The release of Java 25 (including GraalVM) and recent years of Java startup performance improvements inspired me to re-evaluate choices in Java web frameworks in low-usage scenarios such as scale-to-zero apps, where Java’s slow startup is a problem. The “industry standard” Spring Boot has improved considerably since I first started using it while alternative frameworks like Quarkus and Micronaut have also matured greatly. I want to check how fast these can start and how much memory they need as a baseline.

In this post I compare the startup time and memory usage for these 3 frameworks in a simple application using JDBC, Flyway, and Postgres. The database has a single table and the application serves both a JSON API and a server-side rendered HTML via a templating engine. I used what I felt are “typical” libraries for each framework based on its common tutorials and project generation tool defaults, aiming to capture “typical” usage rather than what is possible with targeted optimizing. For example, Spring Boot is usually lighter with Undertow instead of the Tomcat default, while Micronaut and Quarkus already default to lighter-weight engines (Netty and Vert.x). In Micronaut, I used Micronaut Data JDBC versus Hibernate in Spring Boot and Quarkus.

Full details are provided later in the post, but as it’s long I’ll start with the results as run on an M2 Pro Macbook with 16GB of memory and GraalVM 25:

A common mistake I’ve seen is developers over-engineering new applications. While it can be partially caused by “ resume-driven design”, I feel it mostly comes from the assumption that the latest techniques seen online from large tech companies apply to everyone. Sure, the Googles and Facebooks of the world can be a great technical role model, but not every application has to be built like Netflix. I would even argue that a “next Netflix” shouldn’t be built initially like Netflix.

A city of 1,000 wouldn’t start with building airports, trains and highways even if it will become a city of 1 million. The same applies to software. Sure, consider the future of maximum scale, but new requirements and technologies will foil even the best plans. You just need to leave a way to evolve to the next stage.

A design pattern I see commonly abused in new projects is picking microservices over modular monolith. It’s hard to imagine a new application that should start its life as microservices. Even with a skilled team, the pressure to deliver the minimum viable product is strong enough such that there is a high risk of getting the worst of both worlds: a distributed monolith, or at best an over-designed and complex to debug system requiring dedicated DevOps engineers before you need them. In the beginning you should focus more on business requirements and value. I’ve seen this push to over-engineer many times – once, I was presented with a proposal to create a Kubernetes cluster for an application that otherwise ran with very little utilization on a single server with 1 vCPU and 4 GB memory.

Some rules of thumb that I have:

You don’t need to use full AI models to get some of the benefits that have come out of the Gen AI / large language model (LLM) wave, especially when it comes to search.

One of the interesting components that have come out is practical uses for embeddings, which represents a concept as a high-dimensional vector (series of decimal numbers). While the idea of embeddings predate LLMs (such as Word2vec in 2013), the embedding models we have access to now like OpenAI’s text-embedding-3-small have been enhanced to understand context, for example the word “ruler” can refer to a measuring device or to a king. The term used here is “attention” in the sense of paying attention to the rest of the context. Humans can easily differentiate the two different meanings of “ruler” in the phrases “I want to measure this wood, please give me the ruler” and “King George was a ruler of England”.

Every year since the dawn of time someone has declared “it will be the year of the Linux desktop!” I don’t know if that will be true in 2025 for everyone, but it is for me. I’m now using Kubuntu exclusively on my home desktop after initially leaving Windows on as a dual boot. Once I realized I went 4 months without even starting Windows once, I knew it was safe to drop it.

Now, I have been exposed to Linux for a long time. I first set up a system in 1999 with Mandrake Linux (most recently called Mandriva), and loved it. I set up a server for development at work with Ubuntu Server in 2006 and since have used Linux servers at work almost exclusively. For a few years in the late 2000s I had an Ubuntu Desktop as my work machine.

In this post I want to share why I switched now and some “risk free” things you can explore if you are interested in switching away from Windows on your PC.

Welcome to the newly redesigned gillius.org, built with Jekyll and CSS by hand, based on some CSS resets I found online.

This has been a long time coming – I last moved to Movable Type Open Source edition in 2011 and the last major CSS design I did in 2006. I’ve been working on this for some time and there’s still more updates I’d like to make, but it’s far past time to cutover at this point.

Some of my very old content has been removed such as C++ and Allegro tutorials and articles from the late 90s and early 2000s which is very out-of-date.

Keep reading for more details and my reasons in picking Jekyll.