This guide explains how to maximize Nginx concurrency by configuring four key parameters—worker_processes, worker_connections, keepalive_timeout, and keepalive_requests—plus three I/O optimizations (sendfile, tcp_nopush, tcp_nodelay), and discusses related OS tuning for real‑world performance in production.
This comprehensive guide walks you through Linux disk expansion, LVM provisioning, filesystem resizing, I/O scheduler tuning, performance benchmarking with fio, monitoring with Prometheus, security hardening, troubleshooting common disk issues, and best‑practice recommendations for reliable production environments.
This article explains the Linux Page Cache mechanism, covering its core concepts, read/write workflows, data consistency, optimization strategies, real-world use cases, advanced topics, common misconceptions, and practical tips for improving system performance and resource management.
Zero‑copy eliminates CPU‑mediated data copies between user and kernel spaces by using DMA and memory‑mapping, dramatically improving I/O performance, reducing context switches, and enabling high‑throughput applications such as file servers, Kafka brokers, and Java networking frameworks.
At the 19th China Linux Kernel Developer Conference, vivo unveiled nine open‑source innovations—including DMA‑BUF DIO for AI model loading, ZHP memory compression, EROFS and f2fs filesystem enhancements, and I/O optimizations—contributing significant performance gains and demonstrating its commitment to Linux kernel advancement worldwide.
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native platforms, revealing how cloud storage cost models affect performance optimization, and presents case‑study findings from Uber’s Presto production environment that highlight fragmented I/O patterns and the financial impact of storage API calls.
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native environments, revealing how cloud storage cost models affect I/O optimization, and presents Uber Presto case‑study findings that highlight fragmented access patterns and financial implications of storage API calls.
This whitepaper investigates the migration of data‑intensive analytics to cloud‑native environments, using Uber’s Presto workload to expose how cloud storage cost models and fragmented I/O patterns affect performance, and proposes optimized I/O strategies to improve cost‑effectiveness and system design.
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native environments, analyzes the unique cost model of cloud storage, and presents case‑study findings from Uber’s Presto production system that reveal fragmented I/O patterns and propose optimization strategies to improve cost‑performance in the cloud.
This whitepaper analyzes the shift of data‑intensive analytics to cloud‑native platforms, examines Uber Presto’s fragmented I/O patterns, reveals hidden storage‑API cost impacts, and proposes cloud‑aware I/O optimization strategies to improve performance‑cost efficiency.
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native environments, analyzing how cloud storage cost models affect performance optimization, and presents an Uber Presto case study that reveals fragmented I/O patterns and proposes cost‑effective optimization strategies.
This whitepaper examines the trend of moving data‑intensive analytics workloads to cloud‑native platforms, revealing how cloud storage cost models affect I/O optimization, and using Uber’s Presto production data to show that traditional I/O strategies overlook costly storage API calls, leading to high expenses.
This whitepaper examines the industry shift of moving data‑intensive analytics workloads to cloud‑native platforms, revealing how cloud storage cost models affect I/O optimization and presenting Uber Presto case‑study findings that highlight fragmented access patterns and associated financial impacts.
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native platforms, revealing how cloud storage’s unique cost model demands finer‑grained I/O optimization, illustrated through an empirical case study of Uber’s Presto production environment and its fragmented access patterns.
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native platforms, analyzing how cloud storage cost models affect performance optimization, and presents a case study of Uber’s Presto production environment that reveals fragmented I/O patterns and the financial impact of storage API calls.
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native environments, analyzes how cloud storage cost models affect performance optimization, and presents Uber Presto case‑study findings that reveal fragmented access patterns and hidden financial costs of traditional I/O strategies.
This whitepaper examines the industry shift of moving data‑intensive analytics to cloud‑native platforms, revealing how cloud storage’s unique cost model demands nuanced I/O optimization, and presents Uber Presto case‑study findings that highlight fragmented access patterns and cost‑effective design strategies for high‑performance cloud workloads.
This whitepaper examines the industry trend of moving data‑intensive analytics to cloud‑native platforms, revealing how cloud storage cost models affect I/O performance and presenting case‑study findings from Uber's Presto deployment to guide efficient I/O design in the cloud.
This whitepaper examines the industry trend of migrating data‑intensive analytics workloads to cloud‑native environments, revealing how cloud storage’s unique cost model demands finer‑grained performance optimization, and presents Uber Presto case‑study findings that expose fragmented I/O patterns and associated financial impacts.
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native platforms, analyzes how cloud storage cost models affect performance optimization, and presents Uber Presto case‑study findings that reveal fragmented access patterns and new I/O strategies to improve cost‑effectiveness.
This whitepaper examines the industry shift of moving data‑intensive analytics to cloud‑native platforms, analyzes how cloud storage cost models affect performance optimization, and presents Uber Presto case‑study findings that reveal fragmented access patterns and the financial impact of traditional I/O strategies in the cloud.
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native platforms, analyzes the unique cost model of cloud storage, and presents case‑study findings from Uber's Presto production environment to guide efficient I/O design and cost‑effective performance optimization.
