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The article explains how Kafka attains ultra‑high write throughput by leveraging disk sequential writes, zero‑copy data transfer, operating‑system page cache, and memory‑mapped files, detailing each technique’s impact on latency, CPU usage, and overall performance.
This article explores Apache Kafka’s core storage architecture, explaining how sequential append‑only writes, sparse indexing, segmented log files, and a leader‑based replication mechanism together enable high‑throughput, reliable, and scalable event streaming for massive data workloads.
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 high throughput by using sequential disk writes, memory‑mapped files, batch compression, and zero‑copy sendfile for reads, while also covering data retention policies and the role of offsets in consumer processing.