Issues » #538

#538 – September 04, 2023

Build Latency, Predictability, and Developer Productivity

On the surface, build latency is a purely technical problem. But humans experience and respond to it in interesting ways: forming expectations, making choices, and organizing work around build latency and similar factors.

Flatfile: Turn files into API-ready data with human-in-the-loop automation (sponsor)

With Flatfile, developers get full control to build fully customizable, powerful workflows that address any file-based import use case. For any file type from any source take advantage of AI data matching, faster validation & cleaning, and human-in-the-loop reviews, all while maintaining enterprise-grade scale and security. Start building today for free!

FIFO queues are all you need for cache eviction

In this blog, I will describe a Simple, Scalable eviction algorithm with three Static FIFO queues (S3-FIFO). Evaluated on 6594 cache traces from 14 companies, we show that S3-FIFO has lower miss ratios than 12 state-of-the-art algorithms designed in the past decades. Moreover, S3-FIFO’s efficiency is robust — it has the lowest mean miss ratio on 10 of the 14 datasets. The use of FIFO queues enables S3-FIFO to achieve good scalability with 6× higher throughput compared to optimized LRU in cachelib at 16 threads.

Database Architecture - A Deep Dive

This article is a deep dive into the internal architecture of databases/DBMS (Database Management Systems). I'll begin with a standard architecture relational databases have; will then take a peek into the architectures of a couple of real-world SQL databases.

The cost of convenience

It is tempting to build abstractions so developers have to do less and build more. However, this can easily end up causing frustrations with developers if not done right.

On The Acoustics of Cocktail Parties

Only parties of well-mannered guests will be considered.

Movement for Engineers

A brief introduction to keeping your body moving as someone who spends a lot of time sitting down.

Unsupervised graph anomaly detection - Catching new fraudulent behaviours

As fraudsters continue to evolve, it becomes more challenging to automatically detect new fraudulent behaviours. At Grab, we are committed to continuously improving our security measures and ensuring our users are protected from fraudsters. Find out how Grab’s Data Science team designed a machine learning model that has the ability to discover new fraud patterns without the need for label supervision.

Strategies and Tools for Performing Migrations on Platform

Needing to deliver faster and more reliably while managing a growing number of contributors and a more complex codebase seems like the fate of every hyper-growth tech company. For platform teams, the challenge is not any different. How can we quickly roll out and increase the adoption of new technologies safely with a growing codebase and organization?


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