Tagged articles

27 articles

Page 1 of 1

May 19, 2026 · Artificial Intelligence

Designing Next‑Gen Recommendation and Search with Agentic RAG Architecture

The article reviews cutting‑edge AI techniques for high‑concurrency, multimodal recommendation and search, detailing Alibaba Cloud's Agentic RAG evolution, Huawei Noah's LLM‑enhanced recommendation pipeline, and Baidu's generative ranking model GRAB, each with architecture diagrams, performance metrics, and real‑world deployment insights.

AI agentsAgentic RAGGenerative Ranking

0 likes · 6 min read

Designing Next‑Gen Recommendation and Search with Agentic RAG Architecture

Baidu Maps Tech Team

Apr 20, 2026 · Artificial Intelligence

How Baidu Maps Reinvents LBS Search with Multi‑Agent AI and RL

Facing the shift from keyword indexing to generative AI, Baidu Maps overhauled its LBS architecture by introducing a native multi‑agent system, context‑engineering (ACE) framework, and reinforcement‑learning alignment, enabling dynamic routing, knowledge evolution, and a 36% boost in planning compliance while maintaining zero‑tolerance for factual errors.

AI agentsContext EngineeringLLM

0 likes · 10 min read

How Baidu Maps Reinvents LBS Search with Multi‑Agent AI and RL

Alibaba Cloud Big Data AI Platform

Dec 29, 2025 · Cloud Native

How a Visual Platform Cut Search Costs by 60% with All‑in‑Elasticsearch

This case study details how a major internet visual platform consolidated its log, keyword, and vector search workloads onto Alibaba Cloud Elasticsearch, eliminating three separate pipelines, reducing write‑costs by 60%, cutting storage expenses over 60%, and achieving multi‑fold performance gains through serverless scaling, FalconSeek engine optimizations, and unified monitoring.

Cost OptimizationElasticsearchRAG

0 likes · 10 min read

How a Visual Platform Cut Search Costs by 60% with All‑in‑Elasticsearch

Baidu Tech Salon

Jun 17, 2025 · Operations

How Baidu Scaled Its Vertical Search: Elastic Scheduling and Data Management Secrets

This article explains how Baidu's vertical search platform tackled massive data growth and scaling challenges by redesigning its data management system, introducing elastic scheduling, decoupling ETCD access, implementing auto‑scaling, and advancing shard expansion to improve performance, stability, and cost efficiency.

Auto ScalingData ManagementSearch Architecture

0 likes · 18 min read

How Baidu Scaled Its Vertical Search: Elastic Scheduling and Data Management Secrets

Bilibili Tech

Jan 7, 2025 · Cloud Native

Design and Implementation of Bilibili's Large-Scale Recall System

Bilibili’s large‑scale recall system separates online processing into a two‑tier merge service and an index service, supports multi‑channel text, item‑to‑item and vector indexes with real‑time updates, uses horizontal sharding, robust CI/CD, monitoring and degradation mechanisms, and is being extended toward model‑based recall and greater automation.

BilibiliSearch Architecturecloud-native

0 likes · 16 min read

Design and Implementation of Bilibili's Large-Scale Recall System

Baidu Geek Talk

Dec 16, 2024 · Artificial Intelligence

AIAPI: Baidu's AI-Native Retrieval System for Large Language Model Applications

AIAPI, Baidu’s AI‑native retrieval platform for large language models, tackles hallucination, slow domain updates, and output opacity by delivering authoritative, timely, full‑content data through a dual‑channel architecture that combines traditional search and RAG, employs reusable ranking, graph‑enhanced data layers, dynamic caching that cuts storage by 70 %, and QueryPlan‑based QoS, achieving markedly higher retrieval quality and a 34 % speed gain with Wenxin 4.0.

