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We are thrilled to announce a major update to our Python Connectors line. The release adds support for Python 3.14, PostgreSQL 18, MySQL 9, and MariaDB 12, introduces modern authentication and security options, and delivers notable performance gains across several connectors.

Here's some excellent news for the Pythonistas in the room: You can now monitor the performance of your Python applications with Honeybadger. Last year, we launched Honeybadger Insights, a new logging and observability tool bundled with Honeybadger. Insights enables you to query your application logs and events to answer performance questions, perform root-cause analyses, and create charts and dashboards to see what's happening in real time.

When your Python app starts slowing down, maybe queries are taking longer, memory keeps creeping up, or API calls are lagging—basic server metrics won’t tell you why. You need to see what’s happening inside the application itself. That’s the role of Application Performance Monitoring (APM). It gives you a breakdown of database queries, external API calls, memory usage, error rates, and more, so you can connect the dots between code and performance.

A recent study looked at real-world Python programs and found something important: Python isn’t the main reason apps slow down. The real problems come from inside the code like poor logic, memory issues, and slow database queries. The problem is, these issues often go unnoticed. Your app may seem fine until users start complaining about slowness or things start breaking under pressure.

Businesses across industries rely on time-stamped data to track system health, monitor performance, and improve operations. Whether it’s sensors on a factory floor or usage logs from a SaaS platform, time series data reveals how things change. As businesses digitize operations and add connected devices, sensors produce growing streams of time-based data. This opens the door to faster analytics and smarter automation. But legacy approaches can’t keep up.

If you’re using Python for search, log aggregation, or analytics, you’ve probably worked with Elasticsearch. It’s fast, scalable, and fairly complex once you go beyond the basics. The official Python client gives you raw access to Elasticsearch’s REST API. But getting it to work the way you want, especially under load, can be tricky. This blog walks through practical ways to index, query, and monitor Elasticsearch from Python code, without getting lost in the docs.

Open-source package repositories like the Python Package Index (PyPI) play a crucial role in software development. However, these platforms are also potential targets for malicious actors attempting to exploit application software vulnerabilities. The JFrog Security Research team regularly monitors open source software repositories using advanced automated tools, in order to detect malicious packages.