Are you taking data from an API in the format the web services gives it to you? You should not dictate the structure of data inside your application based on how an API provider structures their data. Instead, you can take advantage of the power of Python's list manipulation techniques to sort, filter, and reorganize data in ways that best suit your needs.
Salt is ending a journey that started three years ago with the first pull request to add support for PyTest. In the upcoming weeks, Salt will finally switch to PyTest as its sole test runner, as the previous and heavily customized unit test runner, runtests.py, finally reaches EOL.
As most developers will agree, writing code is oftentimes, if not always, easier than debugging. As a simple definition, debugging is the process of understanding what is going on in your code. When speaking in terms of Python, it is a relatively simple process. Every developer has their own personal debugging method or tool they swear by. When it comes to Python, most developers use one (or more) of the following: print statements, traditional logging, a pdb debugger, or an IDE debugger.
Scripting languages (VBScript, Python, PowerShell etc) are both flexible and convenient to create small scripts, to handle a simple monitoring task (such as poll a device for data or execute custom alert). Python has an advantage of being general purpose cross-platform scripting language for years, with many well-known scripts either already available on the Net, or quick to compose.
Learn how to monitor Lambda functions written in Python with AppDynamics Python Tracer SDK.
This post is a brief summary of a presentation I gave recently where I deploy Elastic App Search, show off the ease of setup, data indexing, and relevance tuning, and take look at a few of the many refined APIs. It’s also written up in a codelab with step-by-step instructions for building a movies search engine app using Python Flask. The app will work on desktop or mobile and is a fast, simple, and reliable way to query the information.
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.
