diagram below shows a container based on an image with three image layers.
The `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
"deletes" `file3` from the container by obscuring any of the original file's
existence in the image's read-only base layer. Of course, the image could have
been in any of the other layers instead or in addition depending on how the
layers are built.
## Configure Docker with AUFS
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.
```bash
$ 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 `docker daemon` command:
```bash
$ sudo docker daemon --storage-driver=aufs &
```
Alternatively, you can edit the Docker config file and add the
`--storage-driver=aufs` option to the `DOCKER_OPTS` line.
```bash
# 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 `docker info` command.
The output above shows that the Docker daemon is running the AUFS storage driver on top of an existing ext4 backing filesystem.
## Local storage and AUFS
As the `docker daemon` runs with the AUFS driver, the driver stores images and containers on within the Docker host's local storage area in the `/var/lib/docker/aufs` directory.
The image layers are shown in order. In the output above, the layer starting
with image ID "d3a1..." is the image's base layer. The image layer starting
with "91e5..." is the image's topmost layer.
## AUFS and Docker performance
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.
## Related information
* [Understand images, containers, and storage drivers](imagesandcontainers.md)