AI-Native SystemsAIAPIQuery Planning

0 likes · 12 min read

AIAPI: Baidu's AI-Native Retrieval System for Large Language Model Applications

Rare Earth Juejin Tech Community

Jul 18, 2024 · Backend Development

Implementing Full‑Text Document Search with Elasticsearch and Milvus

This article describes how to combine Elasticsearch’s keyword matching with Milvus’s vector‑based semantic search to build a scalable document search service, covering data preprocessing, architecture, query handling, custom scoring, DSL configuration, and result merging.

ElasticsearchFull‑Text SearchMilvus

0 likes · 12 min read

Implementing Full‑Text Document Search with Elasticsearch and Milvus

Baidu Geek Talk

Jan 8, 2024 · Backend Development

Exgraph: A Graph Execution Engine for Task Orchestration

Exgraph, Baidu Search’s graph execution engine, uses a human‑readable description language and a robust execution core with dependency injection, object pooling, and interruption handling to orchestrate complex, parallel or conditional tasks, improving code readability and unifying diverse execution scenarios in search architecture.

DAGGo developmentObject Pooling

0 likes · 10 min read

Exgraph: A Graph Execution Engine for Task Orchestration

Baidu Geek Talk

Nov 9, 2023 · Artificial Intelligence

Deep Learning Model Architecture Evolution in Baidu Search

The article chronicles Baidu Search’s Model Architecture Group’s evolution of deep‑learning‑driven search, detailing the shift from inverted‑index to semantic vector indexing, the use of transformer‑based models for text and image queries, large‑scale offline/online pipelines, and extensive GPU‑centric optimizations such as pruning, quantization and distillation, all aimed at delivering precise, cost‑effective results to hundreds of millions of users.

ErnieGPU inferenceModel Optimization

0 likes · 14 min read

Architect

Apr 25, 2022 · Cloud Native

Designing a Cloud‑Native Intelligent Data Architecture for Baidu Search Platform

This article presents a cloud‑native redesign of Baidu's search middle‑platform that introduces intelligent data management, elastic scaling, on‑demand resource allocation, precise fan‑out, and localized computation to address efficiency, cost, stability, and performance challenges of large‑scale search workloads.

Data ManagementSearch Architecturecloud-native

0 likes · 14 min read

Designing a Cloud‑Native Intelligent Data Architecture for Baidu Search Platform

Baidu Geek Talk

Dec 15, 2021 · Cloud Native

Cloud-Native Intelligent Data Management Architecture for Baidu Search Platform

Cloud-native redesign of Baidu's search middle platform introduces partition, shard, replica, and addressing controllers that enable elastic scaling, on-demand resource allocation, precise fan‑out, and localized computation, reducing capacity adjustment time from weeks to hours, cutting costs by 30‑80%, raising availability above 99.9% and halving query latency.

Data ManagementSearch Architecturecloud-native

0 likes · 17 min read

Cloud-Native Intelligent Data Management Architecture for Baidu Search Platform

Baidu Geek Talk

Oct 29, 2021 · Industry Insights

Baidu’s QCon 2021 Highlights: Elastic Scaling, Search Architecture, AI Chips

This article compiles Baidu engineers' QCon 2021 talks, covering micro‑service evolution, large‑scale container elastic scaling, search system elasticity, AI‑chip deployment at massive scale, and cost‑focused monitoring, each with abstracts, outlines and key takeaways for practitioners.

AI chipsCloud NativeMicroservices

0 likes · 11 min read

Baidu’s QCon 2021 Highlights: Elastic Scaling, Search Architecture, AI Chips

Xianyu Technology

Mar 18, 2021 · Backend Development

Multi-Engine Concurrent Search Architecture for Idlefish

Idlefish’s new multi‑engine concurrent search architecture replaces the tightly‑coupled single‑engine pipeline with deep engine isolation, asynchronous multi‑engine recall, and unified result merging, cutting dump build time from 14 h to 5 h, shrinking memory use dramatically, improving latency by only ~15 ms, and boosting exposure by 50 % and orders by 33 %.

