This article explains the performance bottlenecks of traditional I/O, introduces zero‑copy concepts and their relationship with DMA and PageCache, details RDMA architectures, and demonstrates practical zero‑copy implementations such as mmap+write, sendfile, splice, tee, and Java NIO APIs.
This article explains how Kafka achieves extremely high throughput by using a Reactor‑based non‑blocking I/O model, zero‑copy data transfer, sequential disk writes, memory‑mapped files, sparse indexing, partition load‑balancing, compression, batch processing, and a lock‑free offset design.
This article explains how RocketMQ’s CommitLog architecture—sequential writes, mmap zero‑copy, PageCache acceleration, fixed‑size log files, flexible flushing strategies, and efficient ConsumeQueue indexing—enables the system to sustain million‑level QPS with high reliability and low latency.
PageCache, a kernel-managed memory cache, bridges the speed gap between slow storage and fast RAM, enabling rapid subsequent file accesses, accelerating copy‑paste operations, and optimizing read/write performance; this article explains its principles, mechanisms, and demonstrates its impact with Linux experiments.
PageCache is an operating‑system mechanism that uses physical memory to cache disk blocks, dynamically resizing with available RAM and employing LRU replacement and read‑ahead, turning slow storage accesses into fast memory reads, yielding up to twenty‑fold speed gains for tasks such as compilation, video editing, and database operations.
PageCache, a kernel-managed memory cache that stores disk data in RAM, dramatically speeds up file operations by turning repeated reads and writes into pure memory accesses, and its dynamic sizing, read‑ahead, and LRU eviction are demonstrated through Linux experiments with large files.
This article explains why Kubernetes containers often show near‑full memory usage by exploring Linux process address spaces, the distinction between RSS and page cache, how cgroup statistics are collected, and provides step‑by‑step C and Go examples to reproduce and diagnose the behavior.
This article explains the key techniques behind Kafka's high‑throughput performance, including batch sending, message compression, sequential disk reads/writes, efficient use of PageCache, zero‑copy transfers, and memory‑mapped index files, with code examples illustrating each mechanism.
This article explains how storage media performance, kernel‑user mode transitions, DMA, zero‑copy, and PageCache interact to affect I/O latency, why multiple data copies and context switches hurt throughput, and how techniques like mmap, sendfile, and asynchronous I/O can reduce overhead.
This article explains the performance hierarchy of storage media, the distinction between kernel and user modes, how data moves between them, and why excessive context switches hurt I/O, then introduces DMA, zero‑copy, PageCache, mmap + write, sendfile, and tuning tips to dramatically improve Linux I/O efficiency.
This article explains Linux memory metrics VSZ, RSS, and PSS, illustrates them with a roommate analogy, details PageCache and its role in I/O, describes swap behavior and the swappiness setting, and introduces zero‑copy techniques to reduce data copying and context switches.
Traditional Linux I/O using read() and write() involves multiple CPU and DMA copies and context switches, while modern techniques like zero‑copy, multiplexing, and page‑cache optimization reduce overhead; this article explains the classic I/O flow, read/write operations, network and disk I/O, and high‑performance strategies such as zero‑copy and Direct I/O.
This article explains the classic Linux read/write system‑call I/O path, the copy and context‑switch overhead involved, and presents high‑performance techniques such as zero‑copy, multiplexing, and PageCache optimizations to reduce latency and improve throughput.
This article explains how Linux traditional read/write system calls work, detailing the CPU, DMA copies and context switches involved, and then explores high‑performance I/O techniques such as zero‑copy, multiplexing and PageCache, together with the Linux I/O stack and buffering layers.
The article explains Linux’s traditional system‑call I/O path, detailing how read() and write() trigger multiple CPU and DMA copies and context switches, describes read and write workflows, explores network and disk I/O, examines the Linux I/O stack, page cache, buffering strategies, zero‑copy, mmap and Direct I/O, and discusses performance trade‑offs.
This article explains the classic Linux read/write system‑call I/O path, its multiple CPU, DMA copies and context switches, and then explores high‑performance optimizations such as zero‑copy, multiplexing, and PageCache, while comparing Buffered I/O, mmap, and Direct I/O.
This article explains the architecture of Hadoop HDFS, identifies performance bottlenecks in page cache and metadata handling on DataNodes, and presents four practical optimization techniques—including cache‑buffer separation, barrier disabling, directory restructuring, and real‑time monitoring—demonstrating significant throughput and latency improvements in large‑scale clusters.
