Serious software development calls for performance optimization. When you start optimizing application performance, you can’t escape looking at profilers. Whether monitoring production servers or tracking frequency and duration of method calls, profilers run the gamut. In this article, I’ll cover the basics of using a Python profiler, breaking down the key concepts, and introducing the various libraries and tools for each key concept in Python profiling.

As applications are getting more complex, it’s becoming harder to deliver high-quality applications. Tools likeJavaScript has come a long way in recent years. Browsers are becoming more robust and machines are growing more powerful. Pair this with the recent development of Node.js for execution of JavaScript on servers, and you can understand why JavaScript has exploded in popularity.

As applications are getting more complex, it’s becoming harder to deliver high-quality applications. Tools like Application Performance Monitoring (APM) are essential for the development process. To get good performance data, developers need to deal with the rising trends of containerization, microservices, heterogeneous cloud services, and big data.

PHP is one of the most popular server-side scripting languages for building web applications because it’s easy to use. No matter what you build, making it easy to log errors is key to ensuring a short code-test-learn feedback cycle.

Logging provides critical value to applications with insight to usage, stats, and metrics, and saves us when debugging a problem. But we often leave logging to poorly implemented afterthoughts. So what should we know to get the most out of our logging? We will look at the Rails logger and some logging best practices.

When developing PHP applications, error logs tend to be underutilized due to their apparent complexity. The reality is that PHP error logs are extremely helpful, especially when configured and used properly. While there are advanced tricks to truly squeeze every last drop of utility out of error logs, this article will cover the basics of configuration and the most common use cases so you can get up and running quickly.

Troubleshooting is a critical skill for developers and DevOps. As our software grows more sophisticated, our problems do too. The cloud adds a new layer of complexity since we need to know how to find a problem that doesn’t occur locally, but has cropped up on a remote system.

Many online applications use a web server as the primary point of contact for their clients. At least 43% of those systems are running the Apache HTTP Server. If you’re responsible for one of those systems, you need to work with the Apache error log.

If you’re a software developer, then you understand how vital application logging is in software development and a critical part of logging is something called logging levels. Log entries generally contain essential information—such as a timestamp, a message, and sometimes additional stuff like an exception’s stack trace. Those pieces of information are useful because they allow someone reading the log entry to understand how the application behaved in production.

Garbage collection is a key component of many modern programming languages, including C#. It’s even hard to imagine what programming would look like in C#, and other modern languages like Java, Ruby, and many others, without this tool.