Funker allows you to package up pieces of your application as Docker containers and have them run on-demand on a swarm.
You can define functions like this as Docker services:
var funker = require('funker');
funker.handler(function(args, callback) {
callback(args.x + args.y);
});Then call them from other Docker services on any node in the swarm:
>>> import funker
>>> funker.call("add", x=1, y=2)
3These functions are being called demand, scale effortlessly, and make your application vastly simpler. It's a bit like serverless, but just using Docker.
First, you need to package up a piece of your application as a function. Let's start with a trivial example: a function that adds two numbers together.
Save this code as handler.js:
var funker = require('funker');
funker.handler(function(args, callback) {
callback(args.x + args.y);
});We also need to define the Node package in package.json:
{
"name": "app",
"version": "0.0.1",
"scripts": {
"start": "node handler.js"
},
"dependencies": {
"funker": "^0.0.1"
}
}
Then, we package it up inside a Docker container by creating Dockerfile:
FROM node:7-onbuild
And building it:
$ docker build -t add .
To run the function, you create a service:
$ docker network create --attachable -d overlay funker
$ docker service create --name add --network funker add
The function is now available at the name add to other things running inside the same network. It has booted up a warm version of the function, so calls made to it will be instant.
Let's try calling the function from a Python shell:
$ docker run -it --net funker funker/python
(The funker/python image is just a Python image with the funker package installed.)
You should now see a Python prompt. Try importing the package and running the function we just created:
>>> import funker
>>> funker.call("add", x=1, y=2)
3Cool! So, to recap: we've put a function written in Node inside a container, then called it from Python. That function is run on-demand, and this is all being done with plain Docker services and no additional infrastructure.
There are implementations of handling and calling Funker functions in various languages:
- funker-example-voting-app – an example app that uses Funker to do processing in the background
Functions are just services, so they are really easy to deploy using Compose. You simply define them alongside your long-running services.
For example, to deploy a function called process-upload:
version: "2"
services:
web:
image: oscorp/web
db:
image: postgres
process-upload:
image: oscorp/process-upload
restart: alwaysIn all the services in this application, the function will be available under the name process-upload. For example, you could call it with a bit of code like this:
funker.call("process-upload", bucket="some-s3-bucket", filename="upload.jpg")The architecture is intentionally very simple. It leans on Docker services as the base infrastructure, and avoids any unnecessary complexity (daemons, queues, storage, consensus systems, and so on).
Functions run as Docker services. When they boot up, they open a TCP socket and sit there waiting for a connection.
To call functions, another Docker service connects to the function at its hostname. This can be done anywhere in a swarm due to Docker's overlay networking. It sends function arguments as JSON, then the function responds with a return value as JSON.
Once it has been called, the function refuses any other connections. Once it has responded, the function closes the socket and quits immediately. Docker's state reconciliation will then boot up a fresh copy of the function ready to receive calls again.
So, each function only processes a single request. To process functions in parallel, we need to have multiple warm functions running in parallel, which is easy to do with Docker's service replication. The idea is to do this automatically, but this is incomplete. See this issue for more background and discussion.
An alternative implementation considered was for the function caller to create the service directly, as has been done in some previous experiments.
The upside of Funker over this implementation is that functions are warm and ready to receive calls, and you don't need the complexity of giving containers access to create Docker services somehow.
The disadvantage is that it doesn't scale easily. We need some additional infrastructure to be able to scale functions up and down to handle demand.
## Credits
- Justin Cormack for the idea.
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