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 一份更新且井然有序的阅读清单,旨在阐释可扩展、可靠且高性能的大规模系统模式。相关概念由杰出工程师撰写的文章和可靠的参考文献进行阐释。案例研究取自服务于数百万乃至数十亿用户的久经考验的系统。 #### 如果您的系统运行缓慢 > 通过回顾一些[设计原则](#principle)并了解科技公司如何解决[可扩展性](#scalability)和[性能](#performance)问题,了解您的问题:可扩展性问题(单用户运行速度快,但在高负载下运行缓慢)或性能问题(单用户运行缓慢)。[智能](#intelligence)部分专为从事大数据和深度学习数据及机器学习工作的人士打造。 #### 如果您的系统崩溃 > “即使有一天你失去了一切,只要你保持冷静,你就能重新开始!”——Thuan Pham,Uber 前首席技术官。所以,保持冷静,记住[可用性](#可用性)和[稳定性](#稳定性)很重要! #### 如果你正在进行系统设计面试 > 在白板上设计系统之前,请查看一些[面试笔记](#面试)和[带有完整图表的真实架构](#架构),以获得全面的了解。你可以查看一些科技巨头工程师的[演讲](#演讲),了解他们如何构建、扩展和优化他们的系统。祝你好运! #### 如果你正在组建你的理想团队 > 扩大团队规模的目标不是扩大团队规模,而是提高团队产出和价值。你可以在[组织](#组织)部分了解科技公司如何在各个方面实现这一目标:招聘、管理、组织、文化和沟通。 #### 社区力量 > 非常欢迎贡献!你可能想看看[贡献指南](CONTRIBUTING.md)。如果您发现此处的链接不再维护或不合适,请提交拉取请求! > 这个项目倾注了大量时间的辛勤劳动。如果您觉得有帮助,请在 Facebook、[Twitter](https://ctt.ec/V8B2p)、[微博](http://t.cn/RnjFLCB) 或您的聊天群中分享!知识就是力量,分享知识力量倍增。谢谢! ## 内容 - [原则](#原则) - [可扩展性](#可扩展性) - [可用性](#可用性) - [稳定性](#稳定性) - [性能](#性能) - [智能](#智能) - [架构](#架构) - [访谈](#访谈) - [组织](#组织) - [讲座](#讲座) - [书籍](#书籍) ## 原则 * [从超大规模服务中汲取的经验教训 - Eric Brewer,加州大学伯克利分校及谷歌](https://people.eecs.berkeley.edu/~brewer/papers/GiantScale-IEEE.pdf) * [构建大型分布式系统的设计、经验教训和建议 - Jeff Dean,谷歌](https://www.cs.cornell.edu/projects/ladis2009/talks/dean-keynote-ladis2009.pdf) * [如何设计优秀的 API 及其重要性 - Joshua Bloch,卡内基梅隆大学及谷歌](https://www.infoq.com/presentations/effective-api-design) * [论效率、可靠性和扩展性 - James Hamilton,AWS 副总裁](http://mvdirona.com/jrh/work/) * [混沌原则工程](https://www.usenix.org/conference/srecon17americas/program/presentation/rosenthal) * [在混乱中寻找秩序](https://www.usenix.org/conference/srecon16/program/presentation/lueder) * [十二要素应用](https://12factor.net/) * [简洁架构](https://blog.cleancoder.com/uncle-bob/2012/08/13/the-clean-architecture.html) * [高内聚低耦合](http://www.math-cs.gordon.edu/courses/cs211/lectures-2009/Cohesion,Coupling,MVC.pdf) * [单体架构与微服务](https://medium.com/@SkyscannerEng/monoliths-and-microservices-8c65708c3dbf) * [CAP 定理及其权衡](http://robertgreiner.com/2014/08/cap-theorem-revisited/) * [CP 数据库和 AP 数据库](https://blog.andyet.com/2014/10/01/right-database) * [无状态 vs 有状态可扩展性](http://ithare.com/scaling-stateful-objects/) * [纵向扩展 vs 横向扩展:隐性成本](https://blog.codinghorror.com/scaling-up-vs-scaling-out-hidden-costs/) * [ACID 和BASE](https://neo4j.com/blog/acid-vs-base-consistency-models-explained/) * [阻塞/非阻塞和同步/异步](https://blogs.msdn.microsoft.com/csliu/2009/08/27/io-concept-blockingnon-blocking-vs-syncasync/) * [数据库的性能和可扩展性](https://use-the-index-luke.com/sql/testing-scalability) * [数据库隔离级别及其对性能和可扩展性的影响](http://highscalability.com/blog/2011/2/10/database-isolation-levels-and-their-effects-on-performance-a.html) * [大型数据库中数据丢失的概率集群](https://martin.kleppmann.com/2017/01/26/data-loss-in-large-clusters.html) * [高可扩展解决方案的数据访问:使用 SQL、NoSQL 和多语言持久化](https://docs.microsoft.com/en-us/previous-versions/msp-n-p/dn271399(v=pandp.10)) * [SQL 与 NoSQL](https://www.upwork.com/hiring/data/sql-vs-nosql-databases-whats-the-difference/) * [SQL 与 NoSQL - Salesforce 的经验教训](https://engineering.salesforce.com/sql-or-nosql-9eaf1d92545b) * [NoSQL 数据库:调查与决策指南](https://medium.baqend.com/nosql-databases-a-survey-and-decision-guidance-ea7823a822d) * [分片工作原理](https://medium.com/@jeeyoungk/how-sharding-works-b4dec46b3f6) * [一致性哈希](http://www.tom-e-white.com/2007/11/consistent-hashing.html) * [一致性哈希:算法权衡](https://medium.com/@dgryski/consistent-hashing-algorithmic-tradeoffs-ef6b8e2fcae8) * [不要被哈希技巧欺骗](https://booking.ai/dont-be-tricked-by-the-hashing-trick-192a6aae3087) * [Netflix 的统一一致性哈希](https://medium.com/netflix-techblog/distributing-content-to-open-connect-3e3e391d4dc9) * [最终一致性 - Werner Vogels,亚马逊首席技术官](https://www.allthingsdistributed.com/2008/12/eventually_consistent.html) * [缓存为王](https://www.stevesouders.com/blog/2012/10/11/cache-is-king/) * [反缓存](https://www.the-paper-trail.org/post/2014-06-06-paper-notes-anti-caching/) * [理解延迟](http://highscalability.com/latency-everywhere-and-it-costs-you-sales-how-crush-it) * [每个程序员都应该知道的延迟数字了解](http://norvig.com/21-days.html#answers) * [服务可用性计算](https://queue.acm.org/detail.cfm?id=3096459&__s=dnkxuaws9pogqdnxmx8i) * [扩展 Web 应用程序时的架构问题:瓶颈、数据库、CPU、IO](http://highscalability.com/blog/2014/5/12/4-architecture-issues-when-scaling-web-applications-bottlene.html) * [常见瓶颈](http://highscalability.com/blog/2012/5/16/big-list-of-20-common-bottlenecks.html) * [分布式事务之外的生活](https://queue.acm.org/detail.cfm?id=3025012) * [依赖软件在各个层级可靠地重定向流量](https://www.usenix.org/conference/srecon15/program/presentation/taveira) * [故意打破常规](https://www.usenix.org/conference/srecon17americas/program/presentation/andrus) * [避免过度工程](https://medium.com/@rdsubhas/10-modern-software-engineering-mistakes-bc67fbef4fc8) * [可扩展性最差实践](https://www.infoq.com/articles/scalability-worst-practices) * [使用可靠的技术 - 不要重复造轮子 - 保持简洁!](https://medium.com/@DataStax/instagram-engineerings-3-rules-to-a-scalable-cloud-application-architecture-c44afed31406) * [通过分散复杂性实现简单性](https://engineering.zalando.com/posts/2018/01/simplicity-by-distributing-complexity.html) * [为什么 Over-R过度使用是错误的](http://tech.transferwise.com/why-over-reusing-is-bad/) * [性能是一项功能](https://blog.codinghorror.com/performance-is-a-feature/) * [将性能融入工作流程](https://codeascraft.com/2014/12/11/make-performance-part-of-your-workflow/) * [服务器端渲染相对于客户端渲染的优势](https://medium.com/walmartlabs/the-benefits-of-server-side-rendering-over-client-side-rendering-5d07ff2cefe8) * [自动化与抽象:Facebook 的经验教训](https://architecht.io/lessons-from-facebook-on-engineering-for-scale-f5716f0afc7a) * [AWS 的注意事项和注意事项](https://8thlight.com/blog/sarah-sunday/2017/09/15/aws-dos-and-donts.html) * [(UI) 设计无法扩展 - Stanley Wood,Spotify 设计总监](https://medium.com/@hellostanley/design-doesnt-scale-4d81e12cbc3e) * [Linux 性能](http://www.brendangregg.com/linuxperf.html) * [构建快速且具有弹性的 Web 应用程序 - Ilya Grigorik](https://www.igvita.com/2016/05/20/building-fast-and-resilient-web-applications/) * [接受部分故障,最大限度地减少服务损失](https://www.usenix.org/conference/srecon17asia/program/presentation/wang_daxin) * [设计弹性](http://highscalability.com/blog/2012/12/31/designing-for-resiliency-will-be-so-2013.html) * [自我修复设计](https://docs.microsoft.com/en-us/azure/architecture/guide/design-principles/self-healing) * [横向扩展设计](https://docs.microsoft.com/en-us/azure/architecture/guide/design-principles/scale-out) * [演进设计](https://docs.microsoft.com/en-us/azure/architecture/guide/design-principles/design-for-evolution) * [从错误中学习](http://highscalability.com/blog/2013/8/26/reddit-lessons-learned-from-mistakes-made-scaling-to-1-billi.html) ## 可扩展性 * [微服务和编排](https://martinfowler.com/microservices/) * [Uber 的面向领域的微服务架构](https://eng.uber.com/microservice-architecture/) * [SoundCloud 的服务架构(3 个部分:领域网关、增值服务、BFF)](https://developers.soundcloud.com/blog/service-architecture-3) * [Riot Games 的容器技术(8 个部分)](https://engineering.riotgames.com/news/thinking-inside-container) * [Pinterest 的容器化技术](https://medium.com/@Pinterest_Engineering/containerization-at-pinterest-92295347f2f3) * [Pinterest 容器使用方式的演变Netflix](https://medium.com/netflix-techblog/the-evolution-of-container-usage-at-netflix-3abfc096781b) * [Uber 的 MySQL Docker 化](https://eng.uber.com/dockerizing-mysql/) * [Spotify 的微服务测试](https://labs.spotify.com/2018/01/11/testing-of-microservices/) * [Treehouse 的 Docker 生产环境](https://medium.com/treehouse-engineering/lessons-learned-running-docker-in-production-5dce99ece770) * [SoundCloud 的微服务](https://developers.soundcloud.com/blog/inside-a-soundcloud-microservice) * [Stripe 的 Kubernetes 可靠运维](https://stripe.com/blog/operating-kubernetes) * [Trivago 使用 Istio 实现跨集群流量镜像](https://tech.trivago.com/2020/06/10/cross-cluster-traffic-mirroring-with-istio/) * [纽约时报:Agrarian-Scale Kubernetes(三部分)](https://open.nytimes.com/agrarian-scale-kubernetes-part-3-ee459887ed7e) * [BBC 的 Nanoservices](https://medium.com/bbc-design-engineering/powering-bbc-online-with-nanoservices-727840ba015b) * [彭博社:PowerfulSeal:Kubernetes 集群测试工具](https://www.techatbloomberg.com/blog/powerfulseal-testing-tool-kubernetes-clusters/) * [主持人:微服务编排器Netflix](https://medium.com/netflix-techblog/netflix-conductor-a-microservices-orchestrator-2e8d4771bf40) * [Docker 容器助力 Shopify 超过 10 万家网店](https://shopifyengineering.myshopify.com/blogs/engineering/docker-at-shopify-how-we-built-containers-that-power-over-100-000-online-shops) * [Medium 的微服务架构](https://medium.engineering/microservice-architecture-at-medium-9c33805eb74f) * [Betabrand 从裸机到 Kubernetes](https://boxunix.com/post/bare_metal_to_kube/) * [Kubernetes 在Tinder](https://medium.com/tinder-engineering/tinders-move-to-kubernetes-cda2a6372f44) * [Quora 的 Kubernetes](https://www.quora.com/q/quoraengineering/Adopting-Kubernetes-at-Quora) * [Pinterest 的 Kubernetes 平台](https://medium.com/pinterest-engineering/building-a-kubernetes-platform-at-pinterest-fb3d9571c948) * [Nubank 的微服务](https://medium.com/building-nubank/microservices-at-nubank-an-overview-2ebcb336c64d) * [Mercari 的微服务支付交易管理](https://engineering.mercari.com/en/blog/entry/20210831-2019-06-07-155849/) * [GRIT:eBay 跨微服务分布式事务协议](https://tech.ebayinc.com/engineering/grit-a-protocol-for-distributed-transactions-across-microservices/) * [Rubix:Palantir 的 Kubernetes](https://medium.com/palantir/introducing-rubix-kubernetes-at-palantir-ab0ce16ea42e) * [CRISP:Uber 微服务架构关键路径分析](https://eng.uber.com/crisp-critical-path-analysis-for-microservice-architectures/) * [分布式缓存](https://www.wix.engineering/post/scaling-to-100m-to-cache-or-not-to-cache) * [EVCache:Netflix 的分布式内存缓存](https://medium.com/netflix-techblog/caching-for-a-global-netflix-7bcc457012f1) * [Netflix 的 EVCache 缓存预热器基础设施](https://medium.com/netflix-techblog/cache-warming-agility-for-a-stateful-service-2d3b1da82642) * [Memsniff:Box 