0 views collected around this technical thread.
This article details a systematic performance test of OceanBase's online DDL capabilities, measuring TPS and QPS impacts across various DDL operations, describing the test environment, scripts, results, and conclusions for reliable database schema changes.
This article explains what QPS (Queries Per Second) is, presents formulas and examples for calculating overall and per‑interface QPS, and shows how to use JMeter to create a test plan, add thread groups and listeners for accurate performance measurement.
This article explains the meanings, calculations, and differences of key high‑performance metrics such as system throughput, QPS, TPS, response time, and concurrency, illustrating each concept with real‑world examples and visual diagrams to help engineers evaluate and optimize large‑scale systems.
This article presents a Go‑based dynamic QPS load‑testing framework that leverages a custom goroutine pool, explains the rationale behind reusing goroutines, provides core functions for task execution and console command handling, and includes a complete runnable demo illustrating high‑throughput performance testing.
This article explains the definitions and differences of QPS and TPS, describes concurrency and throughput concepts, introduces related metrics such as PV, UV, DAU, MAU, and outlines key considerations for system performance testing and capacity planning.
This article explains how Java architects should select appropriate MySQL database machine configurations, collaborate with DBAs for deployment, and conduct essential performance and load testing, covering QPS vs TPS, IOPS, throughput, latency, and other critical metrics for production environments.
This article describes how to efficiently test billions of API parameter combinations by replacing naive nested loops with a queue‑based concurrent approach, dynamically controlling QPS, and addressing memory‑pressure issues using thread‑safe data structures.
The article explains the core performance metrics QPS, TPS, response time (RT), concurrency, and throughput, provides formulas to relate them, demonstrates practical calculations for capacity planning, and discusses how to determine the optimal number of threads for a backend system.
This article explains the key performance metrics of backend systems—including QPS, TPS, response time, concurrency, and throughput—provides their definitions, relationships, practical calculation examples, and guidance on optimal thread counts for achieving balanced performance.
This article explains how to combine dynamic and static performance testing models in FunTester by adding asynchronous console commands to control thread‑pool scaling and QPS adjustments, providing step settings, incremental changes, and graceful termination through simple key inputs.
This guide walks through creating a SpringBoot service, adding Sentinel dependencies, writing a high‑traffic test endpoint, explaining Sentinel’s entry/exit logic, and configuring the Sentinel dashboard to enforce a single‑node QPS limit of 20, complete with code samples and screenshots.
When building a QPS performance testing model with Disruptor, the author discovered that using an excessive number of consumer threads can cause the shutdown method to fail, leaving threads alive, inflating CPU usage, and destabilizing test results.
This article details a practical reproduction of a large‑scale WeChat red‑packet service, describing the design goals, hardware and software setup, concurrency architecture, monitoring tools, and performance results that demonstrate a single‑machine handling one million connections and up to sixty thousand queries per second.
This article details the design, goals, hardware and software choices, implementation techniques, performance testing phases, and analysis of a prototype backend capable of handling one million concurrent connections and up to 60 k QPS to simulate the massive load of ten billion WeChat red‑envelope requests.
This article explains how to design and evaluate system capacity by defining key metrics such as QPS, TPS and concurrency, describing when capacity assessment is needed, and outlining a step‑by‑step methodology—including traffic analysis, peak estimation, stress testing and redundancy planning—to ensure reliable performance under varying load conditions.
This article details the end‑to‑end preparation, execution, reporting, and retrospective of a large‑scale production‑environment load test for the Double 11 shopping festival, covering data preparation, QPS target calculation, multi‑scenario testing, issue analysis, and continuous improvement practices.
The article explains how to assess system capacity by analyzing daily traffic, calculating average and peak QPS, applying the 80/20 rule, conducting stress tests, and adjusting resources, illustrated with a sports event example and a book reservation system case study.
This article explains how to estimate and plan system capacity—covering concepts like QPS, concurrency, peak traffic calculation, load testing, and practical steps with examples—to ensure services can handle both regular and surge workloads effectively.
This article explains how to design and evaluate system capacity by using a sports event example, defining key metrics such as QPS, TPS and concurrency, applying the 80/20 rule, performing stress tests, and calculating required instances for reliable operation.
This article explains the definitions of QPS (queries per second) and TPS (transactions per second) in MySQL, compares the various calculation methods—including Queries‑per‑second, Questions‑per‑second, and GTID‑based approaches—and recommends the most reliable ways to measure database load and transaction throughput.