Architecture Weekly #163
Architecture Weekly Issue #163. Articles, books, and playlists on architecture and related topics. Split by sections, highlighted with complexity: 🤟 means hardcore, 👷♂️ is technically applicable right away, 🍼 - is an introduction to the topic or an overview. Now in telegram and Substack as well.
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Highlights
Accelerating Large-Scale Test Migration with LLMs 👷♂️
This post describes how Airbnb harnessed large language models to supercharge test migrations. It covers their engineering challenges and why speed and reliability matter when your codebase grows so big. They also share practical tips for seamlessly integrating LLMs into routine developer workflows. The story shines a light on scaling test coverage without driving folks insane. If you’re wrestling with legacy test suites, you’ll find some refreshing insights here.
Vertical Sharding Sucks 👷♂️
Vertical sharding can significantly complicates queries and can choke performance. The article walks through the typical reasons teams consider splitting databases in the first place. You’ll learn about unexpected complexity, how it impacts schemas, and alternatives to avoid painting yourself into a corner. It’s a call to be cautious before slicing your data in ways that might backfire. If you’ve ever dreamed of scaling by “just splitting tables,” this is a must-read.
#db
Top 5 ways to use AI in Software Architecture 🍼
Software Architects need to make decisions, review existing ones, document software systems and prototype new systems. AI can be powerful assistants. Here's the top 5 ways how to use LLMs to advance your software architecture
#ai #architecture
Follow-Up
Postgres Scaling Roadmap 👷♂️
Here, you’ll find a handy overview of how Postgres can scale for large workloads. The author explores common strategies, from partitioning and sharding to replication and connection pooling. What sets this piece apart is its future-forward perspective on upcoming Postgres features. If you’re planning to run Postgres at scale, you can glean valuable pointers from real-world scenarios. Expect a balanced mix of technical insights and forward-thinking advice.
#db
ElasticSketch: Bringing Resource Elasticity to Network Measurement 🤟
This is a deep dive into data-plane resource management through a technique called ElasticSketch. The paper focuses on elastic resource allocation for network measurement, which helps capture traffic patterns without massive overhead. They detail the algorithm’s design, plus real-world performance results. If you’re into systems engineering or network metrics, you’ll discover a clever approach to handle dynamic workloads. Even if network measurement isn’t your core job, the resource elasticity concepts are worth understanding.
#paper
Cost-Efficient Event Ingestion into Iceberg S3 Tables on AWS 🤟
How to handle a stream of events in a budget-friendly manner using Apache Iceberg and Amazon S3? You’ll see how the author tuned their setup to reduce storage bills and streamline data ingestion. There’s a nice step-by-step breakdown of architectural choices, plus some trade-off explanations. It’s full of practical tips for those running big data pipelines. Recommended if you’re all about optimizing AWS costs.
#s3 #aws
Introducing Architecture as Code in SonarQube 🍼
SonarSource unveils a new feature that transforms the way teams define, visualize, and validate software architecture. It’s a sweet combination of code-based rules and automated checks to ensure your design stays on track. By embedding architectural policies right in your CI pipeline, you can catch design drift early. The article gives a glimpse into how it fits neatly with SonarQube’s usual static analysis. If you’re a fan of “as code” solutions, this is worth a read.
#aaac
Optimizing Postgres Table Layout for Maximum Efficiency 🤟
In this guide, the author walks through different ways to tune Postgres tables, from choosing column order to leveraging fill factors. They highlight how small changes can yield big boosts in performance. There’s also some discussion on how indexing strategies and data types play a role. It’s the kind of hands-on advice that can help you squeeze out more speed without crazy hacks. Useful if you crave performance gains for your Postgres setup.
Understanding JSON Schema Compatibility 👷♂️
This blog post delves into the nuances of JSON schema evolution and keeping your data consistent. The author covers schema validation rules, versioning concerns, and the difference between backward and forward compatibility. It’s presented in a straightforward manner to help you avoid typical schema pitfalls that can break your API. If you juggle JSON data structures in microservices or event-driven systems, this piece will clear up some confusion. Overall, it’s a neat primer on a common challenge for modern data exchange.
#protocols
Big thanks to Nikita, Constantin, Anatoly, Oleksandr, Dima, Pavel B, Pavel, Robert, Roman, Iyri, Andrey, Lidia, Vladimir, August, Roman, Egor, Roman, Evgeniy, Nadia, Daria, Dzmitry, Mikhail, Nikita, Dmytro, Denis and Mikhail for supporting the newsletter on Patreon!