的强大 Memcache 流量分析器](https://blog.box.com/blog/introducing-memsniff-robust-memcache-traffic-analyzer/) * [使用一致性哈希和缓存涂抹进行缓存Etsy](https://codeascraft.com/2017/11/30/how-etsy-caches/) * [Facebook 照片缓存分析](https://code.facebook.com/posts/220956754772273/an-analysis-of-facebook-photo-caching/) * [Facebook 缓存效率练习](https://code.facebook.com/posts/964122680272229/web-performance-cache-efficiency-exercise/) * [tCache:Trivago 可扩展数据感知 Java 缓存](http://tech.trivago.com/2015/10/15/tcache/) * [Pycache:Quora 进程内缓存](https://engineering.quora.com/Pycache-lightning-fast-in-process-caching) * [减少 Memcached 内存使用量Trivago 内存使用率降低 50%](http://tech.trivago.com/2017/12/19/how-trivago-reduced-memcached-memory-usage-by-50/) * [Yelp 内部服务调用缓存](https://engineeringblog.yelp.com/2018/03/caching-internal-service-calls-at-yelp.html) * [Allegro 利用大数据估算缓存效率](https://allegro.tech/2017/01/estimating-the-cache-efficiency-using-big-data.html) * [Zalando 分布式缓存](https://engineering.zalando.com/posts/2018/04/distributed-cache-akka-kubernetes.html) * [S3 分布式缓存ClickHouse](https://clickhouse.com/blog/building-a-distributed-cache-for-s3) * [NetFlix 将应用程序数据从 RAM 缓存到 SSD](https://medium.com/netflix-techblog/evolution-of-application-data-caching-from-ram-to-ssd-a33d6fa7a690) * [Skyscanner 复制缓存的权衡](https://medium.com/@SkyscannerEng/the-tradeoffs-of-a-replicated-cache-b6680c722f58) * [Yext 使用四叉树进行位置缓存](http://engblog.yext.com/post/geolocation-caching) * [Yext 视频元数据缓存Vimeo](https://medium.com/vimeo-engineering-blog/video-metadata-caching-at-vimeo-a54b25f0b304) * [Twitter 使用 Redis 扩展](http://highscalability.com/blog/2014/9/8/how-twitter-uses-redis-to-scale-105tb-ram-39mm-qps-10000-ins.html) * [Slack 使用 Redis 扩展任务队列](https://slack.engineering/scaling-slacks-job-queue-687222e9d100) * [Github 将持久数据移出 Redis](https://githubengineering.com/moving-persistent-data-out-of-redis/) * [在 Redis中存储数亿个简单键值对Instagram](https://engineering.instagram.com/storing-hundreds-of-millions-of-simple-key-value-pairs-in-redis-1091ae80f74c) * [Trivago 的 Redis](http://tech.trivago.com/2017/01/25/learn-redis-the-hard-way-in-production/) * [Deliveroo 的 Redis 存储优化](https://deliveroo.engineering/2017/01/19/optimising-membership-queries.html) * [Wattpad 的 Redis 内存优化](http://engineering.wattpad.com/post/23244724794/store-more-stuff-memory-optimization-in-redis) * [Heroku 的 Redis Fleet](https://blog.heroku.com/rolling-redis-fleet) * [SoundCloud 的远程构建缓存未命中解决方案(两部分)](https://developers.soundcloud.com/blog/gradle-remote-build-cache-misses-part-2) * [评分与评论(两部分) Flipkart](https://blog.flipkart.tech/ratings-reviews-flipkart-part-2-574ab08e75cf) * [eBay 商品预取缓存](https://tech.ebayinc.com/engineering/prefetch-caching-of-ebay-items/) * [Wix 跨区域缓存库](https://www.wix.engineering/post/how-we-built-a-cross-region-caching-library) * [Pinterest 分布式缓存性能和效率提升](https://medium.com/pinterest-engineering/improving-distributed-caching-performance-and-efficiency-at-pinterest-92484b5fe39b) * [Pinterest 微服务缓存的标准化和改进DoorDash](https://doordash.engineering/2023/10/19/how-doordash-standardized-and-improved-microservices-caching/) * [HTTP 缓存和 CDN](https://developer.mozilla.org/en-US/docs/Web/HTTP/Caching) * [Zynga 地理代理:Zynga 如何降低移动游戏延迟](https://www.zynga.com/blogs/engineering/zynga-geo-proxy-reducing-mobile-game-latency) * [康泰纳仕的 Google AMP](https://technology.condenast.com/story/the-why-and-how-of-google-amp-at-conde-nast) * [Deliveroo 托管基础设施 (CDN) 的 A/B 测试](https://deliveroo.engineering/2016/09/19/ab-testing-cdns.html) * [SoundCloud 使用 Kubernetes 实现 HAProxy 面向用户流量](https://developers.soundcloud.com/blog/how-soundcloud-uses-haproxy-with-kubernetes-for-user-facing-traffic) * [Bandaid:Dropbox 的服务代理](https://blogs.dropbox.com/tech/2018/03/meet-bandaid-the-dropbox-service-proxy/) * [Slack 的服务工作者](https://slack.engineering/service-workers-at-slack-our-quest-for-faster-boot-times-and-offline-support-3492cf79c88) * [Spotify 的 CDN 服务](https://labs.spotify.com/2020/02/24/how-spotify-aligned-cdn-services-for-a-lightning-fast-streaming-experience/) * [分布式锁](https://martin.kleppmann.com/2016/02/08/how-to-do-distributed-locking.html) * [Chubby:谷歌松耦合分布式系统的锁服务](https://blog.acolyer.org/2015/02/13/the-chubby-lock-service-for-loosely-coupled-distributed-systems/) * [Uber 的分布式锁](https://www.youtube.com/watch?v=MDuagr729aU) * [GoSquared 使用 Redis 的分布式锁](https://engineering.gosquared.com/distributed-locks-using-redis) * [Twitter 上的 ZooKeeper](https://blog.twitter.com/engineering/en_us/topics/infrastructure/2018/zookeeper-at-twitter.html) * [Chartio 使用分布式锁定消除重复查询](https://chartio.com/blog/eliminating-duplicate-queries-using-distributed-locking/) * [分布式跟踪、追踪和测量](https://www.oreilly.com/ideas/understanding-the-value-of-distributed-tracing) * [Zipkin:Twitter 的分布式系统追踪](https://blog.twitter.com/engineering/en_us/a/2012/distributed-systems-tracing-with-zipkin.html) * [SoundCloud 使用 Kubernetes Pod 元数据改进 Zipkin 追踪](https://developers.soundcloud.com/blog/using-kubernetes-pod-metadata-to-improve-zipkin-traces) * [Canopy:可扩展的分布式追踪与分析Facebook](https://www.infoq.com/presentations/canopy-scalable-tracing-analytics-facebook) * [Pintrace:Pinterest 的分布式追踪](https://medium.com/@Pinterest_Engineering/distributed-tracing-at-pinterest-with-new-open-source-tools-a4f8a5562f6b) * [XCMetrics:Spotify 的一体化 Xcode 构建指标追踪工具](https://engineering.atspotify.com/2021/01/20/introducing-xcmetrics-our-all-in-one-tool-for-tracking-xcode-build-metrics/) * [LinkedIn 的实时分布式追踪](https://engineering.linkedin.com/distributed-service-call-graph/real-time-distributed-tracing-website-performance-and-efficiency) * [Shopify 的大规模跟踪服务基础设施](https://www.usenix.org/conference/srecon17americas/program/presentation/arthorne) * [HelloFresh 的分布式跟踪](https://engineering.hellofresh.com/scaling-hellofresh-distributed-tracing-7b182928247d) * [Pinterest 的分布式跟踪数据分析](https://medium.com/@Pinterest_Engineering/analyzing-distributed-trace-data-6aae58919949) * [Uber 的分布式跟踪](https://eng.uber.com/distributed-tracing/) * [JVM Profiler:Uber 的分布式 JVM 应用程序跟踪](https://eng.uber.com/jvm-profiler/) * [数据检查Dropbox](https://www.usenix.org/conference/srecon17asia/program/presentation/mah) * [Showmax 的分布式系统追踪](https://tech.showmax.com/2016/10/tracing-distributed-systems-at-showmax/) * [Palantir 的企业级 osquery](https://medium.com/@palantir/osquery-across-the-enterprise-3c3c9d13ec55) * [Etsy 的 StatsD](https://codeascraft.com/2011/02/15/measure-anything-measure-everything/) * [分布式调度](https://www.csee.umbc.edu/courses/graduate/CMSC621/fall02/lectures/ch11.pdf) * [PagerDuty 的分布式任务调度(3 部分)](https://www.pagerduty.com/eng/distributed-task-scheduling-3/) * [谷歌的 Cron 构建](https://landing.google.com/sre/sre-book/chapters/distributed-periodic-scheduling/) * [Quora 的分布式 Cron 架构](https://engineering.quora.com/Quoras-Distributed-Cron-Architecture) * [Chronos:Airbnb 的 Cron 替代品](https://medium.com/airbnb-engineering/chronos-a-replacement-for-cron-f05d7d986a9d) * [Nextdoor 的调度器](https://engblog.nextdoor.com/we-don-t-run-cron-jobs-at-nextdoor-6f7f9cc62040) * [Peloton:Uber 面向多样化集群工作负载的统一资源调度器](https://eng.uber.com/peloton/) * [Fenzo:Netflix 面向 Apache Mesos 框架的 OSS 调度器](https://medium.com/netflix-techblog/fenzo-oss-scheduler-for-apache-mesos-frameworks-5c340e77e543) * [Airflow - 工作流编排](https://airflow.apache.org/) * [Airbnb 的 Airflow](https://medium.com/airbnb-engineering/airflow-a-workflow-management-platform-46318b977fd8) * [Airflow Adyen 的 Airflow 案例](https://www.adyen.com/knowledge-hub/apache-airflow-at-adyen) * [Pandora 的 Airflow 案例](https://engineering.pandora.com/apache-airflow-at-pandora-1d7a844d68ee) * [Robinhood 的 Airflow 案例](https://medium.com/robinhood-engineering/why-robinhood-uses-airflow-aed13a9a90c8) * [Lyft 的 Airflow 案例](https://eng.lyft.com/running-apache-airflow-at-lyft-6e53bb8fccff) * [Drivy 的 Airflow 案例](https://drivy.engineering/airflow-architecture/) * [Grab 的 Airflow 案例](https://engineering.grab.com/experimentation-platform-data-pipeline) * [ Adobe](https://medium.com/adobetech/adobe-experience-platform-orchestration-service-with-apache-airflow-952203723c0b) * [沃尔玛 Airflow 作业运行审计](https://medium.com/walmartlabs/auditing-airflow-batch-jobs-73b45100045) * [MaaT:阿里巴巴基于 DAG 的分布式任务调度器](https://hackernoon.com/meet-maat-alibabas-dag-based-distributed-task-scheduler-7c9cf0c83438) * [boundary-layer:Etsy 的声明式 Airflow 工作流](https://www.etsy.com/codeascraft/boundary-layer-declarative-airflow-workflows) * [分布式监控和报警](https://www.oreilly.com/ideas/monitoring-distributed-systems) * [Unicorn:eBay 的修复系统](https://www.ebayinc.com/stories/blogs/tech/unicorn-rheos-remediation-center/) * [M3:Uber 的指标和监控平台](https://eng.uber.com/optimizing-m3/) * [Athena:Dropbox 的自动构建健康管理系统](https://blogs.dropbox.com/tech/2019/05/athena-our-automated-build-health-management-system/) * [Vortex:Dropbox 的服务器应用程序监控](https://blogs.dropbox.com/tech/2019/11/monitoring-server-applications-with-vortex/) * [Nuage:Dropbox 的云管理服务LinkedIn](https://engineering.linkedin.com/blog/2019/solving-manageability-challenges-with-nuage) * [Telltale:Netflix 的应用程序监控](https://netflixtechblog.com/telltale-netflix-application-monitoring-simplified-5c08bfa780ba) * [ThirdEye:LinkedIn 的监控平台](https://engineering.linkedin.com/blog/2019/06/smart-alerts-in-thirdeye--linkedins-real-time-monitoring-platfor) * [Perriskop:SoundCloud 的异常监控服务](https://developers.soundcloud.com/blog/periskop-exception-monitoring-service) * [Securitybot:分布式警报机器人Dropbox](https://blogs.dropbox.com/tech/2017/02/meet-securitybot-open-sourcing-automated-security-at-scale/) * [阿里巴巴的监控系统](https://www.usenix.org/conference/srecon18asia/presentation/xinchi) * [Dailymotion 