Python optimization is the solution to speed performance issues. But, when do you optimize, and what parts of the code should be optimized? This article will help you answer these questions. Developers always want to efficiently write neat code. However, things are quite different when working with a Python-based data science project. There will be situations where you need Python optimization. However, there are cases where optimization yields irrelevant results.

When a business application slows down, bad things happen. Your customer support gets slammed with service requests. Your boss calls an emergency meeting to talk to the product and developer teams. Everybody’s asking the same question: what happened? Diagnosing a slow application and finding the cause of the problem is something developers need to do quickly. Performance-related problems are in the top five SaaS user churn, which is a major preventable loss of revenue.

Website owners commonly ask why they need a website performance load tester. Throughout web history, some extremely popular sites have had their fair share of serious downtimes. Either they get massive traffic volumes, or their website simply crashes. Thus, web performance load tester services check the application’s ability to perform under anticipated user loads.

It is important that web applications are optimized to ensure the best application performance for the end user. PHP developers can improve website performance by load testing their PHP website. Load testing checks the rigidity, speed, interoperability of the system, and the performance and the reliability of a PHP website.

Website load testing services determine how websites deal with online traffic. Too often, people equate it with performance testing. However, load testing is just a type of website performance testing. Other types of performance testing include endurance testing, volume testing, scalability testing, spike testing, and stress testing. In website deployment, it is common that transactions may fail and systems crash. It is due to concurrent demands on the website and adjustment over resources.

One of the struggles developers face is how to catch all Python exceptions. Developers often categorize exceptions as coding mistakes that lead to errors when running the program. Some developers still fail to distinguish between errors and exceptions. In the case of Python application development, a python program terminates as soon as it encounters an unhandled error. So, to establish the difference between errors and exceptions, there are two types of errors.

In the vast ever-changing world of technology, Serverless and Faas are the two new categories of cloud computing services. Both FaaS and Serverless have helped organizations in saving money, refocusing developers’ time, relegating infrastructure management, and harnessing cloud technology. However, while both are treated as the same, there is a slight difference. Through this post, we will shed light on the similarities and differences between FaaS vs.

Software testing methods are essential in building software. It helps developers deal with different types of bugs. As we all know, these bugs may range from a missing semicolon to a critical business requirement. Thus, software testing becomes an essential part of a test driven development environment. We are in the era of process automation. Today, businesses are dependent on one or more software products. Therefore, software quality becomes crucial.

The year 2015 was marked by a new paradigm shift in web development. Google introduced Progressive Web Applications and JavaScript libraries and frameworks, such as React.js, Angular.js or Vue.js, created new development methodologies. By 2020, the web environment is flooded with PWAs based on reactive programming and built with cutting-edge frameworks.

For enterprise applications and startups to scale, they need to manage large volumes of data in real-time. Customers must have the ability to search for any product or service from your database within seconds. When you manage a relational database, data is spread across multiple tables. So, customers may experience lag during search and data retrieval. However, this is different with Elasticsearch and other NoSQL databases.