Sentry recently launched Performance Issues, a feature to help developers discover and fix common performance problems in their projects. We tested this project internally and with alpha users, so when we finally turned it on for all Sentry users, we were delighted (and dismayed) to hear from Carlton Gibson, current Django fellow and great human, that Sentry had.
Today we are releasing Dynamic Sampling, available to all new customers, and opt-in for existing customers. This goes beyond a new feature however and is an overhaul to the way we package Sentry’s Performance Monitoring product. We are saying goodbye to the days of static, magic number sampling configured within the SDK and moving to a world of flexibility.
Sentry is an open source company, and it’s important to us to financially support our non-commercial colleagues in the community as we continue to enjoy commercial success. We’ve given money forever, but last year we really got organized and gave $154,999.89 to 108 recipients. Two points make a line, and this year we are back with a continuation of the industry-leading open source funding program we put in place last year.
In the first three parts of this series around improving performance in your Django applications, we focused on database, code optimization, and frontend optimization. In part 4, we will focus on ways to improve the speed of the Django applications.
Sentry Alerts ping you on Slack, Microsoft Teams, or Pager Duty when something goes needs your attention. However, too many alerts can turn your notification channel into an endless noise feed. I spoke with dozens of Sentry customers in the past 6 months, and something I heard over and over again was “Sentry can get noisy at times” and “There are days I can’t keep up with Sentry notifications because we get so many of them”. Does this sound familiar?
Profilers measure the performance of a program at runtime by adding instrumentation to collect information about the frequency and duration of function calls. They are crucial tools for understanding the real-world performance characteristics of code and are often the first step in optimizing a program. Apple and Google have first party profiling tools, but they are only usable for local debugging during development.
Forethought is a leading AI company providing customer service solutions that transform the customer experience. As a high-growth startup with a fast-expanding engineering org., teams had to deal with compounding complexity, leading to challenges measuring the impact and health of their services. Forethought’s core engineering team maintains common services between other internal teams, infrastructure, data, and tools and, as they added more engineers, the original team split into five.
The following guest post addresses how to improve your services’ performance with Sentry and other application profilers for Unity. Learn more about Sentry’s Profiling product or try it out now if you’re already a Sentry user. We’re making intentional investments in performance monitoring to give relevant context to help you solve what’s urgent faster.
Exceptions are the outcomes you do not usually expect in your application. But as a developer, expecting the unexpected is essential to capture exceptions and handle them appropriately. Exception handling is not only applicable to web development projects but also to Unity applications. This article brings everything you need to know as a beginner to Unity exception handling, including methods to handle exceptions, when to use them, and how to manage exceptions easier using distributed logging.
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.