的真实用户监控](https://medium.com/dailymotion/real-user-monitoring-1948375f8be5) * [Uber 的警报生态系统](https://eng.uber.com/observability-at-scale/) * [Airbnb 的警报框架](https://medium.com/airbnb-engineering/alerting-framework-at-airbnb-35ba48df894f) * [基于服务级别目标 (SLO) 的警报SoundCloud](https://developers.soundcloud.com/blog/alerting-on-slos) * [Uber 基于作业的可观测性异常检测预测工作流程](https://eng.uber.com/observability-anomaly-detection/) * [HackerEarth 使用 Graphite 和 Cabot 构建的监控和警报系统](http://engineering.hackerearth.com/2017/03/21/monitoring-and-alert-system-using-graphite-and-cabot/) * [Twitter 的可观测性(两部分)](https://blog.twitter.com/engineering/en_us/a/2016/observability-at-twitter-technical-overview-part-ii.html) * [Twitter 的分布式安全警报Slack](https://slack.engineering/distributed-security-alerting-c89414c992d6) * [彭博实时新闻预警](https://www.infoq.com/presentations/news-alerting-bloomberg) * [领英数据管道监控系统](https://engineering.linkedin.com/blog/2019/an-inside-look-at-linkedins-data-pipeline-monitoring-system-) * [Picnic 的监控与可观察性](https://blog.picnic.nl/monitoring-and-observability-at-picnic-684cefd845c4) * [分布式安全](https://msdn.microsoft.com/en-us/library/cc767123.aspx) * [Dropbox 的大规模安全方法](https://blogs.dropbox.com/tech/2018/02/security-at-scale-the-dropbox-approach/) * [Aardvark 和 Repokid:Netflix 分布式高速开发的 AWS 最小权限](https://medium.com/netflix-techblog/introducing-aardvark-and-repokid-53b081bf3a7e) * [LISA:LinkedIn 的分布式防火墙](https://www.slideshare.net/MikeSvoboda/2017-lisa-linkedins-distributed-firewall-dfw) * [在云端存储比特币的安全基础设施Coinbase](https://engineering.coinbase.com/how-coinbase-builds-secure-infrastructure-to-store-bitcoin-in-the-cloud-30a6504e40ba) * [BinaryAlert:Airbnb 的实时无服务器恶意软件检测](https://medium.com/airbnb-engineering/binaryalert-real-time-serverless-malware-detection-ca44370c1b90) * [Segment 的可扩展 IAM 架构,用于保护对 100 个 AWS 账户的访问](https://segment.com/blog/secure-access-to-100-aws-accounts/) * [Indeed 的 OAuth 审计工具箱](http://engineering.indeedblog.com/blog/2018/04/oaudit-toolbox/) * [Active Directory 密码黑名单Yelp](https://engineeringblog.yelp.com/2018/04/ad-password-blacklisting.html) * [Slack 的大规模系统调用审计](https://slack.engineering/syscall-auditing-at-scale-e6a3ca8ac1b8) * [Athenz:雅虎的细粒度、基于角色的访问控制](https://yahooeng.tumblr.com/post/160481899076/open-sourcing-athenz-fine-grained-role-based) * [Dropbox 支持 WebAuthn 安全登录](https://blogs.dropbox.com/tech/2018/05/introducing-webauthn-support-for-secure-dropbox-sign-in/) * [安全开发生命周期Slack](https://slack.engineering/moving-fast-and-securing-things-540e6c5ae58a) * [Kinvolk 的非特权容器构建](https://kinvolk.io/blog/2018/04/towards-unprivileged-container-builds/) * [Diffy:Netflix 云端数字取证差异引擎](https://medium.com/netflix-techblog/netflix-sirt-releases-diffy-a-differencing-engine-for-digital-forensics-in-the-cloud-37b71abd2698) * [在 AWS 中检测凭证泄露Netflix](https://medium.com/netflix-techblog/netflix-cloud-security-detecting-credential-compromise-in-aws-9493d6fd373a) * [Spotify 的可扩展用户隐私](https://labs.spotify.com/2018/09/18/scalable-user-privacy/) * [AVA:Indeed 的 Web 应用程序审计](https://engineering.indeedblog.com/blog/2018/09/application-scanning/) * [TTL 即服务:Yelp 自动撤销过期权限](https://engineeringblog.yelp.com/2018/11/ttl-as-a-service.html) * [企业密钥管理Slack](https://slack.engineering/engineering-dive-into-slack-enterprise-key-management-1fce471b178c) * [Twitch 的可扩展性和身份验证](https://blog.twitch.tv/en/2019/03/15/how-twitch-addresses-scalability-and-authentication/) * [Netflix 的边缘身份验证和令牌无关身份传播](https://netflixtechblog.com/edge-authentication-and-token-agnostic-identity-propagation-514e47e0b602) * [Palantir 使用 Cilium 强化 Kubernetes 基础设施](https://blog.palantir.com/hardening-palantirs-kubernetes-infrastructure-with-cilium-1c40d4c7ef0) * [改进 Web Lyft 通过自动化进行漏洞管理](https://eng.lyft.com/improving-web-vulnerability-management-through-automation-2631570d8415) * [在 Drobbox 同步密码负载时出现时钟偏差](https://dropbox.tech/application/dropbox-passwords-clock-skew-payload-sync-merge) * [分布式消息传递、队列和事件流](https://arxiv.org/pdf/1704.00411.pdf) * [Cape:Dropbox 的事件流处理框架](https://blogs.dropbox.com/tech/2017/05/introducing-cape/) * [Brooklin:LinkedIn 的近实时数据流分布式服务](https://engineering.linkedin.com/blog/2019/brooklin-open-source) * [Samza:LinkedIn 用于延迟洞察的流处理系统](https://engineering.linkedin.com/blog/2018/04/samza-aeon--latency-insights-for-asynchronous-one-way-flows) * [Bullet:LinkedIn 的前瞻性流数据查询引擎Yahoo](https://yahooeng.tumblr.com/post/161855616651/open-sourcing-bullet-yahoos-forward-looking) * [EventHorizon:Etsy 事件流监控工具](https://codeascraft.com/2018/05/29/the-eventhorizon-saga/) * [Qmessage:Quora 分布式异步任务队列](https://engineering.quora.com/Qmessage-Handling-Billions-of-Tasks-Per-Day) * [Cherami:Uber 用于传输异步任务的消息队列系统](https://eng.uber.com/cherami/) * [Dynein:Uber 分布式延迟作业队列系统Airbnb](https://medium.com/airbnb-engineering/dynein-building-a-distributed-delayed-job-queueing-system-93ab10f05f99) * [Timestone:Netflix 的非并行化工作负载队列系统](https://netflixtechblog.com/timestone-netflixs-high-throughput-low-latency-priority-queueing-system-with-built-in-support-1abf249ba95f) * [Riot Games 的消息服务](https://engineering.riotgames.com/news/riot-messaging-service) * [Dropbox 的消息系统模型](https://dropbox.tech/infrastructure/infrastructure-messaging-system-model-async-platform-evolution) * [使用事件日志调试生产环境Zillow](https://www.zillow.com/engineering/debugging-production-event-logging/) * [Netflix 的跨平台应用内消息编排服务](https://medium.com/netflix-techblog/building-a-cross-platform-in-app-messaging-orchestration-service-86ba614f92d8) * [Netflix 的视频网关守卫](https://medium.com/netflix-techblog/re-architecting-the-video-gatekeeper-f7b0ac2f6b00) * [Netflix 为数百万台设备扩展推送消息服务](https://www.infoq.com/presentations/neflix-push-messaging-scale) * [使用 RabbitMQ 延迟异步消息处理Indeed](http://engineering.indeedblog.com/blog/2017/06/delaying-messages/) * [雅虎流式计算引擎基准测试](https://yahooeng.tumblr.com/post/135321837876/benchmarking-streaming-computation-engines-at) * [Deliveroo 使用 Protobuf Schema 验证提升流数据质量](https://deliveroo.engineering/2019/02/05/improving-stream-data-quality-with-protobuf-schema-validation.html) * [Medium 扩展电子邮件基础设施](https://medium.engineering/scaling-email-infrastructure-for-medium-digest-254223c883b8) * [实时消息传递Slack](https://slack.engineering/real-time-messaging/) * [Nike 的事件流数据库](https://medium.com/nikeengineering/moving-faster-with-aws-by-creating-an-event-stream-database-dedec8ca3eeb) * [Udemy 的事件追踪系统](https://medium.com/udemy-engineering/designing-the-new-event-tracking-system-at-udemy-a45e502216fd) * [事件驱动的消息传递](https://martinfowler.com/articles/201701-event-driven.html) * [阿里巴巴的领域驱动设计](https://medium.com/swlh/creating-coding-excellence-with-domain-driven-design-88f73d2232c3) * [Weebly 的领域驱动设计](https://medium.com/weebly-engineering/how-to-organize-your-monolith-before-breaking-it-into-services-69cbdb9248b0) * [Moonpig 的领域驱动设计](https://engineering.moonpig.com/development/modelling-for-domain-driven-design) * [Netflix 下载的事件溯源扩展](https://www.infoq.com/presentations/netflix-scale-event-sourcing) * [Weebly 的事件溯源扩展Jet.com](https://medium.com/@eulerfx/scaling-event-sourcing-at-jet-9c873cac33b8) * [eBay 的事件溯源(两部分)](https://www.ebayinc.com/stories/blogs/tech/event-sourcing-in-action-with-ebays-continuous-delivery-team/) * [现在免费使用事件溯源](https://medium.com/inside-freenow/event-sourcing-an-evolutionary-perspective-31e7387aa6f1) * [Brainly 使用事件溯源和 CQRS 模式实现可扩展内容推送](https://medium.com/engineering-brainly/scalable-content-feed-using-event-sourcing-and-cqrs-patterns-e09df98bf977) * [发布-订阅消息传递](https://aws.amazon.com/pub-sub-messaging/) * [Pulsar:雅虎大规模发布/订阅消息传递](https://yahooeng.tumblr.com/post/150078336821/open-sourcing-pulsar-pub-sub-messaging-at-scale) * [Wormhole:Facebook 的发布/订阅系统](https://code.facebook.com/posts/188966771280871/wormhole-pub-sub-system-moving-data-through-space-and-time/) * [MemQ:Pinterest 的云原生发布/订阅系统](https://medium.com/pinterest-engineering/memq-an-efficient-scalable-cloud-native-pubsub-system-4402695dd4e7) * [微服务中的发布/订阅系统Netflix](https://medium.com/netflix-techblog/how-netflix-microservices-tackle-dataset-pub-sub-4a068adcc9a) * [Kafka - 消息代理](https://martin.kleppmann.com/papers/kafka-debull15.pdf) * [Kafka 在 LinkedIn 的应用](https://engineering.linkedin.com/kafka/running-kafka-scale) * [Kafka 在 Pinterest 的应用](https://medium.com/pinterest-engineering/how-pinterest-runs-kafka-at-scale-ff9c6f735be) * [Kafka 在 Trello 的应用](https://tech.trello.com/why-we-chose-kafka/) * [Kafka 在 Salesforce 的应用](https://engineering.salesforce.com/how-apache-kafka-inspired-our-platform-events-architecture-2f351fe4cf63) * [Kafka 在纽约纽约时报](https://open.nytimes.com/publishing-with-apache-kafka-at-the-new-york-times-7f0e3b7d2077) * [Kafka 在 Yelp 中的应用](https://engineeringblog.yelp.com/2016/07/billions-of-messages-a-day-yelps-real-time-data-pipeline.html) * [Kafka 在 Criteo 中的应用](https://medium.com/criteo-labs/upgrading-kafka-on-a-large-infra-3ee99f56e970) * [Shopify 在 Kubernetes 上运行 Kafka](https://shopifyengineering.myshopify.com/blogs/engineering/running-apache-kafka-on-kubernetes-at-shopify) * [Kafka 在 PaaSTA 上的应用:Yelp 在 Kubernetes 上运行 Kafka (2)部分)](https://engineeringblog.yelp.com/2022/03/kafka-on-paasta-part-two.html) * [Yelp 零停机迁移 Kafka 的 Zookeeper](https://engineeringblog.yelp.com/2019/01/migrating-kafkas-zookeeper-with-no-downtime.html) * [Uber 使用 Kafka 进行重新处理和死信队列处理](https://eng.uber.com/reliable-reprocessing/) * [Chaperone:Uber 端到端 Kafka 审计](https://eng.uber.com/chaperone/) * [Dropbox 基础设施中 Kafka 吞吐量限制的查找](https://blogs.dropbox.com/tech/2019/01/finding-kafkas-throughput-limit-in-dropbox-infrastructure/) * [成本编排沃尔玛](https://medium.com/walmartlabs/cost-orchestration-at-walmart-f34918af67c4) * [Hulu 使用 InfluxDB 和 Kafka 扩展至每秒超过 100 