This article explains why deleting huge log files can crash a server, compares the low‑IO echo and truncate commands for safely emptying logs, provides practical examples, parameter tips, additional methods, and best‑practice recommendations for production environments.
This article explains why traditional I/O interfaces rely on data copying, demonstrates the hidden overhead of read/write in a network server, and introduces zero‑copy methods such as mmap, sendfile, DMA Gather Copy, and splice to dramatically reduce copies and context switches for faster I/O.
This article analyzes Kuaishou's development of a Java 17 transparent coroutine solution, detailing its architectural evolution, scheduling and I/O optimizations, performance gains in cloud‑native high‑load environments, and future directions for large‑scale production deployment.
Baidu’s Search Content Storage team built an HTAP table storage system and a serverless compute‑scheduling architecture that separates OLTP and OLAP workloads, delivering up to 200 GB/s peak IO, reducing storage cost by 75 %, and enabling SQL‑style task development with native FaaS functions.
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 a case study of Uber's Presto deployment that reveals fragmented access patterns and new I/O cost considerations.
The article analyzes IO‑intensive bottlenecks in mobile apps, explains how MMKV’s mmap‑based storage, rewrite, and expansion mechanisms cause main‑thread stalls, and presents concrete optimizations such as value comparison before write, pre‑expansion, compression, expiration handling, and proper instance management to dramatically reduce latency.
Facing severe IO-wait and read bottlenecks as product data grew from tens of millions to billions, this article analyzes root causes in Elasticsearch clusters and presents a comprehensive solution involving index parameter tuning, merge settings, translog async writes, query optimizations, and hardware upgrades to restore performance and stability.
This article presents practical techniques for optimizing high‑concurrency online services—such as avoiding relational databases, employing multi‑level caching, leveraging multithreading, implementing circuit‑breaker patterns, reducing I/O, managing retries, handling edge cases, and logging efficiently—to maintain sub‑300 ms response times under massive load.
This article outlines practical strategies for handling high‑QPS backend services, including avoiding relational databases, employing multi‑level caching, leveraging multithreading, implementing degradation and circuit‑breaker mechanisms, optimizing I/O, using cautious retries, handling edge cases, and logging efficiently.
This article outlines practical techniques for handling ultra‑high‑traffic backend services—including abandoning relational databases, employing multi‑level caching, leveraging multithreading, applying degradation and circuit‑breaker patterns, optimizing I/O, using cautious retries, guarding boundary cases, and implementing efficient logging—to maintain sub‑300 ms response times at 200k+ QPS.
This article outlines practical techniques for handling online services with QPS exceeding 200,000, including avoiding relational databases, employing multi‑level caching, leveraging multithreading, implementing degradation and circuit‑breaker patterns, optimizing I/O, using controlled retries, handling edge cases, and logging efficiently.
This article outlines practical techniques for handling ultra‑high‑traffic online services—such as avoiding relational databases, employing multi‑level caches, leveraging multithreading, applying degradation and circuit‑breaker patterns, optimizing I/O, using retries wisely, handling edge cases, and logging efficiently—to keep response times under 300 ms.
This article details 360's Threat Hunting platform built on Flink, covering its evolution, architecture, block‑index design, Hilbert‑curve data ordering, like‑pushdown, join optimizations, Alluxio caching, and future plans for BI and multi‑user concurrency, all aimed at efficient PB‑scale data querying.
Through a series of experiments using MySQL 8.0, this article demonstrates how group commit reduces I/O operations by consolidating multiple transactions into a single commit group, showing that doubling load increases runtime modestly while transaction count and commit groups rise significantly, highlighting performance benefits.
The article explains Linux page cache fundamentals, shows how to inspect and reclaim cache, and provides detailed tuning of vm.dirty_* and vm.swappiness parameters to smooth Kafka write traffic, reduce I/O spikes, and improve overall performance, illustrated with before‑and‑after benchmarks.
This article details how a high‑traffic MongoDB sharded cluster exceeding one million TPS was optimized through software‑level tweaks, configuration changes, WiredTiger storage‑engine tuning, and hardware upgrades, resulting in latency reductions from hundreds of milliseconds to a few milliseconds and significantly improved stability.
This article analyzes RocksDB's compaction rate‑limiting source code and presents practical tuning methods—both fixed and auto‑tuned—to mitigate IO spikes in the distributed KV store WTable, improving real‑time read/write latency and stability.
This article explains how to evaluate storage performance by analyzing host ports, storage systems, and backend disks, discusses IO aggregation penalties, business workload models, RAID and cache impacts, and provides practical guidance for accurate performance assessment and tuning.
This article illustrates the structure of a B+ tree, explains how leaf and non‑leaf nodes store data and pointers, and walks through a concrete search for the key 29, highlighting the three disk I/O operations required to locate the value.
The article describes how Qunar's group‑buying backend faced IO bottlenecks during large‑scale image conversion and solved the issue by moving intermediate files to a memory‑based tmpfs filesystem (/dev/shm), achieving a dramatic load reduction without code changes or server restarts.
