In this age of complex software systems, code instrumentation patterns define specific approaches to debugging various anomalies in business logic. These approaches offer more options beyond the built-in debuggers to improve developer productivity, ultimately creating a positive impact on the software’s commercial performance. In this post, let’s examine the various code instrumentation patterns for Node.js.
Sometimes, C/C++ projects have a long development cycle. When working on such a project, it can be easy to take our development environment for granted, and forget about the effort invested in its bring-up. The build environment works like magic, the test framework is neatly integrated, and the CI/CD pipeline relieves us of tedious, repetitive tasks.
Tracing and debugging microservices is one of the biggest challenges this popular software development architecture comes with - probably the most difficult one. Due to the distributed architecture, it's not as straightforward as debugging traditional monolithic applications. Instead of using direct debugging methods, you'll need to rely on logging and monitoring tools, coding practices, specific databases, and other indirect solutions to successfully debug microservices.
When it comes to debugging performance related issues, the range of these issues together with their root cause can be overwhelming to developers.
I’ve been a firmware engineer at two wearable companies in the past, Pebble and Fitbit, and there was always one class of customer support tickets and user complaints that never went away: issues around battery life. It was a constant game of whack-a-mole with every new firmware version introducing a battery regression.
What do you do when things break in production? Debugging microservices isn’t a walk in the park. Microservices are designed to be loosely coupled, which makes them more scalable and resilient, but also more difficult to debug. When a problem occurs in a microservices app, it can be difficult to track down the source of the problem. When the problem is in production, the clock is ticking and you have to resolve the issues fast.