万个指标](https://medium.com/hulu-tech-blog/how-hulu-uses-influxdb-and-kafka-to-scale-to-over-1-million-metrics-a-second-1721476aaff5) * [PayPal 扩展 Kafka 以支持数据增长](https://medium.com/paypal-tech/scaling-kafka-to-support-paypals-data-growth-a0b4da420fab) * [流数据重复数据删除](https://en.wikipedia.org/wiki/Data_deduplication) * [Exactly-once 语义, Kafka](https://www.confluent.io/blog/exactly-once-semantics-are-possible-heres-how-apache-kafka-does-it/) * [Tapjoy 的实时重复数据删除](http://eng.tapjoy.com/blog-list/real-time-deduping-at-scale) * [Segment 的重复数据删除](https://segment.com/blog/exactly-once-delivery/) * [Mail.Ru 的重复数据删除](https://medium.com/@andrewsumin/efficient-storage-how-we-went-down-from-50-pb-to-32-pb-99f9c61bf6b4) * [PB 级重复数据删除Mixpanel](https://medium.com/mixpaneleng/petabyte-scale-data-deduplication-mixpanel-engineering-e808c70c99f8) * [分布式日志记录](https://blog.codinghorror.com/the-problem-with-logging/) * [LinkedIn 的日志记录](https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying) * [Pinterest 的可扩展且可靠的日志提取](https://medium.com/@Pinterest_Engineering/scalable-and-reliable-data-ingestion-at-pinterest-b921c2ee8754) * [Twitter 的高性能复制日志服务](https://blog.twitter.com/engineering/en_us/topics/infrastructure/2015/building-distributedlog-twitter-s-high-performance-replicated-log-servic.html) * [CERN 的 Spark 日志服务Accelerator](https://databricks.com/blog/2017/12/14/the-architecture-of-the-next-cern-accelerator-logging-service.html) * [Quora 的日志记录和聚合](https://engineering.quora.com/Logging-and-Aggregation-at-Quora) * [Badoo 的守护进程日志收集和分析](https://badoo.com/techblog/blog/2016/06/06/collection-and-analysis-of-daemon-logs-at-badoo/) * [Palantir 使用静态代码分析进行日志解析](https://medium.com/palantir/using-static-code-analysis-to-improve-log-parsing-18f0d1843965) * [Centralized Application Logging at eBay](https://tech.ebayinc.com/engineering/low-latency-and-high-throughput-cal-ingress/) * [Netflix 以超大规模丰富 VPC 流日志,提供网络洞察](https://netflixtechblog.com/hyper-scale-vpc-flow-logs-enrichment-to-provide-network-insight-e5f1db02910d) * [BookKeeper:雅虎的分布式日志存储](https://yahooeng.tumblr.com/post/109908973316/bookkeeper-yahoos-distributed-log-storage-is) * [LogDevice:Facebook 的分布式日志数据存储](https://code.facebook.com/posts/357056558062811/logdevice-a-distributed-data-store-for-logs/) * [LogFeeder:日志收集系统Yelp 上的 DBLog 框架](https://engineeringblog.yelp.com/2018/03/introducing-logfeeder.html) * [DBLog:Netflix 的通用变更数据捕获框架](https://medium.com/netflix-techblog/dblog-a-generic-change-data-capture-framework-69351fb9099b) * [分布式搜索](http://nwds.cs.washington.edu/files/nwds/pdf/Distributed-WR.pdf) * [Instagram 搜索架构](https://instagram-engineering.com/search-architecture-eeb34a936d3a) * [eBay 搜索架构](http://www.cs.otago.ac.nz/homepages/andrew/papers/2017-8.pdf) * [Box 搜索架构](https://medium.com/box-tech-blog/scaling-box-search-using-lumos-22d9e0cb4175) * [Coupang 搜索发现索引平台](https://medium.com/coupang-tech/the-evolution-of-search-discovery-indexing-platform-fa43e41305f9) * [通用搜索系统Pinterest](https://medium.com/pinterest-engineering/building-a-universal-search-system-for-pinterest-e4cb03a898d4) * [eBay 将搜索引擎效率提升超过 25%](https://www.ebayinc.com/stories/blogs/tech/making-e-commerce-search-faster/) * [Palantir 使用 Lucene 对遥测日志进行索引和查询](https://medium.com/palantir/indexing-and-querying-telemetry-logs-with-lucene-234c5ce3e5f3) * [TripAdvisor 的查询理解](https://www.tripadvisor.com/engineering/query-understanding-at-tripadvisor/) * [LinkedIn 的搜索联邦架构(2018)](https://engineering.linkedin.com/blog/2018/03/search-federation-architecture-at-linkedin) * [Slack 搜索](https://slack.engineering/search-at-slack-431f8c80619e) * [DoorDash 的搜索引擎](https://careersatdoordash.com/blog/introducing-doordashs-in-house-search-engine/) * [Twitter 搜索的稳定性和可扩展性](https://blog.twitter.com/engineering/en_us/topics/infrastructure/2022/stability-and-scalability-for-search) * [Twitter 的搜索服务 (2014)](https://blog.twitter.com/engineering/en_us/a/2014/building-a-complete-tweet-index.html) * [自动完成搜索(2 部分) Traveloka](https://medium.com/traveloka-engineering/high-quality-autocomplete-search-part-2-d5b15bb0dadf) * [Canva 的数据驱动自动更正系统](https://product.canva.com/building-a-data-driven-autocorrection-system/) * [Flipkart 的印度语音搜索调整](https://blog.flipkart.tech/adapting-search-to-indian-phonetics-cdbe65259686) * [Nautilus:Dropbox 的搜索引擎](https://blogs.dropbox.com/tech/2018/09/architecture-of-nautilus-the-new-dropbox-search-engine/) * [Galene:LinkedIn 的搜索架构](https://engineering.linkedin.com/search/did-you-mean-galene) * [Manas:高性能定制搜索系统在 Pinterest](https://medium.com/@Pinterest_Engineering/manas-a-high-performing-customized-search-system-cf189f6ca40f) * [Sherlock:Flipkart 的近实时搜索索引](https://blog.flipkart.tech/sherlock-near-real-time-search-indexing-95519783859d) * [Nebula:Airbnb 用于构建搜索后端的存储平台](https://medium.com/airbnb-engineering/nebula-as-a-storage-platform-to-build-airbnbs-search-backends-ecc577b05f06) * [ELK(Elasticsearch、Logstash、Kibana)堆栈](https://logz.io/blog/15-tech-companies-chose-elk-stack/) * [Uber 使用 ELK 进行实时预测](https://eng.uber.com/elk/) * [Envato 构建可扩展 ELK 技术栈](https://webuild.envato.com/blog/building-a-scalable-elk-stack/) * [Robinhood 使用 ELK](https://robinhood.engineering/taming-elk-4e1349f077c3) * [Uber 扩展 Elasticsearch 集群](https://www.infoq.com/presentations/uber-elasticsearch-clusters?utm_source=presentations_about_Case_Study&utm_medium=link&utm_campaign=Case_Study) * [eBay Elasticsearch 性能调优实践](https://www.ebayinc.com/stories/blogs/tech/elasticsearch-performance-tuning-practice-at-ebay/) * [使用 Elasticsearch 提升性能Tinder 插件(共 2 部分)](https://medium.com/tinder-engineering/how-we-improved-our-performance-using-elasticsearch-plugins-part-2-b051da2ee85b) * [Kickstarter 众筹中的 Elasticsearch](https://kickstarter.engineering/elasticsearch-at-kickstarter-db3c487887fc) * [Trivago 使用 Logstash 和 Google Protocol Buffers 进行日志解析](https://tech.trivago.com/2016/01/19/logstash_protobuf_codec/) * [Yelp 使用 Data Pipeline 和 Elasticsearch 进行快速订单搜索](https://engineeringblog.yelp.com/2018/06/fast-order-search.html) * [将核心业务搜索迁移至 Elasticsearch Yelp](https://engineeringblog.yelp.com/2017/06/moving-yelps-core-business-search-to-elasticsearch.html) * [Vinted 的 Elasticsearch 分片技术](http://engineering.vinted.com/2017/06/05/sharding-out-elasticsearch/) * [Wattpad 的 Elasticsearch 自排名搜索](http://engineering.wattpad.com/post/146216619727/self-ranking-search-with-elasticsearch-at-wattpad) * [Vulcanizer:Github 上用于操作 Elasticsearch 的库](https://github.blog/2019-03-05-vulcanizer-a-library-for-operating-elasticsearch/) * [分布式存储](http://highscalability.com/blog/2011/11/1/finding-the-right-data-solution-for-your-application-in-the.html) * [内存存储](https://medium.com/@denisanikin/what-an-in-memory-database-is-and-how-it-persists-data-efficiently-f43868cff4c1) * [MemSQL 架构 - 快速 (MVCC、InMem、LockFree、CodeGen) 和熟悉 (SQL)](http://highscalability.com/blog/2012/8/14/memsql-architecture-the-fast-mvcc-inmem-lockfree-codegen-and.html) * [Quora 的 Memcached 效率优化](https://engineering.quora.com/Optimizing-Memcached-Efficiency) * [基于 Cisco UCS 的 MemSQL 实时数据仓库](https://blogs.cisco.com/datacenter/memsql) * [Tapjoy 迁移至 MemSQL](http://eng.tapjoy.com/blog-list/moving-to-memsql) * [迪士尼使用 MemSQL 和 Kinesis 实现实时洞察](https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/68131) * [Pandora 使用 MemSQL 在仪表板中查询数千亿行数据](https://engineering.pandora.com/using-memsql-at-pandora-79a86cb09b57) * [对象存储](http://www.datacenterknowledge.com/archives/2013/10/04/object-storage-the-future-of-scale-out) * [Uber 的 HDFS 扩展方案](https://eng.uber.com/scaling-hdfs/) * [Databricks 选择 S3 而非 HDFS 的原因](https://databricks.com/blog/2017/05/31/top-5-reasons-for-choosing-s3-over-hdfs.html) * [Quantcast 的 Amazon S3 文件系统](https://www.quantcast.com/blog/quantcast-file-system-on-amazon-s3/) * [Trivago 使用 S3 版本控制进行大规模图像恢复](https://tech.trivago.com/2018/09/03/efficient-image-recovery-at-scale-using-amazon-s3-versioning/) * [云对象存储Yahoo](https://yahooeng.tumblr.com/post/116391291701/yahoo-cloud-object-store-object-storage-at) * [Ambry:LinkedIn 的分布式不可变对象存储](https://www.usenix.org/conference/srecon17americas/program/presentation/shenoy) * [Dynamometer:LinkedIn 在最小硬件上以最大保真度对 HDFS 进行规模测试](https://engineering.linkedin.com/blog/2018/02/dynamometer--scale-testing-hdfs-on-minimal-hardware-with-maximum) * [Hammerspace:Airbnb 的持久化、并发、堆外存储](https://medium.com/airbnb-engineering/hammerspace-persistent-concurrent-off-heap-storage-3db39bb04472) * [MezzFS:在 Netflix 的媒体处理平台中挂载对象存储](https://medium.com/netflix-techblog/mezzfs-mounting-object-storage-in-netflixs-media-processing-platform-cda01c446ba) * [Magic Pocket:Dropbox 内部多 EB 存储系统](https://blogs.dropbox.com/tech/2016/05/inside-the-magic-pocket/) * [关系数据库](https://www.mysql.com/products/cluster/scalability.html) * [Uber 的 MySQL](https://www.uber.com/en-SG/blog/mysql-at-uber/) * [Pinterest 的 MySQL](https://medium.com/@Pinterest_Engineering/learn-to-stop-using-shiny-new-things-and-love-mysql-3e1613c2ce14) * [Twitch 的 PostgreSQL](https://blog.twitch.tv/en/2016/10/11/how-twitch-uses-postgresql-c34aa9e56f58) * [基于 MySQL 的财务报告系统在 Uber 的扩展](https://www.uber.com/en-SG/blog/mysql-at-uber/) * [Pinterest 的 MySQL](https://medium.com/@Pinterest_Engineering/learn-to-stop-using-shiny-new-things-and-love-mysql-3e1613c2ce14) * [Twitch 的 PostgreSQL](https://blog.twitch.tv/en/2016/10/11/how-twitch-uses-postgresql-c34aa9e56f58) * [Twitch 的 MySQL 财务报告系统扩展](https://medium.com/airbnb-engineering/tracking-the-money-scaling-financial-reporting-at-airbnb-6d742b80f040) * [Wix 的 MySQL 扩展](https://www.wix.engineering/post/scaling-to-100m-mysql-is-a-better-nosql) * [Meta 的 MySQL Raft 构建和部署](https://engineering.fb.com/2023/05/16/data-infrastructure/mysql-raft-meta/) * [Airbnb 