containerd/design/data-flow.md
Aaron.L.Xu a3cae915c3 fix some typos
Signed-off-by: Aaron.L.Xu <likexu@harmonycloud.cn>
2017-02-21 15:02:36 +08:00

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# Data Flow
In the past, container systems have hidden the complexity of pulling container
images, hiding many details and complexity. This document intends to shed light
on that complexity and detail how a "pull" operation will look from the
perspective of a containerd user. We use the _bundle_ as the target object in
this workflow, and walk back from there to describe the full process. In this
context, we describe both pulling an image and creating a bundle from that
image.
With containerd, we redefine the "pull" to comprise the same set of steps
encompassed in prior container engines. In this model, an image defines a
collection of resources that can be used to create a _bundle_. There is no
specific format or object called an image. The goal of the pull is to produce a
set of steps is to resolve the resources that comprise an image, with the
separation providing lifecycle points in the process.
A reference implementation of the complete "pull", performed client-side, will
be provided as part of containerd, but there may not be a single "pull" API
call.
A rough diagram of the dataflow, along with the relevant components, is below.
![Data Flow](data-flow.png)
While the process proceeds left to right in the diagram, this document is
written right to left. By working through this process backwards, we can best
understand the approach employed by containerd.
## Running a Container
For containerd, we'd generally like to retrieve a _bundle_. This is the
runtime, on-disk container layout, which includes the filesystem and
configuration required to run the container.
Generically, speaking, we can say we have the following directory:
```
config.json
rootfs/
```
The contents of `config.json` isn't interesting in this context, but for
clarity, it may be the runc config or a containerd specific configuration file
for setting up a running container. The `rootfs` is a directory where
containerd will setup the runtime container's filesystem.
While containerd doesn't have the concept of an image, we can effectively build
this structure from an image, as projected into containerd. Given this, we can
say that are requirements for running a container are to do the following:
1. Convert the configuration from the container image into the target format
for the containerd runtime.
2. Reproduce the root filesystem from the container image. While we could
unpack this into `rootfs` in the bundle, we can also just pass this as a set
of mounts to the container configuration.
The above defines the framework in which we will operate. Put differently, we
can say that we want to create a bundle by creating these two components of a
bundle.
## Creating a Bundle
Now that we've defined what is required to run a container, a _bundle_, we need
to create one.
Let's say we have the following:
```
ctr run ubuntu
```
This does no pulling of images. It only takes the name and creates a _bundle_.
Broken down into steps, the process looks as follows:
1. Lookup the digest of the image in metadata store.
2. Resolve the manifest in the content store.
3. Resolve the layer snapshots in the snapshot subsystem.
4. Transform the config into the target bundle format.
5. Create a runtime snapshot for the rootfs of the container, including resolution of mounts.
6. Run the container.
From this, we can understand the required resources to _pull_ an image:
1. An entry in the metadata store a name pointing at a particular digest.
2. The manifest must be available in the content store.
3. The result of successively applied layers must be available as a snapshot.
## Unpacking Layers
While this process may be pull or run driven, the idea is quite simple. For
each layer, apply the result to a snapshot of the previous layer. The result
should be stored under the chain id (as defined by OCI) of the resulting
application.
## Pulling an Image
With all the above defined, pulling an image simply becomes the following:
1. Fetch the manifest for the image, verify and store it.
2. Fetch each layer of the image manifest, verify and store them.
3. Store the manifest digest under the provided name.
Note that we leave off using the name to resolve a particular location. We'll
leave that for another doc!