Python is a highly versatile language. From software engineering to machine learning and data analysis, it’s everywhere. As a multipurpose scripting and programming language, it’s often utilized for manipulating and working with data. So, when you’re working with Python, whether you’re analyzing data or writing scripts, you’re likely to encounter dates and time stamps.
In the eternal hunt for elusive bugs, logging is an indispensable aid. By recording the events and messages that occur during the execution of your program, logging opens the door to unparalleled debugging and performance monitoring capabilities. It all starts with Python’s built-in logging module. However, the true power of Python logging is unlocked not merely by using it, but by mastering it.
Microservices architecture has become the new norm for modern applications due to its numerous advantages compared to traditional monolithic architecture. However, microservices also come with several challenges. Especially when it comes to observability, traditional monitoring tools and techniques can no longer handle microservices’ distributed and dynamic nature.
Python app monitoring and debugging can be challenging. Using distributed tracing and OpenTelemetry visualization minimizes MTTR and improves developer experience, as seen in this article.
Logging is the process of keeping records of activities and data of a software program. It is an important aspect of developing, debugging, and running software solutions as it helps developers track their program, better understand the flow and discover unexpected scenarios and problems. The log records are extremely helpful in scenarios where a developer has to debug or maintain another developer’s code.