的 MaxScale (MySQL) 数据库代理](https://medium.com/airbnb-engineering/unlocking-horizontal-scalability-in-our-web-serving-tier-d907449cdbcf) * [从 Postgres 切换到 MySQL Uber](https://www.uber.com/en-NL/blog/postgres-to-mysql-migration/) * [Instagram 使用 Postgres 应对增长](https://engineering.instagram.com/handling-growth-with-postgres-5-tips-from-instagram-d5d7e7ffdfcb) * [TransferWise 扩展分析数据库 (Postgres)](http://tech.transferwise.com/scaling-our-analytics-database/) * [Adyen 更新 50 TB 的 PostgreSQL 数据库](https://medium.com/adyen/updating-a-50-terabyte-postgresql-database-f64384b799e7) * [Adyen 扩展数据库访问以应对每天数千亿次查询PayPal](https://medium.com/paypal-engineering/scaling-database-access-for-100s-of-billions-of-queries-per-day-paypal-introducing-hera-e192adacda54) * [Yelp 如何最大程度减少 MySQL 读写宕机时间](https://engineeringblog.yelp.com/2020/11/minimizing-read-write-mysql-downtime.html) * [Facebook 如何将 MySQL 从 5.6 迁移到 8.0](https://engineering.fb.com/2021/07/22/data-infrastructure/mysql/) * [Quora 如何从 HBase 迁移到 MyRocks](https://quoraengineering.quora.com/Migration-from-HBase-to-MyRocks-at-Quora) * [Booking.com 的 MySQL 并行复制(4 部分)](https://medium.com/booking-com-infrastructure/evaluating-mysql-parallel-replication-part-4-annex-under-the-hood-eb456cf8b2fb) * [Github 上缓解 MySQL 复制延迟并降低读取负载](https://githubengineering.com/mitigating-replication-lag-and-reducing-read-load-with-freno/) * [Shopify 数据库副本的读取一致性](https://shopify.engineering/read-consistency-database-replicas) * [黑盒审计:Yelp 验证 MySQL 和 Redshift 之间的端到端复制完整性](https://engineeringblog.yelp.com/2018/04/black-box-auditing.html) * [对主 MySQL 数据库进行分区Airbnb](https://medium.com/airbnb-engineering/how-we-partitioned-airbnb-s-main-database-in-two-weeks-55f7e006ff21) * [Herb:Uber 用于无模式数据存储的多数据中心复制引擎](https://eng.uber.com/herb-datacenter-replication/) * [分片](https://quabase.sei.cmu.edu/mediawiki/index.php/Shard_data_set_across_multiple_servers_(基于范围)) * [Pinterest 的 MySQL 分片](https://medium.com/@Pinterest_Engineering/sharding-pinterest-how-we-scaled-our-mysql-fleet-3f341e96ca6f) * [Pinterest 的 MySQL 分片Twilio](https://www.twilio.com/engineering/2014/06/26/how-we-replaced-our-data-pipeline-with-zero-downtime) * [Square 的 MySQL 分片](https://medium.com/square-corner-blog/sharding-cash-10280fa3ef3b) * [Quora 的 MySQL 分片](https://www.quora.com/q/quoraengineering/MySQL-sharding-at-Quora) * [Uber 的无模式数据存储分片层](https://eng.uber.com/schemaless-rewrite/) * [Instagram 的分片和 ID](https://instagram-engineering.com/sharding-ids-at-instagram-1cf5a71e5a5c) * [Postgres 分片Notion](https://www.notion.so/blog/sharding-postgres-at-notion) * [Solr:提升 Box 批量索引性能](https://blog.box.com/blog/solr-improving-performance-batch-indexing/) * [Tinder 的地理分片建议(共 3 部分)](https://medium.com/tinder-engineering/geosharded-recommendations-part-3-consistency-2d2cb2f0594b) * [Facebook 使用 Shard Manager 扩展服务](https://engineering.fb.com/production-engineering/scaling-services-with-shard-manager/) * [Presto 分布式 SQL 查询引擎](https://research.fb.com/wp-content/uploads/2019/03/Presto-SQL-on-Everything.pdf?) * [Pinterest 的 Presto](https://medium.com/@Pinterest_Engineering/presto-at-pinterest-a8bda7515e52) * [Lyft 的 Presto 基础设施](https://eng.lyft.com/presto-infrastructure-at-lyft-b10adb9db01) * [Grab 的 Presto](https://engineering.grab.com/scaling-like-a-boss-with-presto) * [Uber 使用 Presto 和 Apache Parquet 进行工程数据分析](https://eng.uber.com/presto/) * [数据整理Slack](https://slack.engineering/data-wrangling-at-slack-f2e0ff633b69) * [Netflix 在 AWS 上的大数据平台中使用 Presto](https://medium.com/netflix-techblog/using-presto-in-our-big-data-platform-on-aws-938035909fd4) * [Eventbrite 上的 Presto 自动伸缩](https://www.eventbrite.com/engineering/big-data-workloads-presto-auto-scaling/) * [Uber 使用 Alluxio 本地缓存加速 Presto](https://www.uber.com/en-MY/blog/speed-up-presto-with-alluxio-local-cache/) * [NoSQL 数据库](https://www.thoughtworks.com/insights/blog/nosql-databases-overview) * [键值对数据库](http://www.cs.ucsb.edu/~agrawal/fall2009/dynamo.pdf) * [DynamoDB 在 Nike 的应用](https://medium.com/nikeengineering/becoming-a-nimble-giant-how-dynamo-db-serves-nike-at-scale-4cc375dbb18e) * [DynamoDB 在 Segment 的应用](https://segment.com/blog/the-million-dollar-eng-problem/) * [DynamoDB 在 Mapbox 的应用](https://blog.mapbox.com/scaling-mapbox-infrastructure-with-dynamodb-streams-d53eabc5e972) * [曼哈顿:分布式键值数据库Twitter](https://blog.twitter.com/engineering/en_us/a/2014/manhattan-our-real-time-multi-tenant-distributed-database-for-twitter-scale.html) * [Sherpa:雅虎的分布式 NoSQL 键值存储](https://yahooeng.tumblr.com/post/120730204806/sherpa-scales-new-heights) * [HaloDB:雅虎的嵌入式键值存储引擎](https://yahooeng.tumblr.com/post/178262468576/introducing-halodb-a-fast-embedded-key-value) * [MPH:Indeed 的快速紧凑不可变键值存储](http://engineering.indeedblog.com/blog/2018/02/indeed-mph/) * [Venice:分布式Linkedin 上的键值数据库](https://engineering.linkedin.com/blog/2017/02/building-venice-with-apache-helix) * [列式数据库](https://aws.amazon.com/nosql/columnar/) * [Cassandra](http://www.cs.cornell.edu/projects/ladis2009/papers/lakshman-ladis2009.pdf) * [Instagram 上的 Cassandra](https://www.slideshare.net/DataStax/cassandra-at-instagram-2016) * [沃尔玛使用 Cassandra 存储图片](https://medium.com/walmartlabs/building-object-store-storing-images-in-cassandra-walmart-scale-a6b9c02af593) * [Discord 上使用 Cassandra 存储消息](https://blog.discordapp.com/how-discord-stores-billions-of-messages-7fa6ec7ee4c7) * [扩展 Cassandra 集群沃尔玛](https://medium.com/walmartlabs/avoid-pitfalls-in-scaling-your-cassandra-cluster-lessons-and-remedies-a71ca01f8c04) * [Yelp 使用 Cassandra 扩展广告分析](https://engineeringblog.yelp.com/2016/08/how-we-scaled-our-ad-analytics-with-cassandra.html) * [Dream11 使用 Spark 和 Cassandra 扩展至 1 亿次以上读写操作](https://medium.com/dream11-tech-blog/leaderboard-dream11-4efc6f93c23e) * [Zomato 将食物信息流从 Redis 迁移到 Cassandra](https://www.zomato.com/blog/how-we-moved-our-food-feed-from-redis-to-cassandra) * [Netflix 在 AWS 上对 Cassandra 可扩展性进行基准测试](https://medium.com/netflix-techblog/benchmarking-cassandra-scalability-on-aws-over-a-million-writes-per-second-39f45f066c9e) * [Intuit QuickBooks 使用 Cassandra 进行大规模服务分解](https://quickbooks-engineering.intuit.com/service-decomposition-at-scale-70405ac2f637) * [SoundCloud 使用 Cassandra 保持计数同步](https://developers.soundcloud.com/blog/keeping-counts-in-sync) * [Cassandra 驱动程序配置,用于提升性能和负载均衡Glassdoor](https://medium.com/glassdoor-engineering/cassandra-driver-configuration-for-improved-performance-and-load-balancing-1b0106ce12bb) * [cstar:Spotify 的 Cassandra 编排工具](https://labs.spotify.com/2018/09/04/introducing-cstar-the-spotify-cassandra-orchestration-tool-now-open-source/) * [HBase](https://hbase.apache.org/) * [Salesforce 的 HBase](https://engineering.salesforce.com/investing-in-big-data-apache-hbase-b9d98661a66b) * [Facebook 中的 HBase消息](https://www.facebook.com/notes/facebook-engineering/the-underlying-technology-of-messages/454991608919/) * [HBase 在 Imgur 通知中的应用](https://blog.imgur.com/2015/09/15/tech-tuesday-imgur-notifications-from-mysql-to-hbase/) * [Pinterest 提升 HBase 备份效率](https://medium.com/@Pinterest_Engineering/improving-hbase-backup-efficiency-at-pinterest-86159da4b954) * [小米的 HBase](https://www.slideshare.net/HBaseCon/hbase-practice-at-xiaomi) * [Redshift](https://www.allthingsdistributed.com/2018/11/amazon-redshift-performance-optimization.html) * [GIPHY 上的 Redshift](https://engineering.giphy.com/scaling-redshift-without-scaling-costs/) * [Hudl 上的 Redshift](https://www.hudl.com/bits/the-low-hanging-fruit-of-redshift-performance) * [Drivy 上的 Redshift](https://drivy.engineering/redshift_tips_ticks_part_1/) * [文档数据库](https://msdn.microsoft.com/en-us/magazine/hh547103.aspx) * [eBay:使用 MongoDB 构建关键任务多数据中心应用程序](https://www.mongodb.com/blog/post/ebay-building-mission-critical-multi-data-center-applications-with-mongodb) * [百度的 MongoDB:多租户集群在 160 个分片上存储超过 2000 亿个文档](https://www.mongodb.com/blog/post/mongodb-at-baidu-powering-100-apps-across-600-nodes-at-pb-scale) * [迁移 Mongo 数据Addepar](https://medium.com/build-addepar/migrating-mountains-of-mongo-data-63e530539952) * [Parse 的 AWS 和 MongoDB 基础设施(已被 Facebook 收购)](https://medium.baqend.com/parse-is-gone-a-few-secrets-about-their-infrastructure-91b3ab2fcf71) * [Addepar 的海量 Mongo 数据迁移](https://medium.com/build-addepar/migrating-mountains-of-mongo-data-63e530539952) * [LinkedIn 的 Couchbase 生态系统](https://engineering.linkedin.com/blog/2017/12/couchbase-ecosystem-at-linkedin) * [SimpleDB 在Zendesk](https://medium.com/zendesk-engineering/resurrecting-amazon-simpledb-9404034ec506) * [Espresso:LinkedIn 的分布式文档存储](https://engineering.linkedin.com/espresso/introducing-espresso-linkedins-hot-new-distributed-document-store) * [图数据库](https://www.eecs.harvard.edu/margo/papers/systor13-bench/) * [FlockDB:Twitter 的分布式图数据库](https://blog.twitter.com/engineering/en_us/a/2010/introducing-flockdb.html) * [TAO:Twitter 的社交图谱分布式数据存储Facebook](https://www.cs.cmu.edu/~pavlo/courses/fall2013/static/papers/11730-atc13-bronson.pdf) * [Akutan:eBay 的分布式知识图谱存储](https://tech.ebayinc.com/engineering/akutan-a-distributed-knowledge-graph-store/) * [时间序列数据库](https://www.influxdata.com/time-series-database/) * [Beringei:Facebook 的高性能时间序列存储引擎](https://code.facebook.com/posts/952820474848503/beringei-a-high-performance-time-series-storage-engine/) * [MetricsDB:Twitter 用于存储指标的时间序列数据库](https://blog.twitter.com/engineering/en_us/topics/infrastructure/2019/metricsdb.html) * [Atlas:Netflix 的内存维度时间序列数据库](https://medium.com/netflix-techblog/introducing-atlas-netflixs-primary-telemetry-platform-bd31f4d8ed9a) * [Heroic:时间序列数据库Spotify](https://labs.spotify.com/2015/11/17/monitoring-at-spotify-introducing-heroic/) * [Roshi:SoundCloud 