Delivering Scalable and Highly Available Session Management with MySQL Cluster

As organizations seek to enhance their users’ web experience through personalization based on historic browsing and buying behaviors, so session data is becoming more critical. Larger volumes of session data need to be managed and persisted in real-time, and so session management has become increasingly performance-intensive, while also demanding very high levels of availability to ensure a seamless customer experience. In these scenarios, it makes sense to evaluate the MySQL Cluster database.

This is practical session, demonstrating how to manage PHP session data with MySQL Cluster, and includes:

  • Demands of session management
  • Brief review of MySQL Cluster architecture
  • Why MySQL Cluster is well placed to meet the demands from session management
  • Getting a session management solution up and running with PhP and MySQL Cluster
  • Performance optimization, including the latest enhancements to JOIN performance
  • Case studies
Photo of Mat Keep

Mat Keep


Mat is part of the MySQL Cluster product team, responsible for product strategy and community development. In addition to driving adoption of MySQL Cluster into telecoms, web and enterprise industries, Mat is also involved in gathering user feedback and roadmap planning/ implementation with the engineering groups. Based out of the United Kingdom, Mat became part of MySQL in September 2008. Prior to that, Mat held a series of product management and development positions with Sun Microsystems and other vendors.

Photo of Bernd Ocklin

Bernd Ocklin

Oracle Svenska AB

Bernhard Ocklin, MySQL Cluster Engineering Director responsible for the team of MySQL Cluster developers, joined the MySQL organization as of September 2005. He first worked as a solution architect for high available database and cluster solutions and is now responsible for MySQL Cluster development at Oracle. To his credit, Bernhard has designed and programmed data mining software in addition to writing a database core prior to using MySQL databases. The outcome of such work has proven extremely useful in big scale data processing, i.e., for fraud detection at the world’s largest derivatives exchange.

  • EnterpriseDB
  • Amazon Web Services
  • Clustrix
  • Continuent
  • Facebook
  • HTI Consultoria e Tecnologia
  • Monty Program
  • Percona
  • Rackspace Hosting
  • Schooner Information Technology
  • SkySQL
  • Xeround

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