In Python development, logging is not only good practice; it is vital. Logging is critical for understanding the execution flow of an application and helps in debugging potential issues. The importance of logging for developing reliable and maintainable Python applications cannot be overstated. Python provides capabilities for running concurrent operations—either in a threaded (single process) or multiple process environment. But what implications do these different approaches have on logging?

While support for logging in the OpenTelemetry Python project is listed as 'experimental,' it's completely possible to send logs from your Python application. The Opentelemetry Collector has support for numerous existing logging systems, effectively exporting log data from wherever you were sending logs currently; you can also use the filelog receiver to tail and send logs from files. The only 'experimental' portion of the Python SDK is sending logs directly from code-level instrumentation.

In this article, learn how to setup application monitoring for Python apps using an open-source solution, SigNoz.

Okay, we are back for Part 2! Last time we discussed the new community Python library for InfluxDB 3.0. Let’s talk about a bolt-on application that uses the client library as the core of its development, the InfluxDB 3.0 Python CLI.

Community Client libraries are back with InfluxDB 3.0. If you would like an overview of each client library then I highly recommend checking out Anais’s blog on their status. In this two-part blog series, we do a deep dive into the new Python Client Library and CLI. By the end, you should have a good understanding of the current features, how the internals work, and my future ideas for both projects.

Python is a highly skilled language with a large developer community, which is essential in data science, machine learning, embedded applications, and back-end web and cloud applications. And logging is critical to understanding software behavior in Python. Once logs are in place, log monitoring can be utilized to make sense of what is happening in the software. Python includes several logging libraries that create and direct logs to their assigned targets.

Code instrumentation is an essential practice in modern software development. Not only does it aid in debugging, it ultimately impacts the MTTR (Mean Time to Resolve) for software running in production. With changing software architectures and deployment patterns over the years, approaches to code instrumentation have also undergone a significant shift.

This post was written by Mercy Kibet, a full-stack developer with a knack for learning and writing about new and intriguing tech stacks. In today’s digital world, software applications are becoming increasingly complex and distributed, making it more challenging than ever to diagnose and troubleshoot issues when they arise.