Bilibili has freely licensed four big-data invention patents—covering precise data reading, cleaning, transmission, and rapid positioning—to foster industry innovation, support the digital ecosystem outlined in China’s 14th Five-Year Plan, and showcase its commitment to open-source big-data solutions.
This article explains how Bilibili’s KV storage system evolved from early Redis/Memcache solutions to a highly available, 100‑times scalable architecture using Raft, sharding, multi‑active disaster recovery, and performance optimizations to support massive traffic peaks and diverse business scenarios.
This article details how Bilibili's KV storage system evolved from early solutions like Redis and MySQL to a highly scalable, high‑availability distributed architecture, describing its overall design, node components, data splitting, multi‑active disaster recovery, typical use cases, and operational challenges.
The article outlines common database and cache failures—such as instance outages, replication lag, data corruption, and cache avalanches—while detailing Bilibili’s mitigation strategies including high‑availability architectures, scaling, multi‑active designs, proxy controls, slow‑query alerts, fault‑injection drills, and ongoing resilience improvements.
This article walks through using Python to crawl Bilibili danmu (bullet comments), process the text with jieba, and generate a visual word cloud, providing complete code examples and tips for adapting the script to other videos.
This article chronicles Bilibili Live’s eight‑year journey from a simple LAMP monolith to a highly available microservice ecosystem, detailing the technical motivations, design principles, Swoole‑based services, containerization, Golang migration, custom gateways, hot‑key handling, and operational safeguards that enabled millions of concurrent viewers.
Bilibili’s SYS team is advancing green data‑center technology by aligning with national energy‑efficiency mandates, co‑authoring cold‑plate liquid‑cooling standards, and piloting the custom R2‑AZ2 Innovation Room Phase‑1, which combines indirect evaporative and magnetic‑levitation hybrid cooling to achieve PUEs as low as 1.13 while testing AI‑driven O&M tools for future large‑scale, low‑carbon deployment.
This article examines Bilibili's end‑to‑end journey of adopting, evaluating, and extending academic adaptive bitrate (ABR) algorithms—such as THROUGHPUT, MPC, BOLA, and Pensieve—to improve mobile video QoE, detailing the shortcomings of traditional methods, the design of a real‑time QoE model, network preprocessing, intelligent resolution framework, model deployment, and user‑preference adaptations.
Bilibili enhanced mobile video streaming by replacing standard ABR methods with an intelligent adaptive bitrate system that uses a real‑time QoE model, refined network‑speed preprocessing, long‑term feature analysis, decision‑tree‑based neural models, and personalized user modes to balance resolution, buffering, and data usage.
This article walks through building a Python tool that extracts Bilibili video and audio URLs via developer tools, downloads the media files, merges them using moviepy, and wraps the process in a simple PySimpleGUI interface, complete with full source code.
This article details Bilibili's end‑to‑end full‑link load‑testing solution for its live‑stream gifting service, covering industry alternatives, the chosen architecture, a three‑stage automated testing framework, link analysis, configuration, validation, and practical case studies to ensure system stability under massive traffic spikes.
Bilibili automated full‑link pressure testing for its high‑traffic live‑stream gifting service by adopting traffic co‑location with storage isolation, creating shadow tables, keys and topics, and building a three‑phase, three‑layer framework that analyses links, confirms configurations, and verifies end‑to‑end behavior across all services.
Bilibili’s full‑chain load‑testing architecture embeds a unique scenario identifier in every request, propagates it through RPC, databases, caches and async queues, isolates production data via shadow resources, and provides a transparent SDK, link analyzer and mock server, enabling comprehensive testing that uncovered dozens of issues and boosted live‑streaming TPS by 140 % without major code changes.
The talk, led by Bilibili’s risk‑control head, outlines Internet risk‑control fundamentals, precise traffic perception techniques, and a full‑scenario joint‑defense framework that combines hierarchical identification, cross‑scene signal sharing, statistical anomaly detection, and layered mitigation (soft and hard) to counter black‑market attacks on platforms.
This presentation explores internet risk control, detailing its classification, precise traffic perception methods, and full‑scenario joint defense and control strategies, using Bilibili as a case study to illustrate challenges, adversaries, detection techniques, and mitigation measures.
Bilibili’s Open Platform streamlines video uploads by replacing a complex multi‑parameter flow with a single upload_token that encapsulates authentication and file metadata, enabling chunked uploads, merging, and manuscript submission via a JavaScript SDK, which cuts integration errors and speeds first‑time submissions by over sixty percent.
This article chronicles Bilibili’s transition from a PHP monolith to a Go‑based bilizone monolith, its subsequent decomposition into microservices, the adoption of Backend‑for‑Frontend (BFF) layers, and the eventual consolidation into a unified API gateway that streamlines routing, security, and traffic management across the platform.
Bilibili’s SRE team, confronting rapid growth and complex systems, built a systematic stability operation that includes emergency response, incident handling, on‑call scheduling, and an Event Operations Center platform, using metrics like MTTR, MTTI and AI‑assisted automation to reduce downtime and improve reliability.
Peng Liangyou describes how Bilibili’s large‑scale distributed KV storage adopts Netflix‑style chaos engineering—defining steady‑state hypotheses, replicating production environments, injecting CPU, memory, network and replica faults via automated “monkey” experiments, monitoring latency and durability with Prometheus/Grafana, and over 1.5 years preventing critical incidents while cutting testing costs and enabling incremental, standards‑based reliability improvements.
Bilibili built an AI collaboration platform based on AIFlow to simplify real-time machine-learning workflows, evolving through three stages that added event-driven scheduling, UI-driven parameter management, version snapshots, and a stateless client-server design, while enabling stream-batch fusion for feature back-filling; future work targets high availability, Airflow 2.0 compatibility, and richer streaming ML operators.
