Systems | Development | Analytics | API | Testing

In the part 1 of this series, we covered the fundamentals of caching (like fragment and Russian Doll caching). Before continuing, read that post here. This time, we’ll look at understanding Rails cache stores at a deeper level, so let’s jump in! Before we start part 2, let’s note that another way to make sure your Rails apps are ultra-fast is with Scout’s integrated monitoring and logging, so you can spot problems before your users do.

When it comes to optimizing web applications, a proper caching strategy is critical because it can significantly reduce load times and improve the overall user experience. This is especially true for Ruby on Rails applications, where performance can often experience bottlenecks due to heavy database queries or complex view rendering.

Ruby on Rails is an excellent choice for building a REST API, stemming from its design principles and robust feature set. It encourages a resource-oriented architecture, including built-in routing, migrations, and task tools. Rails also includes Active Record, an Object-Relational Mapping (ORM) layer, which simplifies database interactions.

Imagine you're building an e-commerce application. You want to use Django and bring all the benefits of that framework. But there are issues. Django's service-oriented architecture doesn’t play well with the event-driven nature of what you need to build. You need to automatically send a welcome email when a new user registers, instantly update inventory levels when an item is purchased, and notify users when their orders are shipped.

Let’s start with a statement everyone can agree on: AI coding is now a permanent fixture of the software development lifecycle. Even if your team hasn’t introduced it officially yet, it’s only a matter of time before one of your peers kicks off the discussion or pushes a PR that, perhaps even unbeknownst to you, is entirely AI-generated. Now, let’s rub some folks the wrong way: AI coding in a microservices architecture is a recipe for disaster.

Check out Scout Monitoring's ollama-rails GitHub repo for samples on how to use ollama-ai to communicate with Ollama. Large Language Models (LLMs) have emerged as a game-changer, enabling machines to understand, generate, and process human language with unprecedented accuracy and fluency. One such tool that has gained significant attention is Ollama, a cutting-edge platform that allows developers to run LLMs locally without relying on cloud services.

If you’re not building with AI, are you even building these days? Sometimes, it seems not. AI has become such an integral part of workflows throughout many tools that a clear understanding of integrating it into your product and framework is critical. Django is such a framework that powers thousands of products across the web: Instagram, Pinterest, and Mozilla are all services built on Django.

Background job processing is integral to modern software architecture. Background jobs allow resource-intensive tasks to be handled asynchronously, improving your application’s responsiveness and efficiency. You can use background processing for tasks such as sending emails, data processing, and batch jobs. If you were to run these synchronously, they could significantly degrade the user experience and system performance. Thus, most frameworks have libraries for running background jobs.

After Twitter moved from Ruby to Scala in 2009, the story was born that Ruby on Rails can’t scale. The story goes that it lacks robustness, is a memory hog, and lacks the concurrency features you need to grow an application. This has been the prevailing wisdom for over a decade. And then along came Shopify, showing that, as Lutke says, Ruby on Rails is a framework that can process billions of events per day and evidently does scale. Ruby on Rails is an excellent candidate for scaling.

Django is one of the leading Python frameworks used to create full-stack web applications. In this comprehensive guide, you will explore the intricacies of testing and debugging within the Django framework, focusing on advanced methodologies and essential tools. Beginning with the fundamentals of Django unit and integration testing, you will delve into advanced techniques such as mocking, testing middleware, and profiling for optimal performance.