Operations | Monitoring | ITSM | DevOps | Cloud

Python

How to instrument your Python application using OpenTelemetry

If you want to see if OpenTelemetry helps you become a better Python developer — or if you just want to know how to add OpenTelemetry to your Python service — you’ve come to the right place. In this blog, we’ll show you how to instrument your Python application using OpenTelemetry and how to visualize your OpenTelemetry data using Application Observability in Grafana Cloud. We’ll walk you through the following steps.

Monitoring apps based on Falcon Web Framework with OpenTelemetry

Falcon is a minimalist Python web API framework for building robust applications and microservices. It also compliments many other Python frameworks by providing extra reliability, flexibility, and performance. Using OpenTelemetry, you can monitor your Falcon applications for performance by collecting telemetry signals like traces. Instrumentation is the biggest challenge engineering teams face when starting out with monitoring their application performance.

Getting Started with Elasticsearch and Python

In the ever-evolving landscape of data management and analytics, the integration of Python with Elasticsearch stands out as a game-changer. Elasticsearch, renowned for its robust distributed search and analytics capabilities, finds a powerful ally in Python through the Python Elasticsearch client. Elasticsearch is an open-source, distributed search and analytics engine known for its scalability and real-time capabilities.

A Basic Introduction to OpenTelemetry Python

Think of a tool that simplifies application monitoring and helps developers and staff trace, collect logs and measure performance metrics. That is what OpenTelemetry Python provides. OpenTelemetry (OTel) Python acts as a guiding light, offering insights into the behaviors and interactions of complex, distributed systems and enabling a deeper understanding of performance bottlenecks and system dependencies. The significance of OTel lies in its pivotal role in modern software development.

Deploying a Python Application with Kubernetes

A powerful open-source container orchestration system, Kubernetes automates the deployment, scaling, and management of containerized applications. It’s a popular choice in the industry these days. Automating tasks like load balancing and rolling updates leads to faster deployments, improved fault tolerance, and better resource utilization, the hallmarks of a seamless and reliable software development lifecycle.

Testing a PyTorch machine learning model with pytest and CircleCI

PyTorch is an open-source machine learning (ML) framework that accelerates the path from research prototyping to production deployment. You can work with PyTorch using regular Python without delving into the underlying native C++ code. It contains a full toolkit for building production-worthy ML applications, including layers for deep neural networks, activation functions and optimizers. It also has associated libraries for computer vision and natural language processing.

AppSignal Monitoring Available for Python Applications

We're happy to announce that AppSignal now offers monitoring tools for Python projects. AppSignal helps you get the most out of your Python application's monitoring metrics, with additional support for multiple Python frameworks and packages such as Django and Celery. In this article, we'll walk you through some of our core features to show you how to power up your Python application with AppSignal.

Querying Arrow tables with DataFusion in Python

InfluxDB v3 allows users to write data at a rate of 4.3 million points per second. However, an incredibly fast ingest rate like this is meaningless without the ability to query that data. Apache DataFusion is an “extensible query execution framework, written in Rust, that uses Apache Arrow as its in-memory format.” It enables 5–25x faster query responses across a broad range of query types compared to previous versions of InfluxDB that didn’t use the Apache ecosystem.

Sept 13, 2023: SF Python Meetup - API Documentation: How Sentry Designed Custom Tooling

On September 13, 2023, Sentry hosted SF Python for a developer meetup in San Francisco. In this talk, Josh Ferge, Senior Software Engineer at Sentry, shared his experiences and insights on Sentry's journey of API documentation for their Django application. He talked about the various things they’ve tried, including: Schema / Example generation using dynamic tests; Writing OpenAPI JSON manually; Django Rest Framework & autodoc tooling around it; Problems with DRF serializers & performance, leading to Sentry custom implementation of schema generation using Python typing.