In this video, we are going to take a look at what memory bloat is, what causes it, and how you can use Scout to eliminate it from your applications. Memory related performance issues have the potential to bring your entire application down, and yet, most APMs completely ignore this fact and fail to provide any useful way of monitoring memory usage at all.
Data scientists today have to choose between a massive toolbox where every item has its pros and cons. We love the simplicity of Python tools like pandas and Scikit-learn, the operation-readiness of Kubernetes, and the scalability of Spark and Hadoop, so we just use all of them. What happens? Data scientists explore data using pandas, then data engineers use Spark to recode the same logic to scale or with live streams or operational databases.
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.
Tired of waiting for sluggish HTTP requests to complete before your backend code can proceed with other things? Sanic is an asynchronous web framework in Python, that is built to be fast. In a world where Flask and Django are the most preferred web development options in Python, Sanic is the new kid on the block. It’s a promising alternative that is not only faster but also delivers efficiency, simplicity, and scalability.
Like many things in life, the toughest part of a test automation project can be getting started. That's why the Solution Architects and Information Developers are working hard to produce good materials for anyone getting started with test automation and Sauce Labs. To help this effort, we've reworked some Python example code, which can be found in the Sauce Labs Training Demo Python repository.
Python powers many applications we use in our day-to-day like Reddit, Instagram, Dropbox, and Spotify. The adoption of Python 3 has been a subject of debate in the Python community. While Python 3 has been out for more than a decade now, there wasn’t much incentive to migrate from the stable Python 2.7 in the earlier releases. If you’re still running on legacy python, it’s high time to migrate as it has reached the end of its life since Jan 2020.
