Operations | Monitoring | ITSM | DevOps | Cloud

In this tutorial, we will discuss InfluxDB and its Python client. We will deploy InfluxDB inside a Kubernetes cluster and then use the InfluxDB Python client to send data to InfluxDB.

The realm of computing is endlessly vast, and programmers can utilize numerous programming languages that facilitate logging. Novice programmers often wonder, why is Python considered the ideal language to learn logging?

This tutorial focuses on how to write a promise module, implementing a new CFEngine promise type. It assumes you already know how to install promise modules and use custom promise types, as shown in the previous blog post.

Python is not known to be a "fast" programming language. However, according to the 2020 Stack Overflow Developer Survey results, Python is the 2nd most popular programming language behind JavaScript (as you may have guessed). This is largely due to its super friendly syntax and its applicability for just about any purpose.

One of the struggles developers face is how to catch all Python exceptions. Developers often categorize exceptions as coding mistakes that lead to errors when running the program. Some developers still fail to distinguish between errors and exceptions. In the case of Python application development, a python program terminates as soon as it encounters an unhandled error. So, to establish the difference between errors and exceptions, there are two types of errors.

According to the Stackoverflow survey of 2019, Python programming language garnered 73.1% approval among developers. It ranks second to Rust and continues to dominate in Data Science and Machine Learning(ML). Python is a developers’ favorite. It is a high-level language known for its robustness and its core philosophy―simplicity over complexity. However, Python application’s performance is another story. Just like any other application, it has its share of performance issues.

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.

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.