1 views collected around this technical thread.
During a Redis version upgrade of the internal R2M cache service, a Biz thread‑pool exhaustion error was traced to JSF threads blocked on a read lock held by a topology‑updater task, revealing a deadlock caused by shared ForkJoinPool usage and missing lock timeouts.
This article analyzes a thread‑pool exhaustion issue in JD's JSF framework during a Redis cache upgrade, detailing log evidence, stack‑trace analysis, lock contention caused by read/write locks and ForkJoinPool usage, and the steps taken to diagnose and resolve the problem.
This article details a step‑by‑step investigation of a JSF thread‑pool exhaustion error that occurred when upgrading the Redis version of JD's internal R2M distributed cache, analyzing stack traces, lock contention, ForkJoinPool behavior, and the eventual remediation steps.
This article details a comprehensive API performance optimization journey, covering current diagnostics with Pfinder and UMP, pinpointing bottlenecks such as excessive RPC loops and large data sets, and presenting solutions like Set-based deduplication, JSF asynchronous calls, and code refactoring that reduce latency from seconds to milliseconds.
This article documents the investigation of a JSF asynchronous call timeout that reduced interface availability, explains the async call mechanisms, analyzes the root cause in the callback thread pool, and presents short‑term and long‑term solutions to restore 100% availability.
The article analyzes the root causes and system‑wide consequences of oversized messages in JD Logistics, explains middleware limits of JMQ and JSF, and presents design principles, code‑level checks, and practical mitigation techniques such as pagination, claim‑check pattern, batch sizing, and monitoring to prevent service outages.
This article presents a detailed practice report on the JSF 1.7.6 preheat strategy applied to JD's VOP platform, describing the background, test scenarios, deployment process, monitoring results with and without preheat, and concluding that dynamic preheat significantly reduces performance spikes during service rollout.
This article explains the design and implementation of JD's JSF RPC framework, detailing the architecture, XML configuration of providers and consumers, Spring namespace handling, service registration, server startup, client initialization, load balancing, and the overall workflow from provider exposure to consumer invocation.
This report details the background, implementation, and results of using the JSF 1.7.6 dynamic preheat strategy to mitigate performance spikes during service deployments on JD's VOP platform, comparing scenarios with and without preheat and providing concrete monitoring data and configuration guidance.
This article introduces the EasyMock platform, outlines its key capabilities for service mocking, explains the technical challenges it solves, and provides a detailed step‑by‑step walkthrough of the JSF Mock implementation—including jar acquisition, JVM class loading, dynamic proxy creation, interface registration, client invocation, and parameter matching/return logic—accompanied by code examples.
This article introduces the EasyMock platform, explains its role in providing JSF and HTTP mock services for development and testing, outlines its architecture and capabilities, and details the step‑by‑step technical implementation of JSF Mock including jar downloading, JVM class loading, dynamic proxy creation, registration, client invocation, parameter matching, and response deserialization.
This article explains the background, design principles, architecture, serialization handling, asynchronous‑to‑synchronous conversion, trigger timing, ease of use, implementation details, and usage documentation of a lightweight, non‑intrusive JSF local debugging tool that leverages Redis publish/subscribe to enable seamless local integration testing for developers.
The article outlines the challenges faced by JD’s Java Service Framework (JSF) amid rapid microservice growth and containerization, and presents a comprehensive, multi‑layer redesign—including infrastructure, service framework, system extension, and application layers—to transform JSF into a cloud‑native microservice platform.