Twissandra is an example project, created to learn and demonstrate how to use Cassandra. Running the project will present a website that has similar functionality to Twitter.
Most of the magic happens in twissandra/cass.py, so check that out.
Installing Twissandra is fairly straightforward. Really it just involves checking out Cassandra and Twissandra, doing a little configuration, and then starting it up. Here's a roadmap of the steps we're going to take to install the project:
- Check out the Twissandra source code
- Install and configure Cassandra
- Create a virtual Python environment with Twissandra's dependencies
- Start up the webserver
Check out the Twissandra source code
git clone git://github.com/twissandra/twissandra.git
Install and configure Cassandra
Follow the instructions for installing and setting up Cassandra. Note, Twissandra requires at-least Cassandra 2.0 or later.
And then make sure Cassandra is running:
Create a virtual Python environment with Twissandra's dependencies
First, make sure to have virtualenv installed. If it isn't installed already, this should do the trick:
sudo easy_install -U virtualenv
Now let's create a new virtual environment, and begin using it:
virtualenv twiss source twiss/bin/activate
We should install pip, so that we can more easily install Twissandra's dependencies into our new virtual environment:
easy_install -U pip
Now let's install all of the dependencies:
pip install -U -r twissandra/requirements.txt
Now that we've got all of our dependencies installed, we're ready to start up the server.
Create the schema
Make sure you're in the Twissandra checkout, and then run the sync_cassandra command to create the proper keyspace in Cassandra:
python manage.py sync_cassandra
Start up the webserver
This is the fun part! We're done setting everything up, we just need to run it:
python manage.py runserver
Now go to http://127.0.0.1:8000/ and you can play with Twissandra!
In Cassandra, the way that your data is structured is very closely tied to how how it will be retrieved. Let's start with the 'users' table. The key is the username, and the remaining columns are properties on the user:
CREATE TABLE users ( username text PRIMARY KEY, password text )
The 'friends' and 'followers' tables have a compound primary key. The first component, the "partition key", controls how the data is spread around the cluster. The second component, the "clustering key", controls how the data is sorted on disk. In this case, the sort order isn't very interesting, but what's important is that all friends and all followers of a user will be stored contiguously on disk, making a query to lookup all friends or followers of a user very efficient.
CREATE TABLE friends ( username text, friend text, since timestamp, PRIMARY KEY (username, friend) ) CREATE TABLE followers ( username text, follower text, since timestamp, PRIMARY KEY (username, follower) )
Tweets are stored with a UUID for the key.
CREATE TABLE tweets ( tweet_id uuid PRIMARY KEY, username text, body text )
The 'timeline' and 'userline' tables keep track of what tweets were made and in what order. To achieve this, we use a TimeUUID for the clustering key, resulting in tweets being stored in chronological order. The "WITH CLUSERING ORDER" option just means that the tweets will be stored in reverse chronological order (newest first), which is slightly more efficient for the queries we'll be performing.
CREATE TABLE userline ( username text, time timeuuid, tweet_id uuid, PRIMARY KEY (username, time) ) WITH CLUSTERING ORDER BY (time DESC) CREATE TABLE timeline ( username text, time timeuuid, tweet_id uuid, PRIMARY KEY (username, time) ) WITH CLUSTERING ORDER BY (time DESC)
Fake data generation
For testing purposes, you can populate the database with some fake tweets.
python manage.py fake_data <num_users> <max_tweets>
num_users is the total number of users to generate and
max_tweets is the
maximum number of tweets per user. The number of tweets per user is determined
by the Pareto distribution so the number of tweets actually generated will vary