时间序列事件分布式存储系统](https://developers.soundcloud.com/blog/roshi-a-crdt-system-for-timestamped-events) * [Goku:Pinterest 时间序列数据库](https://medium.com/@Pinterest_Engineering/goku-building-a-scalable-and-high-performant-time-series-database-system-a8ff5758a181) * [Netflix 时间序列数据存储扩展(两部分)](https://medium.com/netflix-techblog/scaling-time-series-data-storage-part-ii-d67939655586) * [时间序列数据抽象层Netflix](https://netflixtechblog.com/introducing-netflix-timeseries-data-abstraction-layer-31552f6326f8) * [Druid - 实时分析数据库](https://druid.apache.org/) * [Druid 在 Airbnb 的应用](https://medium.com/airbnb-engineering/druid-airbnb-data-platform-601c312f2a4c) * [Druid 在 Walmart 的应用](https://medium.com/walmartlabs/event-stream-analytics-at-walmart-with-druid-dcf1a37ceda7) * [Druid 在 eBay 的应用](https://tech.ebayinc.com/engineering/monitoring-at-ebay-with-druid/) * [Druid 在Netflix](https://netflixtechblog.com/how-netflix-uses-druid-for-real-time-insights-to-ensure-a-high-quality-experience-19e1e8568d06) * [分布式仓库、依赖项和配置管理](https://betterexplained.com/articles/intro-to-distributed-version-control-illustrated/) * [DGit:Github 上的分布式 Git](https://githubengineering.com/introducing-dgit/) * [Stemma:Palantir 的分布式 Git 服务器](https://medium.com/@palantir/stemma-distributed-git-server-70afbca0fc29) * [Flickr 上的分布式系统配置管理](https://code.flickr.net/2016/03/24/configuration-management-for-distributed-systems-using-github-and-cfg4j/) * [Git 仓库位于微软](https://blogs.msdn.microsoft.com/bharry/2017/05/24/the-largest-git-repo-on-the-planet/) * [微软解决 Git 大型代码库问题](https://www.infoq.com/news/2017/02/GVFS) * [谷歌的单一代码库](https://cacm.acm.org/magazines/2016/7/204032-why-google-stores-billions-of-lines-of-code-in-a-single-repository/fulltext) * [谷歌的基础设施扩展和 (Git) 工作流Adyen](https://medium.com/adyen/from-0-100-billion-scaling-infrastructure-and-workflow-at-adyen-7b63b690dfb6) * [Booking.com 的 Dotfiles 分发](https://medium.com/booking-com-infrastructure/dotfiles-distribution-dedb69c66a75) * [Secret Detector:防止 Yelp 源代码中存在机密信息](https://engineeringblog.yelp.com/2018/06/yelps-secret-detector.html) * [LinkedIn 的大规模软件依赖管理](https://engineering.linkedin.com/blog/2018/09/managing-software-dependency-at-scale) * [在高速代码库中合并代码LinkedIn](https://engineering.linkedin.com/blog/2020/continuous-integration) * [Twitter 的动态配置](https://blog.twitter.com/engineering/en_us/topics/infrastructure/2018/dynamic-configuration-at-twitter.html) * [Mixpanel 的动态配置](https://medium.com/mixpaneleng/dynamic-configuration-at-mixpanel-94bfcf97d6b8) * [GoDaddy 的动态配置](https://sg.godaddy.com/engineering/2019/03/06/dynamic-configuration-for-nodejs/) * [Spotify 的车队管理(3 个部分)](https://engineering.atspotify.com/2023/5/fleet-management-at-spotify-part-3-fleet-wide-refactoring) * [扩展持续集成和持续交付](https://www.synopsys.com/blogs/software-security/agile-cicd-devops-glossary/) * [Facebook 的持续集成堆栈](https://code.fb.com/web/rapid-release-at-massive-scale/) * [Netflix 使用分布式存储库和依赖项进行持续集成](https://medium.com/netflix-techblog/towards-true-continuous-integration-distributed-repositories-and-dependencies-2a2e3108c051) * [Dropbox 使用 Bazel 进行持续集成和部署](https://blogs.dropbox.com/tech/2019/12/continuous-integration-and-deployment-with-bazel/) * [Dropbox 采用 Bazel 进行 Web 开发Airbnb](https://medium.com/airbnb-engineering/adopting-bazel-for-web-at-scale-a784b2dbe325) * [BuzzFeed 的持续部署](https://tech.buzzfeed.com/continuous-deployments-at-buzzfeed-d171f76c1ac4) * [Screwdriver:雅虎动态基础设施的持续交付构建系统](https://yahooeng.tumblr.com/post/155765242061/open-sourcing-screwdriver-yahoos-continuous) * [Betterment 的 CI/CD](https://www.betterment.com/resources/ci-cd-shortening-the-feedback-loop/) * [BuzzFeed 的 CI/CD Brainly](https://medium.com/engineering-brainly/ci-cd-at-scale-fdfb0f49e031) * [Shopify 使用 Anka 扩展 iOS CI](https://engineering.shopify.com/blogs/engineering/scaling-ios-ci-with-anka) * [Yelp 扩展 Jira Server](https://engineeringblog.yelp.com/2019/04/Scaling-Jira-Server-Administration-For-The-Enterprise.html) * [Flexport 自动扩展 CI/CD 集群](https://flexport.engineering/how-flexport-halved-testing-costs-with-an-auto-scaling-ci-cd-cluster-8304297222f) ## 可用性 * [弹性工程:学会拥抱失败](https://queue.acm.org/detail.cfm?id=2371297) * [LinkedIn Waterbear 项目弹性工程](https://engineering.linkedin.com/blog/2017/11/resilience-engineering-at-linkedin-with-project-waterbear) * [iHeartRadio 应对流量过饱和的弹性](https://tech.iheart.com/resiliency-against-traffic-oversaturation-77c5ed92a5fb) * [GO-JEK 分布式系统中的弹性](https://blog.gojekengineering.com/resiliency-in-distributed-systems-efd30f74baf4) * [企业实用 NoSQL 弹性设计模式eBay](https://www.ebayinc.com/stories/blogs/tech/practical-nosql-resilience-design-pattern-for-the-enterprise/) * [Quora 的灾难恢复能力](https://engineering.quora.com/Ensuring-Quoras-Resilience-to-Disaster) * [Expedia 的网站恢复能力](https://www.infoq.com/presentations/expedia-website-resiliency?utm_source=presentations_about_Case_Study&utm_medium=link&utm_campaign=Case_Study) * [eBay 使用 Kafka 实现灾难恢复和恢复](https://tech.ebayinc.com/engineering/resiliency-and-disaster-recovery-with-kafka/) * [eBay 多区域 Kafka 的灾难恢复Uber](https://eng.uber.com/kafka/) * [故障转移](http://cloudpatterns.org/mechanisms/failover_system) * [全局流量路由和故障转移的演变](https://www.usenix.org/conference/srecon16/program/presentation/heady) * [灾难恢复故障转移测试](https://www.usenix.org/conference/srecon17asia/program/presentation/liu_zehua) * [设计一个可应对故障的微服务架构](https://blog.risingstack.com/designing-microservices-architecture-for-failure/) * [GoSquared 使用 ELB 实现自动故障转移](https://engineering.gosquared.com/use-elb-automatic-failover) * [美国航空淘汰数据库以提高可用性Express](http://americanexpress.io/eliminate-the-database-for-higher-availability/) * [Vinted 使用 Redis Sentinel 实现故障转移](http://engineering.vinted.com/2015/09/03/failover-with-redis-sentinel/) * [FreeAgent 的高可用性 SaaS 基础架构](http://engineering.freeagent.com/2017/02/06/ha-infrastructure-without-breaking-the-bank/) * [GitHub 上的 MySQL 高可用性](https://github.blog/2018-06-20-mysql-high-availability-at-github/) * [Eventbrite 的 MySQL 高可用性](https://www.eventbrite.com/engineering/mysql-high-availability-at-eventbrite/) * [业务连续性和灾难恢复沃尔玛](https://medium.com/walmartlabs/business-continuity-disaster-recovery-in-the-microservices-world-ef2adca363df) * [负载均衡](https://blog.vivekpanyam.com/scaling-a-web-service-load-balancing/) * [现代网络负载均衡和代理简介](https://blog.envoyproxy.io/introduction-to-modern-network-load-balancing-and-proxying-a57f6ff80236) * [五大(负载均衡)可扩展性模式](https://www.f5.com/company/blog/top-five-scalability-patterns) * [支持 Facebook 超过 13 亿用户的负载均衡基础设施](https://www.usenix.org/conference/srecon15europe/program/presentation/shuff) * [DHCPLB:DHCP 负载均衡器Facebook](https://code.facebook.com/posts/1734309626831603/dhcplb-an-open-source-load-balancer/) * [Katran:Facebook 的可扩展网络负载均衡器](https://code.facebook.com/posts/1906146702752923/open-sourcing-katran-a-scalable-network-load-balancer/) * [Deterministic Aperture:Twitter 的分布式负载均衡算法](https://blog.twitter.com/engineering/en_us/topics/infrastructure/2019/daperture-load-balancer.html) * [Netflix 使用 Eureka 进行负载均衡](https://medium.com/netflix-techblog/netflix-shares-cloud-load-balancing-and-failover-tool-eureka-c10647ef95e5) * [Netflix 的边缘负载均衡](https://medium.com/netflix-techblog/netflix-edge-load-balancing-695308b5548c) * [Zuul 2:Netflix 的云网关](https://medium.com/netflix-techblog/open-sourcing-zuul-2-82ea476cb2b3) * [Yelp 的负载均衡](https://engineeringblog.yelp.com/2017/05/taking-zero-downtime-load-balancing-even-further.html) * [Github 的负载均衡](https://githubengineering.com/introducing-glb/) * [使用一致性哈希改进负载均衡](https://githubengineering.com/introducing-glb/) * [Vimeo](https://medium.com/vimeo-engineering-blog/improving-load-balancing-with-a-new-consistent-hashing-algorithm-9f1bd75709ed) * [500 像素下的 UDP 负载均衡](https://developers.500px.com/udp-load-balancing-with-keepalived-167382d7ad08) * [QALM:Uber 的 QoS 负载管理框架](https://eng.uber.com/qalm/) * [LinkedIn 使用 Rum DNS 进行流量引导](https://www.usenix.org/conference/srecon17europe/program/presentation/rastogi) * [Dropbox 的流量基础设施(边缘网络)](https://blogs.dropbox.com/tech/2018/10/dropbox-traffic-infrastructure-edge-network/) * [Dropbox 基于智能 DNS 的负载均衡技术](https://blogs.dropbox.com/tech/2020/01/intelligent-dns-based-load-balancing-at-dropbox/) * [Stripe 的 DNS 系统监控](https://stripe.com/en-sg/blog/secret-life-of-dns) * [Monday 的多 DNS 架构(共 3 部分)](https://medium.com/monday-engineering/how-and-why-we-migrated-our-dns-from-cloudflare-to-a-multi-dns-architecture-part-3-584a470f4062) * [Hulu 的动态任播 DNS 基础设施](https://medium.com/hulu-tech-blog/building-hulus-dynamic-anycast-dns-infrastructure-985a7a11fd30) * [速率限制](https://www.keycdn.com/support/rate-limiting/) * [Cloudflare 使用速率限制扩展数百万个域名](https://blog.cloudflare.com/counting-things-a-lot-of-different-things/) * [Cloud Bouncer:雅虎的分布式速率限制](https://yahooeng.tumblr.com/post/111288877956/cloud-bouncer-distributed-rate-limiting-at-yahoo) * [Stripe 使用速率限制器扩展 API](https://stripe.com/blog/rate-limiters) * [Allegro 的分布式速率限制](https://allegro.tech/2017/04/hermes-max-rate.html) * [Ratequeue:Allegro 的核心队列和速率限制系统Twilio](https://www.twilio.com/blog/2017/11/chaos-engineering-ratequeue-ha.html) * [Grab 的配额服务](https://engineering.grab.com/quotas-service) * [Figma 的速率限制](https://medium.com/figma-design/an-alternative-approach-to-rate-limiting-f8a06cf7c94c) * [自动扩缩](https://medium.com/@BotmetricHQ/top-11-hard-won-lessons-learned-about-aws-auto-scaling-5bfe56da755f) * [Pinterest 的自动扩缩](https://medium.com/@Pinterest_Engineering/auto-scaling-pinterest-df1d2beb4d64) * [基于请求队列的自动扩缩Square](https://medium.com/square-corner-blog/autoscaling-based-on-request-queuing-c4c0f57f860f) * [Trivago 的 Jenkins 自动扩容](http://tech.trivago.com/2017/02/17/your-definite-guide-for-autoscaling-jenkins/) * [Spotify 的 Pub-Sub 