This article walks through a real‑world case where a batch‑score query API that originally took up to 20 seconds was systematically optimized through index tuning, multithreaded execution, and request‑size limiting, ultimately achieving sub‑second response times.
This article presents a practical guide for SpringBoot projects, detailing twelve concrete strategies—including batch processing, asynchronous execution, caching, pagination, and transaction handling—to dramatically reduce API latency and improve overall backend efficiency.
The article explains why JSON can become a performance bottleneck in high‑throughput APIs and compares four binary serialization formats—Protocol Buffers, FlatBuffers, MessagePack, and CBOR—showing benchmark results, trade‑offs, and a practical migration checklist.
This article aggregates multiple independent evaluations of DeepSeek‑R1 across major cloud providers, comparing accuracy on AIME math problems, token‑per‑second throughput, first‑token latency, stability under high concurrency, and overall service reliability, ultimately highlighting Volcano Engine as the top performer.
This article walks through diagnosing slow API responses, using Pfinder for tracing, refactoring Java collection handling with Sets, and applying JSF asynchronous calls to dramatically cut latency, offering a step‑by‑step guide to backend performance optimization.
Facing tight schedules and diverse coding habits, many Chinese projects struggle with API latency; this guide walks through requirement analysis, acceptance standards, common pitfalls, and a comprehensive set of optimization tactics—from configuration and code tweaks to caching, async processing, and observability tools—to dramatically improve API performance.
This article introduces the open‑source system‑design‑101 repository, explaining why learning system design matters, and walks through essential topics such as API performance, the CAP theorem, caching strategies, microservice best practices, and secure password storage, with links to video courses and the project’s GitHub address.
Discover a comprehensive set of ten practical techniques—including batch processing, asynchronous execution, caching, pooling, parallelization, indexing, transaction management, and SQL tuning—to dramatically reduce API latency and improve efficiency in legacy backend systems, illustrated with real-world examples and code snippets.
This article outlines twelve practical techniques—including batch processing, asynchronous execution, caching, pre‑processing, pooling, parallelization, indexing, transaction sizing, code refactoring, pagination, SQL tuning, and lock granularity—to dramatically reduce API latency and improve overall backend efficiency.
This article outlines a comprehensive set of backend API performance optimization techniques—including batch processing, asynchronous execution, caching, pre‑processing, pooling, parallelization, indexing, transaction handling, SQL tuning, and lock granularity—to reduce latency and improve system efficiency.
This article presents a comprehensive guide to optimizing API latency by covering batch processing, asynchronous execution, caching, pre‑processing, pooling, parallelism, indexing, transaction management, code refactoring, pagination, SQL tuning, and lock granularity, each illustrated with practical code examples and diagrams.
The article enumerates common reasons why APIs become slow—ranging from database slow queries and complex business logic to poorly designed thread pools, locks, and machine issues—and offers concrete mitigation strategies such as query optimization, caching, asynchronous processing, and proper resource configuration.
A backend engineer shares a step‑by‑step case study of reducing a batch‑score query API from 20 seconds to under 500 ms by analyzing the problem, adding a composite index, introducing multithreaded CompletableFuture calls, and limiting batch size with pagination and thread‑pool tuning.
This article explains how to break down API response time, identify common latency causes, and use browser developer tools, the httpstat command‑line utility, and flame‑graph profiling (Async Profiler) to pinpoint and optimize slow backend performance.
This article describes how to implement a multi‑interface performance test for the collect and uncollect operations of a teacher’s resource list, using Groovy scripts, a custom FunTester framework, multithreaded request generation, request‑id tracking, and response recording to produce QPS and RT metrics.
This article introduces the Hey load‑testing tool, explains how to install and run it with specific QPS settings, analyzes the resulting metrics and charts, and offers practical tips for identifying bottlenecks such as network bandwidth or CPU usage during capacity planning.