Big DataLuaQuery Planning

0 likes · 10 min read

Multi-Engine Concurrent Search Architecture for Idlefish

21CTO

Jul 29, 2020 · Backend Development

How JD.com Scaled Its Order Search with Elasticsearch: From Chaos to Real‑Time Dual Clusters

This article details how JD.com’s order center migrated from a MySQL‑only design to a high‑performance Elasticsearch cluster, evolving through isolation, replica tuning, master‑slave adjustments, and real‑time dual‑cluster architecture to achieve billions of documents, hundreds of millions of daily queries, and robust fault tolerance.

Backend EngineeringElasticsearchSearch Architecture

0 likes · 12 min read

How JD.com Scaled Its Order Search with Elasticsearch: From Chaos to Real‑Time Dual Clusters

MaGe Linux Operations

Jul 28, 2020 · Big Data

How Leading Chinese Companies Scale Elasticsearch for Billions of Orders

This article surveys how major Chinese tech firms such as JD.com, Ctrip, Didi, and 58.com deploy and evolve Elasticsearch clusters to handle massive order data, log analysis, real‑time monitoring, and security tasks, detailing architecture choices, shard strategies, multi‑cluster designs, and performance optimizations.

Big DataElasticsearchOrder Management

0 likes · 11 min read

How Leading Chinese Companies Scale Elasticsearch for Billions of Orders

iQIYI Technical Product Team

Jun 19, 2020 · Artificial Intelligence

Emoji Search at iQIYI Douya: From ElasticSearch to Lucene and Semantic Retrieval

iQIYI Douya’s emoji search evolved from ElasticSearch to a pure Lucene implementation and added semantic vector retrieval, enabling fast, scalable, and more accurate text‑based search of AI‑generated images for small‑to‑medium businesses by combining custom tokenization, dense embeddings, and hybrid ranking.

ElasticsearchSearch ArchitectureVector Retrieval

0 likes · 14 min read

Emoji Search at iQIYI Douya: From ElasticSearch to Lucene and Semantic Retrieval

Architecture Digest

May 8, 2020 · Big Data

Elasticsearch Adoption Cases in Chinese Companies: JD.com, Ctrip, Qunar, 58.com, Didi and More

This article surveys how major Chinese internet companies such as JD.com, Ctrip, Qunar, 58.com and Didi have adopted Elasticsearch and the Elastic Stack for high‑volume order queries, log analysis, real‑time monitoring, security analytics, and large‑scale distributed search, describing their architecture evolution, shard strategies, and operational practices.

Big DataLog AnalyticsSearch Architecture

0 likes · 16 min read

Elasticsearch Adoption Cases in Chinese Companies: JD.com, Ctrip, Qunar, 58.com, Didi and More

Programmer DD

Mar 27, 2020 · Big Data

How Leading Chinese Companies Scale Elasticsearch for Billions of Queries

This article surveys how major Chinese tech firms such as JD.com, Ctrip, Qunar, 58.com and Didi design, scale, and operate massive Elasticsearch clusters for search, real‑time analytics, and security, detailing architecture choices, shard strategies, data pipelines and performance optimizations.

Big DataDistributed SystemsElasticsearch

0 likes · 12 min read

How Leading Chinese Companies Scale Elasticsearch for Billions of Queries

JavaEdge

Jun 26, 2019 · Backend Development

How Does Elasticsearch Write and Query Data? A Deep Dive into ES Internals

This article explains the complete workflow of Elasticsearch write, read, search, delete, and update operations, covering coordinating nodes, shard routing, buffer refresh, translog, segment files, commit/flush processes, and the underlying inverted index mechanism.

ElasticsearchSearch Architecturenear real-time

0 likes · 10 min read

How Does Elasticsearch Write and Query Data? A Deep Dive into ES Internals

Fangduoduo Tech

May 25, 2019 · Backend Development

How Fangdd Scales Real‑Estate Search with Elasticsearch: Architecture & Lessons

This article explains how Fangdd leverages Elasticsearch to boost search performance across consumer, broker, and internal products, detailing a platformized architecture that separates indexing and querying, addresses operational challenges, and outlines design patterns for index management and incremental updates.

