Exception handling in Java isn’t an easy topic. Beginners find it hard to understand and even experienced developers can spend hours discussing how and which Java exceptions should be thrown or handled. That’s why most development teams have their own set of rules on how to use them.
It’s one thing to write code that works. But what about clean, readable, concise code? That’s another thing entirely. To create an app that solves one problem? Not that hard. What about one that not only solves the problem, but it’s also easy and pleasurable to use? Now we’re talking. You could apply the same reasoning for many software properties, which would make for a long article. Instead, let’s focus on only one of those properties: performance.
Software is a complex thing. As soon as you deploy an application to production—especially when you don’t have any control over the environment it’s running on—anything could happen. You’ve created this “monster” and set if free. It’s now free from your control. How do you tame this beast before it creates havoc? The first step is to trace its steps (no pun intended).
Test automation plays a significant role in software development. It improves speed, better detects errors and bugs, is repeatable, scalable, has broader coverage, and more. Let’s take a look at test automation using a CI/CD pipeline. In today’s competitive world, great speed in developing software, features, and fixes are vital.
You’ve already used PHP streams in your PHP development. However, they’re transparent, so you may not have noticed them. Streams are powerful tools. In this article, you’ll learn to harness the power of streams in your PHP development and take your applications to the next level. Streams provide on-demand access to data. This means you don’t need to load the entire contents of your dataset into memory before processing can start.
Python is a powerful and versatile higher-order programming language. Whether you’re developing a web application or working with machine learning, this language has you covered. Python does well at optimizing developer productivity. You can quickly create a program that solves a business problem or fills a practical need. However, the solutions you reach when developing quickly aren’t always optimized for python performance.
The last decade has ushered in a golden era of software engineering. The rise of cloud computing freed companies from managing their own data centers and provided on-demand scaling. These services allow for provisioning servers on the fly using configuration and code. Treating that task as just another type of software development led to the advent of DevOps.
The shift to cloud computing and the DevOps revolution have fueled some important changes in the way we think about software development and monitoring. It has delivered huge benefits to the companies that have fully embraced the approach. In fact, the DevOps Research and Assessment (DORA) 2018 industry survey found a new small group of “elite” performers that are deploying code far more often and having a far better mean time to resolution (MTTR) than the next closest group.
Mean time to resolution (MTTR) is defined as the sum of the total amount of time that service was interrupted divided by the number of individual incidents. The unit of measurement is some quantity of time. Ideally, you can use minutes as the unit. That is, unless you blacked out the eastern seaboard for weeks!
