Privacy Policy

Last updated: September 05, 2026

This blog (letsarchitecttheperformance.blogspot.com) is hosted on Blogger, a service provided by Google. This page explains what data is collected here and how it's used.

Cookies and data collection

Blogger and Google may use cookies and similar technologies to serve the site, analyze traffic, and (where enabled) serve ads. Google's use of advertising cookies enables it and its partners to serve ads based on your visits to this site and other sites on the Internet. You can opt out of personalized advertising by visiting Google's Ads Settings.

Third-party advertising

This site may display ads served by Google AdSense. Google, as a third-party vendor, uses cookies to serve ads. Google's use of advertising cookies enables it and its partners to serve ads to visitors based on their visit to this site and/or other sites on the Internet. You may opt out of the use of the DoubleClick cookie for interest-based advertising by visiting Google's Ads Settings or aboutads.info.

Comments

If comments are enabled, Blogger's commenting system may collect the name, email, and content you submit with a comment.

Contact

Questions about this policy: neerajmanitripathi@gmail.com

Comments

Post a Comment

Popular posts from this blog

Performance Testing 102: Little's Law and It's usage in Performance Testing

  Little's law is a fundamental principle in queuing theory that states the average number of customers in a stable system (L) is equal to the average arrival rate (λ) multiplied by the average time the customers spend in the system (W). It can be mathematically represented as L = λW. This law is named after John Little, who first described it in 1961. It applies to a wide range of systems, including manufacturing, transportation, and service systems, and has many practical applications in performance engineering and testing. Additionally, it's important to keep in mind that Little's Law can also be applied to other areas of performance engineering such as capacity planning, server sizing, and bottleneck identification. By understanding the relationship between the number of requests, the number of requests being serviced, and the average response time, performance engineers can make informed decisions about how to optimize and scale their systems to meet the demands of the...
Read more

Performance Testing 104: Workload Modelling Designing & Process

Image
What is Workload? Performance Test Workload refers to the distribution of load across the identified scenario. Performance tester prepares a workload to simulate the real-world situation in the performance test environment. In performance testing cycle, different workloads are created to study the behavior of the system under various loads and conditions. Sometimes the workload is also known as a scenario. What is Workload Modelling? The process of creating a performance testing scenario with the help of non-functional requirements and performance test scripts is known as Performance Test Scenario creation or Workload Modelling. Purpose of Workload Modelling Workload Modelling helps to generate the expected production-like conditions in the performance test environment to check the true performance of the application. Another purpose is that when there are some production performance issues detected in the live environment then workload modelling helps to replicate the same condition...
Read more

Mastering the Art of Scaling in SaaS Applications

  In today's digital age, SaaS applications are synonymous with agility and adaptability. Their very essence lies in their capacity to scale and meet burgeoning user demands seamlessly. In this detailed guide, we'll explore the nuances of scaling, its impact on performance, and real-life instances of scaling in action. The Genesis: Understanding Scaling Scaling, in the world of SaaS, refers to an application's ability to accommodate an increase in user load, ensuring that as demand surges, the system remains both responsive and performant. It’s the invisible cog ensuring your application runs smoothly, regardless of the number of concurrent users. The Two Pillars of Scaling : Horizontal Scaling (Scale-Out) : Definition : Envision adding more tables (machines) to a vast dining hall (system). Each table can serve a set number of guests (workloads). Advantage : It's like having multiple mini-stores instead of one mega-store. If one store closes, others continue to operate....
Read more