Improving Database Performance with Rails

Known for its high standards of efficiency, Ruby on Rails provides a number of ways to further optimize its database queries and structure. Getting better results from your database requires a deeper understanding of how the framework interacts with mySQL as well as potential solutions to speed up the interactions. Even as Rails 3.0 Beta vastly improves many of the database integration issues, many developers on Rails 2.x versions can benefit from optimization as well as a deeper understanding of the solutions involved.

At a basic level, the way controllers are setup on Rails databases leaves much to be desired out of the box.  For example suppose you have a large number of records in a specific database and want to utilize associates to call certain instances on the table. If you’re looking at a list of top rated users (for a social or gaming site) you can use a basic query to sort through the top rates users @user = user.find(:all) but this will take a long time to process through all of the records depending on the exact nature of your query. To streamline the database lookup you can refine this query to accelerate selection of the right fields.

In order to optimize the database queries you can refine this query to limit the fields selected so you can both scale your database and improve site performance at the same time. You can use the include and select functions so you can load the specific columns in your table which matter specifically for the query. Join function sallow you to combine multiple SQL statements so you can jointly determine the best solution and improve load times. You can then refine the query with a combined user:user_level with a joint call to the database that optimizes performance:

@user = user.find(:all, :select => ‘user.names,user.scores AS user_level’, :joins => [:level])

As a general best practice it’s smart to think about queries in the context of an exclusion standard: the better you understand what you’re trying to find, you can then optimize the query based upon how you can better focus your queries. You should never assume that a particular query is optimized until you benchmark and log the exact line-level load times on your site – it’s important to always test iterations and versions to identify areas of potential improvement within your site.  Many developers believe they have identified weaknesses in their queries only to realize their load times become larger after making what they thought were optimizations.

Identifying potential improvements in a database it’s important to consider structural from query optimization to best determine the most effective updates. Suppose you want to combine a query with three unique WHERE clauses, pulling data from three unique tables. In this case you may find that query optimization doesn’t go far enough and that you should optimize your database table structure to combine related columns into a single table for references.  When calling multiple data sources via independent tables or APIs, it’s important to consistently test potential sources for improvement as you move forward.