NetEase Game Operations Platform
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NetEase Game Operations Platform

The NetEase Game Automated Operations Platform delivers stable services for thousands of NetEase titles, focusing on efficient ops workflows, intelligent monitoring, and virtualization.

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Recent Articles

Latest from NetEase Game Operations Platform

81 recent articles
NetEase Game Operations Platform
NetEase Game Operations Platform
Feb 1, 2020 · Operations

Practical Guide to GitLab CI/CD for Microservice Projects

This article presents a comprehensive practical guide on using GitLab's built‑in CI/CD features for microservice projects, covering pipeline, stage, job concepts, .gitlab-ci.yml configuration, runner installation, Docker image building, registry push, and deployment strategies, supplemented with code snippets and diagrams.

CI/CDDevOpsDocker
0 likes · 10 min read
Practical Guide to GitLab CI/CD for Microservice Projects
NetEase Game Operations Platform
NetEase Game Operations Platform
Jan 18, 2020 · Mobile Development

Understanding iOS Push Notifications: Remote and Local Push, History, and Implementation Details

This article provides a comprehensive overview of iOS push notification mechanisms, covering remote and local push differences, device token acquisition across iOS versions, silent push constraints, scheduling nuances, and the evolution of notification APIs from iOS 7 to iOS 13, with practical code examples.

APNSObjective‑CPush Notification
0 likes · 17 min read
Understanding iOS Push Notifications: Remote and Local Push, History, and Implementation Details
NetEase Game Operations Platform
NetEase Game Operations Platform
Jan 11, 2020 · Operations

Best Practices for Writing Efficient Dockerfiles

This article presents a concise Dockerfile template, explains how to build efficient images by minimizing layers, leveraging caches, using multi‑stage builds, and addresses common pitfalls such as over‑caching, proper use of ARG/ENV, COPY vs ADD, and CMD/ENTRYPOINT configurations.

DevOpsDockerDockerfile
0 likes · 12 min read
Best Practices for Writing Efficient Dockerfiles
NetEase Game Operations Platform
NetEase Game Operations Platform
Dec 21, 2019 · Artificial Intelligence

Time Series Forecasting Algorithms and Their Application in NetEase Game Monitoring

The article reviews traditional, neural network, and open‑source time‑series forecasting methods, explains their strengths and limitations, and demonstrates how NetEase applies short‑term and long‑term prediction models such as Holt‑Winters, ARIMA, STL, Prophet, and LSTM to improve game monitoring and proactive alerting.

ARIMAHolt-WintersLSTM
0 likes · 12 min read
Time Series Forecasting Algorithms and Their Application in NetEase Game Monitoring
NetEase Game Operations Platform
NetEase Game Operations Platform
Dec 14, 2019 · Frontend Development

Building a Resource Topology Diagram with d3.js

This article explains how to use d3.js to create interactive resource topology diagrams, covering SVG basics, d3 selections, data binding, enter/update/exit patterns, force‑directed layout, path calculations, and various optimizations such as text centering, arrow markers, and intersection handling.

Data VisualizationSVGd3.js
0 likes · 22 min read
Building a Resource Topology Diagram with d3.js
NetEase Game Operations Platform
NetEase Game Operations Platform
Dec 7, 2019 · Operations

Intelligent Anomaly Detection for Operations Maintenance: Machine Learning Methods and Workflow

This article explains the importance of operations maintenance, outlines the challenges of traditional rule‑based anomaly detection, and describes how machine‑learning‑driven AIOps—including feature engineering, unsupervised and supervised models—can provide more accurate, scalable, and automated detection of server anomalies.

aiopsfeature engineeringoperations
0 likes · 10 min read
Intelligent Anomaly Detection for Operations Maintenance: Machine Learning Methods and Workflow
NetEase Game Operations Platform
NetEase Game Operations Platform
Nov 24, 2019 · Backend Development

Optimizing a Python Flask Backend: Reducing Response Time from 37.6 s to 1.47 s with Profiling and Database Refactoring

This article walks through a systematic performance investigation of a Python‑Flask backend, using Chrome Network, flame‑graph profiling, and MySQL query redesign to cut a 37.6‑second page load down to 1.47 seconds while highlighting practical code‑level and architectural optimizations.

Optimizationbackendprofiling
0 likes · 10 min read
Optimizing a Python Flask Backend: Reducing Response Time from 37.6 s to 1.47 s with Profiling and Database Refactoring