The 2022 Bilibili Technology Patent Selection Awards honored ten innovative projects across Best Popularity and Most Popular categories, showcasing advances such as advanced bullet comments, optimized gift animation, video rendering, virtual avatar production, virtual material editing, mini‑program integration, AI‑driven live‑stream switching, blur‑face enhancement, and ghost video tools.
Video bitrate depends on content complexity, resolution, frame rate, and the chosen codec and bitrate‑control method, with modern encoders like HEVC and AV1 using advanced prediction, transform, quantization and entropy coding to dramatically reduce size while preserving perceived quality for smoother streaming.
OpenSergo, launched by Alibaba Cloud, bilibili, ByteDance and major open‑source communities, seeks to create a language‑agnostic, technology‑agnostic service‑governance specification and implementation that unifies control and data planes for heterogeneous microservices, easing enterprise complexity, reducing developer burden, and encouraging open‑source contributions.
The article explores the intense “involution” of Bilibili’s knowledge zone, highlighting how elementary‑school children like Ongo and vita are mastering algorithms, Swift Playgrounds, and Microbit projects, while parents debate the benefits and risks of such early‑stage coding immersion.
Bilibili’s data retrieval journey progressed from a fragmented, chimney‑style pipeline to a unified Flink‑based service layer with the Ark construction system and Akuya SQL engine, and finally to an Iceberg‑driven lakehouse that eliminates data duplication, streamlines cross‑engine optimization, and offers platformized, low‑latency analytics.
Recent incidents of illegal camera hacking—ranging from public surveillance footage posted on Bilibili to a livestreamed gynecological surgery—highlight severe privacy violations, prompting platform bans, police action, and expert advice on securing cameras against unauthorized access.
This article walks through scraping Bilibili documentary data with Python, processing it using pandas, and visualizing regional distributions, genre comparisons, yearly trends, episode lengths, popularity metrics, and comment timing to uncover insights about Chinese, UK, and US documentary patterns.
This article demonstrates how to collect, process, and visualize Bilibili documentary metadata using Python APIs, pandas, and various plotting libraries, revealing insights into regional distribution, genre trends, episode lengths, popularity metrics, and comment dynamics across Chinese, British, and American documentary collections.
This guide shows how to use Python tools like you‑get and a custom BililLocal player to download Bilibili videos, handle download issues, retrieve danmu subtitles, and play the content locally, providing step‑by‑step instructions and useful resources.
This tutorial explains how to locate Bilibili video danmaku (bullet‑comment) APIs, extract the CID, and use Python libraries such as requests, BeautifulSoup, and pandas to download, clean, and save the comment data to CSV files, with an optional API‑based shortcut.
In this article, a programmer recounts the recent Bilibili outage, analyzes its timeline, proposes technical root‑cause hypotheses such as CDN failure and service‑chain avalanche, shares insights from the platform’s high‑availability architecture, and outlines preventive techniques for building more resilient backend systems.
The article analyzes Bilibili's recent half‑hour service disruption, explores technical rumors such as an etcd crash, examines Kubernetes‑based cloud‑native infrastructure, reviews similar historic outages, and offers expert recommendations for improving high‑availability and disaster‑recovery in large‑scale internet services.
The article chronicles Chen Rui’s journey from a Kingsoft antivirus leader to Bilibili’s CEO, highlighting his strategic decisions, community‑centric product management, and the platform’s evolution into a dominant cultural hub for China’s Gen‑Z users.
Bilibili’s journey from a small anime‑focused community to a diversified, publicly listed media powerhouse illustrates how strategic leadership, content expansion, and innovative monetization transformed a niche platform into a multibillion‑dollar enterprise serving millions of Chinese youth.
A curated list of free Bilibili video courses covering Java, computer networks, operating systems, Redis, RocketMQ, Kafka, and MySQL, each with brief descriptions and direct links for beginners and intermediate learners.
This article introduces BiliBiliTool, an open‑source utility that automates daily Bilibili actions such as watching, sharing, and coin‑throwing to earn experience, quickly upgrade account levels, and collect rewards, while providing step‑by‑step instructions for setup and execution.
This tutorial shows how to use Python to fetch data from Bilibili’s “马保国” channel via its public API, extract video metadata, clean and visualize 14,000 records, and generate insights such as top‑viewed videos and a comment word cloud.
This article demonstrates how to use Python to crawl over 200,000 Bilibili comments, analyze popular memes across different content categories, and presents a step‑by‑step guide to extracting danmu via network requests with code examples for practical data mining.
This article describes how to discover Bilibili's comment XML APIs, decode the embedded CRC32‑hashed user IDs, choose an appropriate database schema, and implement a Python‑PHP tool that retrieves, filters, and displays comments based on video CID and keyword.
The article details how a data analyst scraped comments from Zhihu, Weibo, and Bilibili about the 2020 Youth Day video “后浪”, applied Baidu AI sentiment analysis and Python word‑cloud generation, and compared the differing emotional tones across platforms.
This article presents a comprehensive DevOps case study of Bilibili, covering its cultural background, community ecosystem, user‑centric demand management, migration to high‑performance microservices, and the implementation of logging, monitoring, and real‑time data platforms to support rapid, reliable delivery.
The presentation by Bilibili's real‑time platform lead details the design and implementation of a Flink‑based streaming data platform, explains how AI workloads are integrated, shares architectural decisions and operational insights, and provides the full slide deck for knowledge dissemination.
This article chronicles Bilibili’s transition from a traditional MVC workflow to a modern front‑end/back‑end separation, detailing the adoption of Node middle‑layers, Vue SSR, architecture refactoring, deployment strategies, performance testing, and lessons learned throughout 2017.
