With the increasing popularity of Python web frameworks supporting asynchronous I/O like FastAPI, Starlette, and soon in Django 3.1, there has been a growing demand for native async I/O support in the Python Elasticsearch client. Async I/O is exciting because your application can use system resources efficiently compared to a traditional multi-threaded application, which leads to better performance on I/O-heavy workloads, like when serving a web application.

All programming languages provide efficient data structures that allow you to logically or mathematically organize and model your data. Most of us are familiar with simpler data structures like lists (or arrays) and dictionaries (or associative arrays), but these basic array-based data structures act more as generic solutions to your programming needs and aren’t really optimized for performance on custom implementations. There’s much more than programming languages bring to the table.

Web frameworks are powerful tools. They abstract the common aspects of building web sites and APIs and allow us to build richer, more stable applications with less effort. A broad range of web frameworks is available to us in Python. Some are proven favorites with large ecosystems and communities. Others excel in niche use cases or for specific kinds of development. Still, others are up-and-comers with compelling new reasons to be considered.

Our instrumentation uses built-in extension mechanisms where possible, such as Django’s database instrumentation. But often libraries have no such mechanisms, so we resort to wrapping third party libraries’ functions with our own decorators. For example, we instrument jinja2 ’s Template.render() function with a decorator to measure template rendering time. We value the correctness of our instrumentation a lot so that we do not affect our users’ applications.

This guide is focused on how to log in Python using the built-in support for logging. It introduces various concepts that are relevant to understanding Python logging, discusses the corresponding logging APIs in Python and how to use them, and presents best practices and performance considerations for using these APIs.

About 90% of all Lambda functions monitored by Dashbird on AWS Lambda are running Nodejs and Python runtimes. Is this purely a reflection of the general popularity of these programming languages?

Earlier this year Python 2 entered End of Life — and Splunk has already released versions of Splunk Cloud and Splunk Enterprise that provide a Python 3 runtime. As the developer of an app that is published to Splunkbase, if your app contains Python code, you need to update it to work with Python 3 and Splunk Enterprise 8.0 by July 1, 2020 as the Splunk Enterprise and Splunk Cloud releases after that date will no longer support the Python 2 runtime.

Logging is a feature that virtually every application must have. No matter what technology you choose to build on, you need to monitor the health and operation of your applications. This gets more and more difficult as applications scale and you need to look across different files, folders, and even servers to locate the information you need. While you can use built-in features to write Python logs from the application itself, you should centralize these logs in a tool like the ELK stack.