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.NET: Article

Database Access Patterns Gone Wild

Inside Telerik, SharePoint and ASP.NET

Telerik Controls are great for building modern, rich clients, and web applications often seen at the enterprise level. I just worked with a company that uses these controls in their soon-to-be-released customer-facing SharePoint portal. They ran a small load test that showed that the response times were ranging between 150ms and 1.3s for most of their pages - which is acceptable. Still, they wanted my opinion on the data - so they shared a dynaTrace session with me. I observed a number of problems prior to the production deployment:

  • "Unbalanced" load balancer will bring a server-cluster down
  • Data driven performance problems will kill a database server
  • Wasteful database connection handling will impact app server performance

Check out the following screenshots that explain my steps and findings while I analyzed their performance data. I hope this is also a good guide for any of your own work you do on Telerik, SharePoint or any other frameworks. It is important to understand what happens underneath the hood - just because it works on a developer's machine or in a small scale load test, doesn't mean it will scale in production.

Step #1: Analyzing Transaction Flow
The transaction flow visualizes the flow of all requests end-to-end through the system. Not only is it interesting to see where my performance hotspots are but it is also very interesting to see how load is distributed, how these tiers are communicating with each other and how they interact with external systems such as the database.

Transaction Flow highlights load balancing, failures and bad database access patterns

Key Takeaways

  • Operations: Make sure your load balancers are correctly configured. You may also want to add a load balancer between your Web Server and App Server as currently there is a 1:1 relationship.
  • Developers: Analyze the performance hotspots and the database access pattern of your application. It should not be necessary to execute that many SQL statements per transaction.
  • Database Admins: Check with engineering on which database statements they execute a lot and which of them can be optimized on the database server.

Step #2: Analyze Database Access Pattern
The Database view shows me which SQL Statements are executed. It is especially interesting for me to identify SQL Statements that are executed multiple times per single request and whether these statements are then prepared or not. If the same statement is executed more than 5 times I wonder if this access pattern can't be optimized, e.g., through a different SELECT, through a stored procedure or by caching the result in the app.

Database view shows which SQL statements are called up to 90x on average per request. Most of them are not prepared

Key Takeaways

  • Operations: A good metric to monitor is the number of SQL Statements executed per transaction as well as the same SQL executed per transaction. As things like this can always slip into production it makes sense to monitor this for every transaction.
  • Developers: Optimize your database access. If these statements have to be executed that many times, make sure these statements get prepared. Otherwise think about caching this data in the app instead of requesting it all over again.

For the rest of the steps, and further insight, click here for the full article.

More Stories By Andreas Grabner

Andreas Grabner has more than a decade of experience as an architect and developer in the Java and .NET space. In his current role, Andi works as a Technology Strategist for Compuware and leads the Compuware APM Center of Excellence team. In his role he influences the Compuware APM product strategy and works closely with customers in implementing performance management solutions across the entire application lifecycle. He is a frequent speaker at technology conferences on performance and architecture-related topics, and regularly authors articles offering business and technology advice for Compuware’s About:Performance blog.

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