Operations | Monitoring | ITSM | DevOps | Cloud

As is often the case with digital products, your users could be experiencing issues you might not be aware of. The unknown unknowns could include random bugs or memory leaks slowing down performance and, in many cases, those issues aren’t reported… folks just bail. If uptime is a core tenet of your business success, unreported issues and users moving on to the next best thing isn’t an option.

The following guest post addresses how to improve your services’s performance with Sentry and other application profilers for Python. Visit Specto.dev 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.

SDKs naturally increase in size over time. After all, it does take more bytes to implement more features. This is not a big deal for most languages—the relative size of each new feature is small, and load times and storage aren’t big concerns for code running on a server.

Python code optimization may seem easy or hard depending on the performance target. If the target is “best effort”, carefully choosing the algorithm and applying well-known common practices is usually enough. If the target is dictated by the UX, you have to go down a few abstraction layers and hack the system sometimes. Or rewrite the underlying libraries. Or change the language, really. This post is about our experience in Python code optimizations when whatever you do is not fast enough.

Many.NET applications and frameworks support a plugin based model. Also known as “add-in” or “extension” model. A plugin model allows extension or customization of functionality by adding assemblies and config files to a directory that is scanned at application startup. For example.

For most of my career I’ve worked with health and wellness startups. Most of these companies have a wearable that tracks movement, heart rate, body weight or stimulates a body part. The common denominator between these apps is their use of sensor data to determine physiological progress an athlete is making. Problem is, your Bluetooth Low Energy (BLE) device does not have an internet connection and cannot send diagnostics anywhere if there are errors.