Backend DevelopmentElasticsearchMicroservices

0 likes · 12 min read

How Fangdd Scales Real‑Estate Search with Elasticsearch: Architecture & Lessons

Youzan Coder

Apr 7, 2019 · Industry Insights

How Youzan Scaled Order Search: Hot‑State Indexing and AKF Expansion

This article reviews the evolution of Youzan's order search architecture over two years, detailing challenges from data growth, the creation of a hot‑state index covering half of search traffic, time‑sharded indexes, and the AKF expansion cube that guides multi‑axis scalability.

Backend DevelopmentBig DataElasticsearch

0 likes · 10 min read

How Youzan Scaled Order Search: Hot‑State Indexing and AKF Expansion

Mafengwo Technology

Mar 28, 2019 · Backend Development

Boosting Search Performance with a Golang Concurrent Proxy

This article explains how Mafengwo transformed its search service by replacing serial function calls with a Golang‑based concurrent proxy, reducing average latency from 400‑500 ms to around 240 ms while improving scalability, fault tolerance, and resource utilization.

Backend DevelopmentGolangProxy

0 likes · 6 min read

Boosting Search Performance with a Golang Concurrent Proxy

Youzan Coder

Aug 31, 2018 · Big Data

Evolution of Youzan Search Platform Architecture: From 1.0 to 4.0

The Youzan Search Platform evolved from a simple Elasticsearch cluster in 2015 to a modular, message‑driven architecture with proxy validation, caching, and management tools, and now plans a cloud‑native, Kubernetes‑based 4.0 version that automates data sync, isolates workloads, and scales elastically to support billions of records.

Data IntegrationElasticsearchProxy

0 likes · 14 min read

Evolution of Youzan Search Platform Architecture: From 1.0 to 4.0

21CTO

Apr 3, 2017 · Backend Development

Inside Mogujie's Search Engine: Architecture and Real‑Time Ranking Flow

This article details Mogujie's end‑to‑end search system architecture, describing both offline and online components such as Topn, ABTest, QR, UPS, the search engine, precision ranking, and feature management, and walks through a concrete online request example from query to final ranked results.

Backend DevelopmentSearch Architecturee‑commerce

0 likes · 12 min read

Inside Mogujie's Search Engine: Architecture and Real‑Time Ranking Flow

Architecture Digest

Apr 2, 2017 · Artificial Intelligence

Mogujie's Search System Architecture and Online Request Flow

This article introduces Mogujie's end‑to‑end search system architecture, detailing its online and offline components such as Topn, ABTest, QR, fine‑ranking, search engine, UPS, and feature platforms, and then walks through a real‑world online request example to illustrate how queries are processed, rewritten, personalized, and finally ranked.

MogujieQuery RewriteSearch Architecture

0 likes · 11 min read

Mogujie's Search System Architecture and Online Request Flow

Baidu Maps Tech Team

Jun 2, 2016 · Backend Development

How Baidu Maps Re‑engineered Its Indexing Unit for Scalable Data Storage

This article details Baidu Maps' technical team’s refactoring of the indexing (build) unit, outlining existing bottlenecks, design challenges, and a new decoupled architecture that separates storage, incremental updates, and full‑index construction using distributed table storage and message‑driven pipelines to improve scalability and reliability.

Baidu MapsScalabilitySearch Architecture

0 likes · 9 min read

How Baidu Maps Re‑engineered Its Indexing Unit for Scalable Data Storage

21CTO

Nov 17, 2015 · Backend Development

Scaling Search for 11.11: Distributed Engine, Smart Routing & Auto‑Scaling

This article explains how a major e‑commerce platform built a horizontally scalable distributed search engine, designed efficient sharding and routing strategies, and implemented automated deployment, rapid scaling, and real‑time monitoring to handle the massive traffic of the 11.11 shopping festival.

Auto ScalingBackendDistributed Search

0 likes · 13 min read