Simple and Sharp RSS Aggregator.

Feedlark uses Vagrant. Vagrant is a tool to deploy a virtual machine with our required environment fully configured. It requires Virtualbox to host the VM. The Vagrant startup script can be seen in Vagrantfile. You can start Vagrant with the instructions in the 'Usage' section below.

Once the environment is set up, Feedlark uses the Gearman messaging system. Gearman allows different modules of the project to be run as separate applications called Workers. These Workers can then be requested to complete their task by a Client. The Gearman Job Server, initiated by Vagrant, handles the communication between each module. All of our Gearman data is transmitted in BSON. The format of the data required by each worker is listed in its individual README.md file.

Feedlark is using MongoDB for its data storage. There are Gearman workers in the dbtools directory to handle most database interactions, so that each individual module doesn't need to re-implement database connections.

The front end uses express.js, an MVC framework in Node.js.

Setting up the Vagrant VM for the first time is very very slow (potentially upwards of 30 minutes), as there are a lot of dependencies. In particular, spacy, the tool we are using for Python natural language processing, requires a 500mb download of its English NLP model. However, once this initial setup has been completed, the VM can be booted in less than a minute.

The whole virtual machine will currently be no larger than 6Gb on disc. It will use at max 3GB of RAM.

• Vagrant
• VirtualBox
• An SSH client (if one isn't included on your system)

$ vagrant up # start
$ vagrant provision  # setup depends
$ vagrant ssh # gain ssh (on a POSIX system)
$ vagrant halt # stop

• vagrant ssh doesn't always work unless you have an ssh binary. Please read this to setup putty.

• CRLF line endings may appear in your files. This will make the scripts error. To fix this run these commands inside your host repository:

  git config core.eol lf
  git config core.autocrlf input
  git checkout-index --force --all
  

  Note that you'll need a text editor that supports LF to edit the files now on Windows.

Run the following commands inside your user folder or equivalent:

$ cd <root_of_your_repo>
$ vagrant up
$ script/start.sh SECRET_KEY_HERE
# if you get errors, ssh to the vagrant box and try:
$ cd /vagrant
$ bash script/start_internal.sh SECRET_KEY_HERE

You should now be able to browse to http://192.168.2.2:3000/.

The Vagrant configuration is laid out in the Vagrantfile.

This will download an image of Ubuntu 15.10 (Wily), install the required packages and setup the services.

Pip dependencies will also be downloaded.

• 192.168.2.2: All services
• 3000: ExpressJS Web Server
• 4730: Gearman Job Server
• 9001: MongoDB

The following symlinks your github repository into your Vagrant box's file system:

• . -> /vagrant

Is something missing from the Vagrant? Open an issue!

This is the code that coalesces the database collections feed and user, and places the data in g2g. That is, it takes the feed data, and the user data, and creates the feeds tailored to each individual user. It also includes the tool to compute the similarity of a user's interests and an article's topics.

The article getter is the Gearman worker that fetches the text from items in RSS feeds. It puts the text of these articles in the database and then calls the topics worker which pulls out the topics of the article.

This module is a command line interface to the backend Gearman workers. It is intended to make debugging easier by allowing manual requests to be made.

The code that provides the Gearman database workers db-add, db-get, db-update, and db-upsert. The workers are written in Go.

This is where all documentation lives that doesn't directly relate to code. Find the specs for the database collections, and the logging specs, here.

A simple Gearman cron-like job that refreshes the feeds at regular intervals.

This contains the Gearman worker to scrape feeds from the web.

This is where we keep the scripts and configs related to the project. For example, the scripts to run the tests on all aspects of the project are here.

This is where we keep the server, which routes HTTP and renders responses.

This contains the tool to parse an article and pick out the topics it relates to.

This contains the tool that is called whenever a user votes on an article. It updates the list of topics they have opinions on in the database, and adds a document to the vote collection in the database.

This contains a worker to refresh the machine learning model of a particular user, by taking all of their votes from the vote collection and training the model with them, then putting a pickled copy of that model into that user's document in the user collection.

This contains the tools that are used to visualise the data in the databases in a more friendly and useful way; to parse the data, and to provide meaningful statistics, such as what are the most subscribed feeds, and what topics is a particular user most interested in.