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Some time ago I promised to create a small tutorial about live fulltext search. A fulltext search, that gives you results as you type. Ingredients: Ruby on rails ferret gem (gem install ferret) acts_as_ferret gem (gem install acts_as_ferret) auto_complete plugin (from the application root: ruby script/plugin install auto_complete) What we will do Create an empty application – simple book database
Recently I was experimenting with ruby bayes classification. At first sight it looks like a difficult topic, but with the right libraries it is interesting and funny. Before you start experimenting, you have to install 3 gems. gem install classifier gem install madeleine Confirm the required stemmer gem. For the beginning, lets experiment with the plain bayes classifier. require 'classifier' bayes
While doing some Ruby on Rails code refactoring, I realized I’d like to visualize the database schema of the application. An easy possibility is to print out db/schema.rb. This seems a bit too linear and little visual to me. OK, I’d better keep thinking. An enterprise approach would be to take a CASE machinery and feed it with the create DB scripts. That is solid and reliable, but a kind of overki
There are several possibilities how to use ferret in RoR. This post will show the easy way – using the acts_as_ferret plugin. To show the syntax and code, I will use the same data objects as in the Full text search in ruby on rails 2 – MySQL Installation Ferret installation is easy gem install ferret will do the job. In addition, it is necessary to install the acts_as_ferret plugin. script/plugin
These days, each web application needs a full text search. Fortunately, there is several handy technologies one can use – native MySQL full text index, Google search, Ferret (ruby port of Lucene) and probably lot more I did not investigate. We have decided to use ferret as the full text search engine. The decision was not straight forward and it taken some time. Well, in fact in our agile approach
My previous post compared MySQL and ferret full text search engines. For our project, the ferret was the winner. Nevertheless, I will try to show the beauty and simplicity of using MySQL indexes. Create table and indices First of all it is necessary to create table and the corresponding index. Create table articles( id integer not null primary key auto_increment, title varchar(20), body varchar(10
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