A midnight bandwidth alarm triggers a deep dive into message compression, revealing how algorithm choice, compression placement, batch handling, and selective policies evolve from simple no‑compression setups to multi‑layered strategies that balance CPU, bandwidth, disk costs, and latency in million‑QPS systems.
Kafka’s I/O efficiency stems from sequential data writes, zero‑copy reads using sendfile, batch compression of messages, and a dual‑threaded batch producer that together minimize random disk access and data copying, dramatically speeding up both reading and writing operations.
This article explains how Kafka achieves ultra‑high throughput by using sequential disk writes, memory‑mapped files, zero‑copy sendfile reads, and batch compression, while also describing its data retention policies and the trade‑offs of synchronous versus asynchronous producer modes.
This article explains how Kafka attains high throughput by using sequential disk writes, memory‑mapped files, sendfile zero‑copy, and batch compression, detailing both write and read path optimizations and their impact on performance.
