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NetEase Media Technology Team

NetEase Media Technology Team

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Recent Articles

Latest from NetEase Media Technology Team

55 recent articles
NetEase Media Technology Team
NetEase Media Technology Team
Mar 8, 2021 · Mobile Development

Cut Android Build Times by Up to 90% Using Componentized Gradle and Remote Compilation

Facing sluggish Gradle builds for a large Android news app, the team applied componentized compilation, Artifactory caching, minimal compile mode, parameter tuning, and remote compilation via SSH, achieving up to 90% faster non‑incremental builds and 75% faster incremental builds while maintaining stability across the CI pipeline.

AndroidArtifactoryBuild Optimization
0 likes · 13 min read
Cut Android Build Times by Up to 90% Using Componentized Gradle and Remote Compilation
NetEase Media Technology Team
NetEase Media Technology Team
Jan 15, 2021 · Backend Development

Go Language Practice and Ngo Framework Development at NetEase Media

Facing high memory usage and slow startup after containerizing its Java services, NetEase Media adopted Go in 2020, leveraging its fast compilation, low‑resource footprint and goroutine‑based concurrency to build the high‑performance Ngo framework, which outperforms Spring‑Boot in throughput while using far less memory.

Go languageGoroutineJava to Go migration
0 likes · 32 min read
Go Language Practice and Ngo Framework Development at NetEase Media
NetEase Media Technology Team
NetEase Media Technology Team
Dec 23, 2020 · Databases

Practical Experience of MyRocks in NetEase Media Business

Since 2019 NetEase Media has migrated several recommendation and account services from RDS to MyRocks, cutting disk usage by up to 68 % and halving response times while handling 40‑50 k QPS write‑heavy workloads, though the engine lacks partitioning, online DDL, and certain index types, requiring careful workload assessment.

Database OptimizationLSM‑TreeMyRocks
0 likes · 12 min read
Practical Experience of MyRocks in NetEase Media Business
NetEase Media Technology Team
NetEase Media Technology Team
Dec 8, 2020 · Operations

Comprehensive Online Load‑Testing and Stability Assurance Framework

The stability‑assurance squad built an online load‑testing framework that injects global TraceIds via a Java‑agent, records real‑traffic, routes test writes to shadow databases and caches, enforces automatic stop‑rules, and provides a UI platform, reducing cost, improving capacity insight, and enabling safe fault‑injection drills.

Distributed TracingJava AgentLoad Testing
0 likes · 12 min read
Comprehensive Online Load‑Testing and Stability Assurance Framework
NetEase Media Technology Team
NetEase Media Technology Team
Aug 13, 2020 · Cloud Native

How NetEase Media Scaled Its Infrastructure with Containerization and Service Mesh

NetEase Media transformed its infrastructure by containerizing services, establishing multiple resource pools, implementing a ServiceMesh with NSF, and isolating beta and production environments, resulting in higher CPU utilization, automated scaling, and improved stability, while sharing lessons learned and future plans.

Cloud NativeInfrastructureKubernetes
0 likes · 22 min read
How NetEase Media Scaled Its Infrastructure with Containerization and Service Mesh
NetEase Media Technology Team
NetEase Media Technology Team
Jul 24, 2020 · Artificial Intelligence

Survey of Video Action Recognition Algorithms: 3D and 2D Convolutional Networks and Pre‑training

This survey reviews video action recognition, comparing 3D convolutional networks that jointly model spatial‑temporal cues but are computationally heavy with 2D‑based approaches like TSM and TIN that embed temporal shifts efficiently, and emphasizes how large‑scale pre‑training markedly improves performance despite limited labeled data.

2D convolutional networks3D convolutional networksComputer Vision
0 likes · 13 min read
Survey of Video Action Recognition Algorithms: 3D and 2D Convolutional Networks and Pre‑training
NetEase Media Technology Team
NetEase Media Technology Team
Jun 12, 2020 · Artificial Intelligence

Semantic Text Understanding for NetEase News Feed Recommendation

NetEase improves its news‑feed recommendation by applying a multi‑stage semantic text understanding pipeline—lexical analysis, hierarchical content tagging, and quality filtering—using two‑level classifiers, LDA‑based topic modeling, multi‑label concept and entity extraction, and dense vector representations to better capture user interests and boost personalization performance.

NLPRecommendation Systemsfeature engineering
0 likes · 9 min read
Semantic Text Understanding for NetEase News Feed Recommendation