消费者自动扩容](https://labs.spotify.com/2017/11/20/autoscaling-pub-sub-consumers/) * [Spotify 根据 CPU 负载自动扩容 Bigtable 集群](https://labs.spotify.com/2018/12/18/bigtable-autoscaler-saving-money-and-time-using-managed-storage/) * [Spotify 的 AWS Step Functions 活动自动扩容](https://engineeringblog.yelp.com/2019/06/autoscaling-aws-step-functions-activities.html) * [Scryer:Netflix 的预测性自动缩放引擎](https://medium.com/netflix-techblog/scryer-netflixs-predictive-auto-scaling-engine-a3f8fc922270) * [Bouncer:Palantir 的简单 AWS 自动缩放滚动](https://medium.com/palantir/bouncer-simple-aws-auto-scaling-rollovers-c5af601d65d4) * [Clusterman:Yelp 的 Mesos 集群自动缩放](https://engineeringblog.yelp.com/2019/02/autoscaling-mesos-clusters-with-clusterman.html) * [Google 全球分布式存储系统的可用性](http://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/36737.pdf) * [Yahoo 的 NodeJS 高可用性](https://yahooeng.tumblr.com/post/68823943185/nodejs-high-availability) * [LinkedIn 的运营(11 部分)](https://www.linkedin.com/pulse/introduction-every-day-monday-operations-benjamin-purgason) * [监控助力 LinkedIn Feed 的高可用性](https://www.usenix.org/conference/srecon17americas/program/presentation/barot) * [支持Facebook](https://code.facebook.com/posts/166966743929963/how-production-engineers-support-global-events-on-facebook/) * [BlaBlaCar 的高可用性](https://medium.com/blablacar-tech/the-expendables-backends-high-availability-at-blablacar-8cea3b95b26b) * [Netflix 的高可用性](https://medium.com/@NetflixTechBlog/tips-for-high-availability-be0472f2599c) * [Twilio 的高可用性云基础设施](https://www.twilio.com/engineering/2011/12/12/scaling-high-availablity-infrastructure-in-cloud) * [Twilio 的数据中心运维自动化Dropbox](https://blogs.dropbox.com/tech/2019/01/automating-datacenter-operations-at-dropbox/) * [Riot Games 全球化玩家账户](https://technology.riotgames.com/news/globalizing-player-accounts) ## 稳定性 * [熔断器](https://martinfowler.com/bliki/CircuitBreaker.html) * [分布式系统中的熔断器](https://www.infoq.com/presentations/circuit-breaking-distributed-systems) * [容器扩展熔断器](https://f5.com/about-us/blog/articles/the-art-of-scaling-containers-circuit-breakers-28919) * [SoundCloud 的弹性经验教训](https://developers.soundcloud.com/blog/lessons-in-resilience-at-SoundCloud) * [保护器:Trivago 的时间序列数据库熔断器](http://tech.trivago.com/2016/02/23/protector/) * [使用熔断器提升生产环境稳定性Heroku](https://blog.heroku.com/improved-production-stability-with-circuit-breakers) * [Zendesk 的熔断器](https://medium.com/zendesk-engineering/the-joys-of-circuit-breaking-ee6584acd687) * [Traveloka 的熔断器](https://medium.com/traveloka-engineering/circuit-breakers-dont-let-your-dependencies-bring-you-down-5ba1c5cf1eec) * [Shopify 的熔断器](https://shopify.engineering/circuit-breaker-misconfigured) * [超时](https://www.javaworld.com/article/2824163/application-performance/stability-patterns-applied-in-a-restful-architecture.html) * [容错(超时和重试,线程) Netflix 的“隔离、信号量和断路器”](https://medium.com/netflix-techblog/fault-tolerance-in-a-high-volume-distributed-system-91ab4faae74a) * [强制超时:DoorDash 的可靠性方法论](https://doordash.engineering/2018/12/21/enforce-timeout-a-doordash-reliability-methodology/) * [eBay 启用 tcp_tw_recycle 后解决连接超时问题](https://www.ebayinc.com/stories/blogs/tech/a-vip-connection-timeout-issue-caused-by-snat-and-tcp-tw-recycle/) * [Booking.com 的 MySQL 崩溃安全复制](https://medium.com/booking-com-infrastructure/better-crash-safe-replication-for-mysql-a336a69b317f) * [隔墙:分区并在同一部分中容忍故障](https://skife.org/architecture/fault-tolerance/2009/12/31/bulkheads.html) * [稳定状态:始终将日志放在单独的磁盘上](https://docs.microsoft.com/en-us/sql/relational-databases/policy-based-management/place-data-and-log-files-on-separate-drives) * [节流:保持稳定的速度](http://www.sosp.org/2001/papers/welsh.pdf) * [多集群:提高弹性和稳定性LinkedIn 的大规模单体 API 服务](https://engineering.linkedin.com/blog/2017/11/improving-resiliency-and-stability-of-a-large-scale-api) * [英雄联盟服务器中的确定性(4 部分)](https://engineering.riotgames.com/news/determinism-league-legends-fixing-divergences) ## 性能 * [操作系统、存储、数据库和网络性能优化](https://stackify.com/application-performance-metrics/) * [Instagram 使用后台数据预取提升性能](https://engineering.instagram.com/improving-performance-with-background-data-prefetching-b191acb39898) * [LinkedIn 修复 Linux 文件系统性能回归问题](https://engineering.linkedin.com/blog/2020/fixing-linux-filesystem-performance-regressions) * [eBay 使用压缩技术解决网络 I/O 瓶颈](https://www.ebayinc.com/stories/blogs/tech/how-ebays-shopping-cart-used-compression-techniques-to-solve-network-io-bottlenecks/) * [优化 Web 服务器以实现高吞吐量和低延迟Dropbox](https://blogs.dropbox.com/tech/2017/09/optimizing-web-servers-for-high-throughput-and-low-latency/) * [Netflix 60,000 毫秒 Linux 性能分析](https://medium.com/netflix-techblog/linux-performance-analysis-in-60-000-milliseconds-accc10403c55) * [Mixpanel 实时缩减 Google Cloud 持久磁盘 (PD-SSD)](https://engineering.mixpanel.com/2018/07/31/live-downsizing-google-cloud-pds-for-fun-and-profit/) * [Zapier 使用 Python 和 Celery 的 jemalloc 将内存使用量降低 40%](https://zapier.com/engineering/celery-python-jemalloc/) * [减少内存Slack 内存占用](https://slack.engineering/reducing-slacks-memory-footprint-4480fec7e8eb) * [Slack 持续负载测试](https://slack.engineering/continuous-load-testing/) * [Pinterest 性能改进](https://medium.com/@Pinterest_Engineering/driving-user-growth-with-performance-improvements-cfc50dafadd7) * [Wix 服务器端渲染](https://www.youtube.com/watch?v=f9xI2jR71Ms) * [Yelp MySQLStreamer 性能提升 30 倍](https://engineeringblog.yelp.com/2018/02/making-30x-performance-improvements-on-yelps-mysqlstreamer.html) * [优化 API Netflix](https://medium.com/netflix-techblog/optimizing-the-netflix-api-5c9ac715cf19) * [沃尔玛使用 Riemann 和 Clojure 进行性能监控](https://medium.com/walmartlabs/performance-monitoring-with-riemann-and-clojure-eafc07fcd375) * [Zynga 实时游戏性能跟踪仪表盘](https://www.zynga.com/blogs/engineering/live-games-have-evolving-performance) * [eBay 优化 CAL 报告 Hadoop MapReduce 作业](https://www.ebayinc.com/stories/blogs/tech/optimization-of-cal-report-hadoop-mapreduce-job/) * [Quartz 调度器性能调优eBay](https://www.ebayinc.com/stories/blogs/tech/performance-tuning-on-quartz-scheduler/) * [Riot Games 的 C++ 性能分析(第一部分:优化,第二部分:测量与分析)](https://engineering.riotgames.com/news/profiling-optimisation) * [HomeAway 的 React 服务器端渲染性能分析](https://medium.com/homeaway-tech-blog/profiling-react-server-side-rendering-to-free-the-node-js-event-loop-7f0fe455a901) * [Dailymotion 的硬件辅助视频转码](https://medium.com/dailymotion-engineering/hardware-assisted-video-transcoding-at-dailymotion-66cd2db448ae) * [千万 RPS 跨分片交易Dropbox](https://blogs.dropbox.com/tech/2018/11/cross-shard-transactions-at-10-million-requests-per-second/) * [Pinterest 的 API 分析](https://medium.com/@Pinterest_Engineering/api-profiling-at-pinterest-6fa9333b4961) * [Yelp 使用 Pagelets 并行化服务器端处理](https://engineeringblog.yelp.com/2017/07/generating-web-pages-in-parallel-with-pagelets.html) * [Twitter 改进 Redis 中的密钥过期时间](https://blog.twitter.com/engineering/en_us/topics/infrastructure/2019/improving-key-expiration-in-redis.html) * [MindGeek 使用火焰图优化广告投放网络性能](https://medium.com/mindgeek-engineering-blog/ad-delivery-network-performance-optimization-with-flame-graphs-bc550cf59cf7) * [容器的预测性 CPU 隔离Netflix 的预测性 CPU 隔离](predictive-cpu-isolation-of-containers-at-netflix-91f014d856c7) * [Uber 提升 HDFS I/O 利用率以提高效率](https://eng.uber.com/improving-hdfs-i-o-utilization-for-efficiency/) * [Cloud Jewels:Etsy 云端用电量估算](https://codeascraft.com/2020/04/23/cloud-jewels-estimating-kwh-in-the-cloud/) * [Unthrottled:Indeed 云端 CPU 限制修复(两部分)](https://engineering.indeedblog.com/blog/2019/12/unthrottled-fixing-cpu-limits-in-the-cloud/) * [性能优化通过调整垃圾回收](https://confluence.atlassian.com/enterprise/garbage-collection-gc-tuning-guide-461504616.html) * [LinkedIn 的 Java 应用程序中的垃圾回收](https://engineering.linkedin.com/garbage-collection/garbage-collection-optimization-high-throughput-and-low-latency-java-applications) * [Adobe 的高吞吐量、低延迟机器学习服务中的垃圾回收](https://medium.com/adobetech/engineering-high-throughput-low-latency-machine-learning-services-7d45edac0271) * [SoundCloud 的 Redux 应用程序中的垃圾回收](https://developers.soundcloud.com/blog/garbage-collection-in-redux-applications) * [Go 应用程序中的垃圾回收Twitch](https://blog.twitch.tv/go-memory-ballast-how-i-learnt-to-stop-worrying-and-love-the-heap-26c2462549a2) * [阿里巴巴 V8 垃圾收集日志分析](https://www.linux.com/blog/can-nodejs-scale-ask-team-alibaba) * [Instagram 使用 Python 垃圾收集技术降低每次请求 50% 的内存增长](https://instagram-engineering.com/copy-on-write-friendly-python-garbage-collection-ad6ed5233ddf) * [Github 移除带外垃圾收集器 (OOBGC) 的性能影响](https://githubengineering.com/removing-oobgc/) * [调试 Java 内存泄漏Allegro](https://allegro.tech/2018/05/a-comedy-of-errors-debugging-java-memory-leaks.html) * [阿里巴巴的 JVM 优化](https://www.youtube.com/watch?v=X4tmr3nhZRg) * [Uber 为大规模服务调优 JVM 内存](https://eng.uber.com/jvm-tuning-garbage-collection/) * [沃尔玛的 Solr 性能调优](https://medium.com/walmartglobaltech/solr-performance-tuning-beb7d0d0f8d9) * [Flipkart 的高吞吐量微服务内存调优](https://blog.flipkart.tech/memory-tuning-a-high-throughput-microservice-ed57b3e60997) * [图片、视频和页面加载性能优化](https://developers.google.com/web/fundamentals/performance/why-performance-matters/) * [Facebook 大规模优化 360 度全景照片](https://code.facebook.com/posts/129055711052260/optimizing-360-photos-at-scale/) * [Etsy 照片基础架构中图片文件大小的缩减](https://codeascraft.com/2017/05/30/reducing-image-file-size-at-etsy/) * [Pinterest 提升 GIF 性能](https://medium.com/@Pinterest_Engineering/improving-gif-performance-on-pinterest-8dad74bf92f1) * [优化 Pinterest 视频播放性能Pinterest](https://medium.com/@Pinterest_Engineering/optimizing-video-playback-performance-caf55ce310d1) * [Netflix 使用动态优化器优化低带宽视频流](https://medium.com/netflix-techblog/optimized-shot-based-encodes-now-streaming-4b9464204830) * [YouTube 的自适应视频流](https://youtube-eng.googleblog.com/2018/04/making-high-quality-video-efficient.html) * [Dailymotion 