JD Cloud Developers (Developer of JD Technology) is a JD Technology Group platform offering technical sharing and communication for AI, cloud computing, IoT and related developers. It publishes JD product technical information, industry content, and tech event news. Embrace technology and partner with developers to envision the future.
This article explains how unreliable networks, clock instability, and node failures can cause data inconsistency in distributed clusters, introduces the Raft consensus algorithm, details its roles, election process, log replication, read/write handling, consistency models, and mechanisms to avoid split-brain and livelock.
The article details DongSQL’s kernel redesign—including RETURNING clause, Hint syntax, CCL concurrency control, statement outlines, and thread‑pool optimizations—showing concrete performance gains in high‑concurrency e‑commerce scenarios.
The 2025 Open Atom Developer Conference highlighted JD's JoyAgent project, an open‑source, 100% enterprise‑grade multi‑agent platform that excels in AI, data governance, and diagnostic analysis, with detailed features, performance metrics, and deployment experiences shared.
This article explains how different chunking methods—fixed size, semantic, recursive, document‑based, agent‑driven, sentence‑level, and paragraph‑level—affect Retrieval‑Augmented Generation, offering practical guidelines, metrics, and optimization tips for real‑world deployments.
This article explains the concept of heterogeneous treatment effects (HTE), clarifies related terminology, outlines why HTE analysis matters for product decisions, and walks through dimension selection, statistical and machine‑learning methods—including ANOVA, causal trees, meta‑learners, and double‑machine‑learning—plus a practical MVP tool with code examples and future development directions.
This article explains how the Easy Code Reader MCP server integrates with large language models to automatically locate, read, and analyze Java source code across multiple micro‑services and JAR files, improving code‑agent accuracy and developer productivity through a set of practical tools and step‑by‑step examples.
This document outlines the background, design, architecture, workflow, and large‑model integration of an AI‑driven Experiment Analysis Agent, detailing how it consolidates data, automates analysis via modular pipelines, leverages DeepSeek models, and enhances user experience through unified front‑end forms and intelligent messaging.
The article explains how JD.com's AI try‑on system Oxygen Tryon uses advanced computer‑vision models to let shoppers instantly preview garments on their own photos, dramatically improving fit perception, reducing return rates, and outlining future technical and business expansions.
JoyCode Agent leverages a multi‑agent, patch‑and‑test co‑generation framework with iterative validation, failure attribution, and experience‑driven retries to achieve a 74.6% Pass@1 rate on the SWE‑Bench Verified benchmark, dramatically reducing computational resources while delivering high‑quality code patches.
During a Double‑11 promotion, an oversized Redis cache key caused a cascade of failures—cache miss, network bandwidth saturation, and a full‑blown cache avalanche—prompting the team to implement big‑key mitigation, compression, lock‑based cache back‑source, and monitoring measures to safeguard future deployments.
