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container.training/slides/intro/Container_Engines.md
Alexis Daboville 5e78e00bc9 Small typos (#272)
* Small typo

* elastichsearch -> elasticsearch

* realeased -> released
2018-06-02 09:09:38 -05:00

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# Docker Engine and other container engines
* We are going to cover the architecture of the Docker Engine.
* We will also present other container engines.
---
class: pic
## Docker Engine external architecture
![](images/docker-engine-architecture.svg)
---
## Docker Engine external architecture
* The Engine is a daemon (service running in the background).
* All interaction is done through a REST API exposed over a socket.
* On Linux, the default socket is a UNIX socket: `/var/run/docker.sock`.
* We can also use a TCP socket, with optional mutual TLS authentication.
* The `docker` CLI communicates with the Engine over the socket.
Note: strictly speaking, the Docker API is not fully REST.
Some operations (e.g. dealing with interactive containers
and log streaming) don't fit the REST model.
---
class: pic
## Docker Engine internal architecture
![](images/dockerd-and-containerd.png)
---
## Docker Engine internal architecture
* Up to Docker 1.10: the Docker Engine is one single monolithic binary.
* Starting with Docker 1.11, the Engine is split into multiple parts:
- `dockerd` (REST API, auth, networking, storage)
- `containerd` (container lifecycle, controlled over a gRPC API)
- `containerd-shim` (per-container; does almost nothing but allows to restart the Engine without restarting the containers)
- `runc` (per-container; does the actual heavy lifting to start the container)
* Some features (like image and snapshot management) are progressively being pushed from `dockerd` to `containerd`.
For more details, check [this short presentation by Phil Estes](https://www.slideshare.net/PhilEstes/diving-through-the-layers-investigating-runc-containerd-and-the-docker-engine-architecture).
---
## Other container engines
The following list is not exhaustive.
Furthermore, we limited the scope to Linux containers.
Containers also exist (sometimes with other names) on Windows, macOS, Solaris, FreeBSD ...
---
## LXC
* The venerable ancestor (first released in 2008).
* Docker initially relied on it to execute containers.
* No daemon; no central API.
* Each container is managed by a `lxc-start` process.
* Each `lxc-start` process exposes a custom API over a local UNIX socket, allowing to interact with the container.
* No notion of image (container filesystems have to be managed manually).
* Networking has to be setup manually.
---
## LXD
* Re-uses LXC code (through liblxc).
* Builds on top of LXC to offer a more modern experience.
* Daemon exposing a REST API.
* Can manage images, snapshots, migrations, networking, storage.
* "offers a user experience similar to virtual machines but using Linux containers instead."
---
## rkt
* Compares to `runc`.
* No daemon or API.
* Strong emphasis on security (through privilege separation).
* Networking has to be setup separately (e.g. through CNI plugins).
* Partial image management (pull, but no push).
(Image build is handled by separate tools.)
---
## CRI-O
* Designed to be used with Kubernetes as a simple, basic runtime.
* Compares to `containerd`.
* Daemon exposing a gRPC interface.
* Controlled using the CRI API (Container Runtime Interface defined by Kubernetes).
* Needs an underlying OCI runtime (e.g. runc).
* Handles storage, images, networking (through CNI plugins).
We're not aware of anyone using it directly (i.e. outside of Kubernetes).
---
## systemd
* "init" system (PID 1) in most modern Linux distributions.
* Offers tools like `systemd-nspawn` and `machinectl` to manage containers.
* `systemd-nspawn` is "In many ways it is similar to chroot(1), but more powerful".
* `machinectl` can interact with VMs and containers managed by systemd.
* Exposes a DBUS API.
* Basic image support (tar archives and raw disk images).
* Network has to be setup manually.
---
## Overall ...
* The Docker Engine is very developer-centric:
- easy to install
- easy to use
- no manual setup
- first-class image build and transfer
* As a result, it is a fantastic tool in development environments.
* On servers:
- Docker is a good default choice
- If you use Kubernetes, the engine doesn't matter