Weekend Reading #65

Weekend Reading: A weekly roundup of interesting Software Architecture and Programming articles from tech companies. Find fresh ideas and insights every weekend.

This week, we look at consistency models in distributed systems, Uber’s adaptive benchmarking framework, LinkedIn’s evolution of its Venice ingestion pipeline, and Meta’s new open-source platform for adaptive experimentation.

Consistency Models

👉 Great baseline if you work with distributed storage or microservices.

A simple guide to strong, eventual, causal, and session consistency. Shows how each model behaves, what guarantees it gives, and where it’s used in real systems.

Uber: Ceilometer – Adaptive Benchmarking Framework

👉 Useful if you’re building performance testing or reliability tooling.

Uber engineers present Ceilometer, a framework that benchmarks systems based on real-world load patterns. It adapts tests dynamically to surface bottlenecks earlier.

The evolution of the Venice ingestion pipeline

👉 A solid read if you’re interested in scalable ingestion or data lake architectures.

LinkedIn explains how they redesigned the ingestion pipeline for Venice, their large-scale derived dataset store. The new architecture improves throughput, consistency, and developer experience.

Meta: AX – Adaptive Experimentation Open Platform

👉 Recommended if your work involves A/B testing, hyperparameter tuning, or ML optimization.

Meta introduces AX, an open-source platform for adaptive experimentation and optimization. It helps teams tune models and parameters efficiently without hand-crafted experiments.