This article explains what synchronous and asynchronous Python code mean, how they are implemented in web servers, the performance factors of context switching and scalability, and the specific scenarios where async can outperform sync.
This article dissects ClickHouse's Distributed table write path, detailing how the Distributed engine creates tables, decides between synchronous and asynchronous insertion, manages local and remote shards, handles sharding keys, and ensures atomic data distribution across the cluster.
Exploring the fundamental differences between synchronous and asynchronous information propagation, the article examines how real-time collaborative editing—exemplified by tools like Google Docs—relies on operational transformation algorithms to maintain data consistency, highlighting historical milestones, technical challenges, and key research references.
