The performance of your application affects your business more than you might think. Top engineering organizations think of performance not as a nice-to-have, but as a crucial feature of their product. Those organizations understand that performance has a direct impact on user experience and, ultimately, their bottom line. Unfortunately, most engineering teams do not regularly test the performance and scalability of their infrastructure.
Software development and deployment contexts have changed considerably over the last decade with Agile approaches. Performance testing has had difficulty keeping up with modern testing principles and software development and continuous deployment processes.
Big Data is a big topic in software development today. When it comes to practice, software testers may not yet fully understand what is exactly Big Data. A tester knows that you need a plan for testing it. Since most Big Data lacks a traditional structure, how does Big Data quality look like? And what the are most appropriate software testing tools? This article tries to answer these questions.
James Waldrop of Twitter discusses the tools, process and philosophy that goes into performance testing at Twitter. Particular emphasis will be placed on the Iago open source load testing library, which he wrote to enable Twitter’s engineering teams to perform load tests before deploying code to production. This presentation dives into implementation details of some of these tests (including source code) and how complicating factors such as OAuth and arbitrary Thrift protocols are managed. Video producer: https://developers.google.com/google-test-automation-conference/ Slides: http://goo.gl/9VY2b
This blog post provides guidelines for scalability testing. It defines the difference between load, performance and scalability testing. A well-designed workload is the first requirement for any performance testing is a well-designed workload. The post provides hints how to to plan, run and analyze scalability tests.
Running stress- or load-tests of asynchronous REST/HTTP services with JMeter is only the first step in performance improvement. If the applications have problems when load increases, you need to find where the issues are. You can spend a lot of time to examine the code base before – if ever – finding the cause of the performance problem. This blog post provides an introduction on how to record and examine telemetry performance measurements with Yourkit3 after running JMeter tests.
In this blog post, Joe Colantonio explains in detail and clearly the concept of “throughput” in performance testing and and why it is one of the top metrics used to measure how well an application is performing.