SphereConsulting

White papers

We create applications that last. We share our knowledge with you in our white papers.

• Architecture of High Traffic Messaging System using Ruby On Rails (Size: 542 Kb)

  Author(s): Yuri Lapitsky

  Date published: July 2010

  
  

  Modern high performance web applications require special architecture decisions. This paper describes one of possible approaches for implementing cost-efficient high traffic messaging system using open source stack of technologies and cloud computing platform. The document is based on successful real word application that was developed by Sphere Consulting Inc. using Ruby on Rails framework.

• 5 ways to boost the performance of your Rails apps (Size: 380 Kb)

  Author(s): Oleg V. Olshanetsky and Pavel Timofeev

  Date published: July 2010

  
  
  

  In this work we did a research on different approaches that can be used in improving performance of your Rails applications and techniques for scaling Rails for high load websites.

• QA for Rails (Size: 1259 Kb)

  Author(s): Svetlana Kostikova and Bogdan Shchepanskiy

  Date published: June 11, 2010

  
  
  With today’s cutthroat competition from big business, trying to compete on price can be a quick road to ruin for even a solid growing midsized company. But here’s a little secret we can share: contrary to common perception, customers will not go almost anywhere just to save a buck.

  So if you want to avoid getting beat up on price, stop trying to compete on price alone. Compete on Quality and on the Process of Communication and Delivery of the project to your customer.

  The attached presentation describes Sphere Consulting approach in testing Applications on Rails. Please contact us at +1-312-436-0165 to know more about how we can deliver your Software Faster and with Exceptional Quality.

• Approaches to Query Optimization (Size: 294 Kb)

  Author(s): Oleg V. Olshanetsky

  Date published: May 7, 2010

  The goal of this document is describe the directions for optimization for SQL queries against large and/or distributed databases based on Oracle, DB2 or Teradata SQL servers. For large-scale databases with complex data model the built-in optimizers often do not provide the most optimal performance, leading to avoidable expenses for database server hardware and software licenses. Therefore the main driver behind this effort was the need to improve on performance of the built-in optimizers in these databases.