This whitepaper examines the industry trend of moving data‑intensive analytics workloads to cloud‑native platforms, revealing how cloud storage cost models affect performance optimization and presenting case‑study‑based I/O strategies derived from Uber's Presto production environment.
This whitepaper examines the industry trend of moving data‑intensive analytics applications to cloud‑native environments, revealing how cloud storage cost models affect performance optimization, and presents case‑study findings from Uber’s Presto production workload that highlight fragmented I/O patterns and the financial impact of storage API calls.
This comprehensive guide covers high‑performance development techniques—including I/O optimization, zero‑copy, multiplexing, concurrency, thread‑pool design, lock‑free programming, inter‑process communication, RPC, serialization, database indexing, caching strategies, Bloom filters, full‑text search, and load balancing—to help developers build fast, scalable, and reliable systems.
This article examines the resource and efficiency challenges of scaling large language model training, explains data, model, pipeline, and tensor parallelism, and provides practical I/O, communication, and stability optimization techniques—including high‑availability storage, RDMA networking, NCCL tuning, and fault‑tolerant recovery—to improve throughput and reliability.
The article outlines the resource and efficiency challenges of scaling large language models, explains data and model parallelism strategies, and details practical I/O, communication, and stability optimizations—including high‑availability storage, RDMA networking, and fault‑tolerance measures—to improve training throughput and reliability.
This article explains Direct Memory Access (DMA), compares it with traditional I/O, details the complete DMA‑based I/O workflow, and explores zero‑copy techniques such as mmap, sendfile, and asynchronous I/O, highlighting their impact on context switches, data copies, and overall transfer performance.
The article explains why relying solely on SQL optimization often fails to resolve deep database performance problems, illustrating with real Oracle and domestic PostgreSQL‑compatible cases where I/O configuration, shared buffers, and system‑level metrics were essential to achieve stability.
The article explains how traditional I/O incurs four data copies and context switches, and how DMA, zero‑copy techniques such as sendfile, mmap, splice, and Direct I/O reduce copying and system calls, improving performance for disk‑to‑network data transfers in Linux.
This article explains how DMA offloads memory‑to‑device data transfers, reduces the four data copies and context switches of traditional I/O, and introduces zero‑copy methods such as sendfile, mmap and Direct I/O, illustrating their mechanisms, advantages, limitations, and real‑world use cases like Kafka and MySQL.
This article explains how DMA offloads memory‑to‑device data transfers, reduces the four data copies and context switches of traditional I/O, and introduces zero‑copy mechanisms such as sendfile, mmap and Direct I/O, with practical examples like Kafka and MySQL.
This article explains how DMA and zero‑copy techniques reduce the four memory copies and context switches typical of Linux I/O, detailing their mechanisms, implementations such as sendfile, mmap and Direct I/O, and real‑world usage in Kafka and MySQL to boost performance.
Zero‑copy I/O minimizes data copying by using DMA and kernel mechanisms such as mmap + write or the sendfile() system call, with sendfile achieving true zero‑copy through fewer system calls, context switches, and memory copies compared to traditional read/write paths.
This article explains the characteristics of OLTP and OLAP workloads, compares their I/O patterns, and provides Oracle database performance best‑practice guidelines, including storage planning, SAN architecture, operating‑system queue‑depth settings, and SwingBench testing results for optimal configuration.
This guide walks developers through a step‑by‑step progression of performance‑boosting techniques—including zero‑copy I/O, epoll, thread pools, lock‑free programming, IPC, RPC, database indexing, caching, Bloom filters, full‑text search, and load balancing—to help build faster, more scalable backend services.
This article explains why traditional read/write file serving incurs multiple data copies and context switches, then introduces mmap, sendfile, and splice as zero‑copy methods that reduce CPU load, discuss their usage, limitations, and practical pitfalls for high‑performance Linux servers.
This article explains the motivation behind B‑trees, their definition, node constraints, height analysis, and compares B‑trees with B+‑trees, highlighting how multi‑level storage and batch disk I/O reduce lookup costs in large‑scale data systems.
Zero‑copy techniques in Linux eliminate unnecessary data copying between user and kernel space, dramatically reducing CPU overhead and boosting throughput; this article explains why zero‑copy is needed, outlines several methods—including user‑space I/O, mmap, sendfile, DMA‑assisted sendfile, splice, copy‑on‑write, buffer sharing, and netmap—plus their trade‑offs.
This article explains how to tune Nginx for high‑concurrency environments by adjusting OS limits, worker processes, worker connections, CPU priority, keep‑alive settings, TCP options, logging, caching, and I/O strategies to achieve optimal performance without relying on magical configurations.
This article explains practical techniques for reducing Linux file fragmentation and dramatically improving I/O throughput—ranging from kernel cache tuning and minimum allocation tweaks to asynchronous I/O, read‑ahead, delayed allocation, and even building a custom filesystem that can deliver three to five times faster disk performance.
