Have you ever had a tough time debugging your Python code? If yes, learning how to set up logging in Python can help you streamline your debugging workflow. As a beginner programmer, you’ll have likely used the print() statement—to print out certain values across runs of your program—to check if the code is working as expected. Using print() statements to debug could work fine for smaller Python programs.
This post is part of the MAUI UI July community series of blog posts and videos, hosted by Matt Goldman. Be sure to check out the other posts in the series! Hi, my name is Matt Johnson-Pint. I recently joined Sentry as an engineer working on the Sentry.NET SDKs. One of my first big projects was adding support for.NET MAUI, which we’ve now launched in preview. Go ahead, give it a try!
Developers started to notice just how big our JavaScript package was and yeah, we knew. We weren’t ignoring the issues; after all, we don’t want the Sentry package to be the cause of a slowdown. But to reduce our JavaScript SDK package size effectively we had to account for shipping new capabilities, like being able to manage the health of a release and performance monitoring, while maintaining a manageable bundle size. After all, new features == bigger package - usually.
In many critical areas, you can automate the completion of repetitive chores in an efficient and effective manner by using a computer language such as Python. When you are just starting out, it’s vital to understand the fundamentals of Python via coding examples. However, if you want to improve your Python skills, you should concentrate on constructing things and automating real-world tasks.
In many critical areas, you can automate the completion of repetitive chores in an efficient and effective manner by using a computer language such as Python. When you are just starting out, it’s vital to understand the fundamentals of Python via coding examples. However, if you want to improve your Python skills, you should concentrate on constructing things and automating real-world tasks.
Nobody likes using an unstable mobile app or even worse, an app that crashes on them. In fact, 9 out of 10 US and UK consumers report uninstalling a mobile application due to poor performance. Crash rates and snappy experiences matter for all applications, but especially for mobile apps. Mobile app crashes and poor performance not only cause users to abandon an app but can also trigger the app to be ranked lower in Apple App Store and Google Play Store search results.
Clever.fm is developing a platform to connect podcast listeners with community and content to make podcasting financially sustainable for creators. Our application provides features such as capturing moments, sharing snippets, and having discussions on podcast episodes.
The following guest post addresses how to improve your services’s performance with Sentry and other application profilers for Python. Check out this post to learn more about application profiling and Sentry’s upcoming mobile application profiling offering. We’re making intentional investments in performance monitoring to make sure we give you all the context to help you solve what’s urgent faster.=
Unless you stare at Sentry all day waiting for error or performance problems to pop up, chances are you rely on alerts to let you know when something breaks or slows down. There are two types of alerts you can set up: 1) Issue Alerts, which are tied to issues that meet predefined criteria, and 2) Metric Alerts, which trigger when error volume or performance metrics like apdex, latency, or throughput surpass a pre-defined threshold.
Monitoring performance in front-end applications is vital. It focuses on the areas of the application users experience. These areas are slow rendering or frame rate, network request errors, and unresponsive user experience. Your application’s users expect the experience of using the application to be fast, responsive, and smooth. In the first article of this series, we discussed some tips for optimizing your React Native application performance.
