Production on NoRaincheck

Setting SQL for Feature Transformations as a Standard

Sat, 01 Mar 2025 00:00:00 +0000

Setting SQL for Feature Transformations as a Standard

March 2025

So Spark’s SQLTransformer is probably the first (and only?) documented, formal, specification for doing SQL transformations specifically for machine learning preprocessing. I think that is interesting for a variety of reasons, with the biggest one being opportunities for standardisation, in particular for real-time flows. Having a well-optimised SQL transformation engine could be immensely valuation. The reason why this hasn’t occured is probably because industry standard today still relies on Python as the execution engine, however this because untenable in scenarios where Python is an inappropriate production programming language choice. At the same time, Spark is typically too expensive of a dependency to justify low latency workflows. Nevertheless lets quickly look at the specification and considerations for using the language with an alternative computation backend.

rqlite - a Production Experiment

Wed, 01 Jan 2025 00:00:00 +0000

rqlite - a Production Experiment

January 2025

rqlite is a distributed version of sqlite using the raft consensus algorithm. The great thing about rqlite actually has nothing to do with the underlying tech, but more to do with broad developer experience and that is the defaults with the provided helm charts.

It just feels like I’m the target audience - someone who wants to quickly push rqlite to production with the minimal dependencies and gives me enough to shoot myself in the foot. Compared with postgres helm charts, rqlite presumes that you may want to just use it as-is, without even a values file. That is a welcome change, whereas almost anything from the bitnami one expects you as a developer will make modifications. Something in that model does not feel quite right. This friction (although seemingly trivial) converted me to use rqlite (the other reason is I was using sqlite for my tests which meant I didn’t need to worry about changing any code to ensure compatibility between different sql dialects).

LLMs - in Review (2024)

Sun, 01 Dec 2024 00:00:00 +0000

LLMs - in Review (2024)

December 2024

2024 was the first year where I took LLMs seriously. I successfully hosted a Llama 70b parameter model in production which was used as with continue.dev for a self-hosted co-pilot replacement, along with a code autocomplete like Qwen Coder or Deepseek, these were fine replacements and surprisingly robust. Huggingface’s TGI along with Triton Server were the main heroes for this project, (Triton was used to serve onnx models for embeddings) though I’ve yet to find a “good” embedding model. At this stage in time, most of the vector database solutions “feel” the same and can all seemingly be trivially hosted via Kubernetes.

Python & TypeScript - in Review (2024)

Sun, 01 Dec 2024 00:00:00 +0000

Python & TypeScript - in Review (2024)

December 2024

One thing that I like to stress is the importance of tooling and relying on defaults. By being able to speak consistently within ones own projects or using commonly seen patterns reduces the mental overhead. These could be folder structures or idioms, especially things which permeate across different programming languages or frameworks.

Here are some of my thoughts on Python and TypeScript; coming from someone who is predominantly a Python developer and does minimal front-end work.