Working from feedback on the existing implementation, we have now
introduced a central metadata object to represent the lifecycle and pin
the resources required to implement what people today know as
containers. This includes the runtime specification and the root
filesystem snapshots. We also allow arbitrary labeling of the container.
Such provisions will bring the containerd definition of container closer
to what is expected by users.
The objects that encompass today's ContainerService, centered around the
runtime, will be known as tasks. These tasks take on the existing
lifecycle behavior of containerd's containers, which means that they are
deleted when they exit. Largely, there are no other changes except for
naming.
The `Container` object will operate purely as a metadata object. No
runtime state will be held on `Container`. It only informs the execution
service on what is required for creating tasks and the resources in use
by that container. The resources referenced by that container will be
deleted when the container is deleted, if not in use. In this sense,
users can create, list, label and delete containers in a similar way as
they do with docker today, without the complexity of runtime locks that
plagues current implementations.
Signed-off-by: Stephen J Day <stephen.day@docker.com>
For some reason, when I wrote this, I forgot about the `View` and
`Update` helpers on boltdb. These are now used and makes the code much
easier to follow.
Signed-off-by: Stephen J Day <stephen.day@docker.com>
Server and Client images of the image store are now provided. We have
created an image metadata interface and converted the bolt functions to
implement that interface over an transaction. A remote client
implementation is provided that implements the same interface.
Signed-off-by: Stephen J Day <stephen.day@docker.com>
This is a first pass at the metadata required for supporting an image
store. We use a shallow approach to the problem, allowing this
component to centralize the naming. Resources for this image can then be
"snowballed" in for actual implementations. This is better understood
through example.
Let's take pull. One could register the name "docker.io/stevvooe/foo" as
pointing at a particular digest. When instructed to pull or fetch, the
system will notice that no components of that image are present locally.
It can then recursively resolve the resources for that image and fetch
them into the content store. Next time the instruction is issued, the
content will be present so no action will be taken.
Another example is preparing the rootfs. The requirements for a rootfs
can be resolved from a name. These "diff ids" will then be compared with
what is available in the snapshot manager. Any parts of the rootfs, such
as a layer, that isn't available in the snapshotter can be unpacked.
Once this process is satisified, the image will be runnable as a
container.
Signed-off-by: Stephen J Day <stephen.day@docker.com>
