Architecture Weekly Issue #162. 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.
System Design Course Cohort #5 is open!
Typical system design courses teach technical skills but often overlook the connection to business problems. This course fills that gap, emphasizing the importance of recognizing and addressing business priorities with technical approaches. Learn to go beyond load balancing options and performance tactics by focusing on solving real business challenges. Course is completed by 50+ engineers with great feedback!
SIGN UP HERE: ONLY 10 slots left!
Highlights
Technology Radar 👷‍♂️
New Technology radar is out and guess what? AI Assitant(i.e. Cursor) made it to Trial in Tools! Other highlights include Observability evolution and Data as product. Check out the full radar!
#radar
Scaling to Millions: NGINX's Concurrent Connection Handling 🤟
Nginx powers half of the internet and serves billions of requests. It makes sense to understand the internal prowess of this web-server, load balancer and API Gateway. This post digs into the internals of the piece-of-the-art software to show how it provides such amazing performance.
javinpaul
#performance
What I Learned Operating ClickHouse 👷‍♂️
This post shares everyday lessons learned from running ClickHouse at scale, with real-life tips on hardware choices, cluster monitoring, and performance tuning. It also shows how to tackle headaches like node outages, data replication, and query optimizations in a hands-on, practical way.
#clickhouse
Business Driven Technical Decision
Me and Anton Sidelnikov are running a Business Driven Technical Decision Workshop next Monday. Find out how best engineering teams make technical decisions in the near real-life example!
Details:
• Duration: 2 hours
• Delivery: Online
• Cost: €79 per participant
• Date: April 14, 4 PM UTC(6 PM Tallinn time)
📌 Reserve your team’s spot today and bridge the gap between technology and business strategy! Sign up now!
Follow-Up
What I Wish Someone Told Me About Postgres 👷‍♂️
This post shares valuable insights from years of experience with Postgres, highlighting key areas that can trip up newbies. The author emphasizes the importance of normalization, understanding NULL values, and leveraging Postgres' documentation—it's a hefty read, but totally worth it for smoother database management!"
#db #postgresql
Unraveling a Postgres Segfault 🤟
Compiler bugs are extremely rare, but Datadog managed to find one. This post tells the story of a puzzling segfault that popped up in Postgres and how the engineering team dug in to solve it. It’s an engaging breakdown of memory troubleshooting, complete with practical tips and code samples to help you prevent similar nightmares.
Anthonin Bonnefoy
New Hash Table 👷‍♂️
Valkey(a Redis fork) shares a modern approach to building hash tables, focusing on performance and memory optimization. It’s a quick read that shows how a reworked design can boost efficiency and reduce collisions in everyday use.
Viktor Söderqvist
#redis #inmemorycache
Advanced Techniques for Big Data Queries 🤟
This paper from the Proceedings of the VLDB Endowment explores novel ways to handle large-scale data operations, highlighting advanced indexing and distributed query optimization. It explores issues like concurrency, reliability, and system design, and backs up its ideas with real-world benchmarks. If you’re curious about scaling big data analytics while keeping performance high, it’s worth a look.
Behind HubSpot AI: How the Prediction Engine Scores Millions of CRM Objects Daily 👷‍♂️
HubSpot is a popular CRM. Part of the CRM job is to facilitate sales - which is why HubSpot add AI for this particular purpose. AI pipeline uses automated data collection, feature engineering, and a feedback loop to fine-tune daily predictions at scale. The key takeaways include the importance of fresh, high-quality data, the need for robust model retraining processes, and building efficient systems that remain flexible for rapid iteration
#casestudy #ai
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! If you like the newsletter, feel free to support it there - with one-time support for example!