减少视频加载时间](https://medium.com/dailymotion/reducing-video-loading-time-fa9c997a2294) * [Zillow 提升首页性能](https://www.zillow.com/engineering/improving-homepage-performance/) * [Zillow 客户端性能优化流程Expedia](https://medium.com/expedia-engineering/go-fast-or-go-home-the-process-of-optimizing-for-client-performance-57bb497402e) * [BBC 的网络性能](https://medium.com/bbc-design-engineering/bbc-world-service-web-performance-26b08f7abfcc) * [使用 Brotli 压缩进行性能优化](https://blogs.akamai.com/2016/02/understanding-brotlis-potential.html) * [LinkedIn 使用 Brotli 压缩提升网站速度](https://engineering.linkedin.com/blog/2017/05/boosting-site-speed-using-brotli-compression) * [Brotli 在Booking.com](https://medium.com/booking-com-development/bookings-journey-with-brotli-978b249d34f3) * [Treebo 的 Brotli](https://tech.treebo.com/a-tale-of-brotli-compression-bcb071d9780a) * [Dropbox 部署 Brotli 用于静态内容](https://dropbox.tech/infrastructure/deploying-brotli-for-static-content) * [Yelp 使用 Brotli 进行渐进式性能增强](https://engineeringblog.yelp.com/2017/07/progressive-enhancement-with-brotli.html) * [DoorDash 使用压缩技术加速 Redis](https://doordash.engineering/2019/01/02/speeding-up-redis-with-compression/) * [语言和框架的性能优化](https://www.techempower.com/benchmarks/) * [Netflix 的 Python 应用](https://netflixtechblog.com/python-at-netflix-bba45dae649e) * [Instagram 的大规模 Python 应用(3 部分)](https://instagram-engineering.com/python-at-scale-strict-modules-c0bb9245c834) * [Issuu 的 OCaml 最佳实践(2 部分)](https://engineering.issuu.com/2018/12/10/our-current-ocaml-best-practices-part-2) * [Slack 的 PHP 应用](https://slack.engineering/taking-php-seriously-cf7a60065329) * [Go 在Trivago](https://tech.trivago.com/2020/03/02/why-we-chose-go/) * [Etsy 的 TypeScript](https://codeascraft.com/2021/11/08/etsys-journey-to-typescript/) * [Etsy 的 Kotlin 状态管理](https://www.etsy.com/sg-en/codeascraft/sealed-classes-opened-my-mind) * [DoorDash 的 Kotlin](https://doordash.engineering/2022/03/22/how-to-leverage-functional-programming-in-kotlin-to-write-better-cleaner-code) * [BPF 与 Go 在Bumble](https://medium.com/bumble-tech/bpf-and-go-modern-forms-of-introspection-in-linux-6b9802682223) * [GitLab 中的 Ruby on Rails](https://medium.com/gitlab-magazine/why-we-use-ruby-on-rails-to-build-gitlab-601dce4a7a38) * [Rust 在 Figma 的生产环境中的应用](https://medium.com/figma-design/rust-in-production-at-figma-e10a0ec31929) * [WeWork 的语言栈选择](https://engineering.wework.com/choosing-a-language-stack-cac3726928f6) * [从 Go 切换到 Rust 的Discord](https://blog.discord.com/why-discord-is-switching-from-go-to-rust-a190bbca2b1f) * [Agoda 的 ASP.NET Core 性能优化](https://medium.com/agoda-engineering/happy-asp-net-core-performance-optimization-4e21a383d299) * [Uber 的 Go 语言数据竞争模式](https://eng.uber.com/data-race-patterns-in-go/) * [Netflix 的 Java 21 虚拟线程](https://netflixtechblog.com/java-21-virtual-threads-dude-wheres-my-lock-3052540e231d) ## 智能 * [大数据](https://insights.sei.cmu.edu/sei_blog/2017/05/reference-architectures-for-big-data-systems.html) * [Uber 数据平台](https://eng.uber.com/uber-big-data-platform/) * [宝马数据平台](https://www.unibw.de/code/events-u/jt-2018-workshops/ws3_bigdata_vortrag_widmann.pdf) * [Netflix 数据平台](https://www.youtube.com/watch?v=CSDIThSwA7s) * [Flipkart 数据平台](https://blog.flipkart.tech/overview-of-flipkart-data-platform-20c6d3e9a196) * [ Coupang](https://medium.com/coupang-tech/evolving-the-coupang-data-platform-308e305a9c45) * [DoorDash 的数据平台](https://doordash.engineering/2020/09/25/how-doordash-is-scaling-its-data-platform/) * [可汗学院的数据平台](http://engineering.khanacademy.org/posts/khanalytics.htm) * [Airbnb 的数据基础设施](https://medium.com/airbnb-engineering/data-infrastructure-at-airbnb-8adfb34f169c) * [LinkedIn 的数据基础设施](https://www.infoq.com/presentations/big-data-infrastructure-linkedin) * [ GO-JEK](https://blog.gojekengineering.com/data-infrastructure-at-go-jek-cd4dc8cbd929) * [Pinterest 的数据采集基础设施](https://medium.com/@Pinterest_Engineering/scalable-and-reliable-data-ingestion-at-pinterest-b921c2ee8754) * [Pinterest 的数据分析架构](https://medium.com/@Pinterest_Engineering/behind-the-pins-building-analytics-f7b508cdacab) * [Spotify 的数据编排服务](https://engineering.atspotify.com/2022/03/why-we-switched-our-data-orchestration-service/) * [大数据处理(2 部分) Spotify](https://labs.spotify.com/2017/10/23/big-data-processing-at-spotify-the-road-to-scio-part-2/) * [Uber 的大数据处理](https://cdn.oreillystatic.com/en/assets/1/event/160/Big%20data%20processing%20with%20Hadoop%20and%20Spark%2C%20the%20Uber%20way%20Presentation.pdf) * [Lyft 的分析管道](https://cdn.oreillystatic.com/en/assets/1/event/269/Lyft_s%20analytics%20pipeline_%20From%20Redshift%20to%20Apache%20Hive%20and%20Presto%20Presentation.pdf) * [Grammarly 的分析管道](https://tech.grammarly.com/blog/building-a-versatile-analytics-pipeline-on-top-of-apache-spark) * [Teads 的分析管道](https://medium.com/teads-engineering/give-meaning-to-100-billion-analytics-events-a-day-d6ba09aa8f44) * [PayPal 用于实时欺诈预防的机器学习数据管道](https://www.infoq.com/presentations/paypal-ml-fraud-prevention-2018) * [LinkedIn 的大数据分析和机器学习技术](https://cdn.oreillystatic.com/en/assets/1/event/269/Big%20data%20analytics%20and%20machine%20learning%20techniques%20to%20drive%20and%20grow%20business%20Presentation%201.pdf) * [LinkedIn 基于 Hadoop 的自助式报告平台](https://cdn.oreillystatic.com/en/assets/1/event/137/Building%20a%20self-serve%20real-time%20reporting%20platform%20at%20LinkedIn%20Presentation%201.pdf) * [LinkedIn 的隐私保护分析和报告LinkedIn](https://engineering.linkedin.com/blog/2019/04/privacy-preserving-analytics-and-reporting-at-linkedin) * [沃尔玛商品库存追踪分析平台](https://medium.com/walmartlabs/how-we-build-a-robust-analytics-platform-using-spark-kafka-and-cassandra-lambda-architecture-70c2d1bc8981) * [Uber 使用 Apache Pinot 实时分析移动应用崩溃](https://www.uber.com/en-SG/blog/real-time-analytics-for-mobile-app-crashes/) * [HALO:Facebook 的硬件分析和生命周期优化](https://code.fb.com/data-center-engineering/hardware-analytics-and-lifecycle-optimization-halo-at-facebook/) * [RBEA:King 的实时分析平台](https://techblog.king.com/rbea-scalable-real-time-analytics-king/) * [AresDB:Uber 的 GPU 驱动实时分析引擎](https://eng.uber.com/aresdb/) * [AthenaX:Uber 的流式分析平台](https://eng.uber.com/athenax/) * [Jupiter:Uber 的配置驱动广告技术批量提取平台](https://www.uber.com/en-SG/blog/jupiter-batch-ingestion-platform/) * [Delta:Netflix 的数据同步和丰富平台](https://medium.com/netflix-techblog/delta-a-data-synchronization-and-enrichment-platform-e82c36a79aee) * [Keystone:Uber 的实时流式处理平台Netflix](https://medium.com/netflix-techblog/keystone-real-time-stream-processing-platform-a3ee651812a) * [Databook:Uber 利用元数据将大数据转化为知识](https://eng.uber.com/databook/) * [Amundsen:Lyft 的数据发现和元数据引擎](https://eng.lyft.com/amundsen-lyfts-data-discovery-metadata-engine-62d27254fbb9) * [Maze:Uber 的漏斗可视化平台](https://eng.uber.com/maze/) ## 架构 * [Medium 的技术栈](https://medium.engineering/the-stack-that-helped-medium-drive-2-6-millennia-of-reading-time-e56801f7c492) * [Shopify 的技术栈](https://engineering.shopify.com/blogs/engineering/e-commerce-at-scale-inside-shopifys-tech-stack) * [Airbnb 的服务构建(4 部分)](https://medium.com/airbnb-engineering/building-services-at-airbnb-part-4-23c95e428064) * [Evernote 的架构](https://evernote.com/blog/a-digest-of-evernotes-architecture/) * [Riot 的聊天服务架构(3 部分)游戏](https://engineering.riotgames.com/news/chat-service-architecture-persistence) * [英雄联盟客户端架构更新](https://technology.riotgames.com/news/architecture-league-client-update) * [Twitter 广告平台架构](https://blog.twitter.com/engineering/en_us/topics/infrastructure/2020/building-twitters-ad-platform-architecture-for-the-future.html) * [Uber API 网关架构](https://eng.uber.com/architecture-api-gateway/) * [Tinder API 网关架构](https://medium.com/tinder/how-we-built-the-tinder-api-gateway-831c6ca5ceca) * [基本架构Slack](https://slack.engineering/how-slack-built-shared-channels-8d42c895b19f) * [eBay 轻量级分布式架构,处理数千个库的发布](https://tech.ebayinc.com/engineering/a-lightweight-distributed-architecture-to-handle-thousands-of-library-releases-at-ebay/) * [LinkedIn 后端](https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin) * [Flickr 后端](https://yahooeng.tumblr.com/post/157200523046/introducing-tripod-flickrs-backend-refactored) * [Zendesk 基础设施(3 部分)](https://medium.com/zendesk-engineering/the-history-of-infrastructure-at-zendesk-part-3-foundation-team-forming-and-evolving-9859e40f5390) * [Grubhub 云基础设施](https://bytes.grubhub.com/cloud-infrastructure-at-grubhub-94db998a898a) * [实时呈现平台LinkedIn](https://engineering.linkedin.com/blog/2018/01/now-you-see-me--now-you-dont--linkedins-real-time-presence-platf) * [LinkedIn 的设置平台](https://engineering.linkedin.com/blog/2019/05/building-member-trust-through-a-centralized-and-scalable-setting) * [Glassdoor 的 Nearline 系统:规模化和性能提升(两部分)](https://medium.com/glassdoor-engineering/building-a-nearline-system-for-scale-and-performance-part-ii-9e01bf51b23d) * [Glassdoor 的广告实时用户操作计数系统Pinterest](https://medium.com/@Pinterest_Engineering/building-a-real-time-user-action-counting-system-for-ads-88a60d9c9a) * [Riot Games 的 API 平台](https://engineering.riotgames.com/news/riot-games-api-deep-dive) * [《纽约时报》的游戏平台](https://open.nytimes.com/play-by-play-moving-the-nyt-games-platform-to-gcp-with-zero-downtime-cf425898d569) * [Kabootar:Swiggy 的通信平台](https://bytes.swiggy.com/kabootar-swiggys-communication-platform-e5a43cc25629) * [Simone:分布式模拟服务Netflix](https://medium.com/netflix-techblog/https-medium-com-netflix-techblog-simone-a-distributed-simulation-service-b2c85131ca1b) * [Seagull:帮助 Yelp 每天运行超过 2000 万次测试的分布式系统](https://engineeringblog.yelp.com/2017/04/how-yelp-runs-millions-of-tests-every-day.html) * [PriceAggregator:Agoda 的智能酒店价格获取系统(3 部分)](https://medium.com/agoda-engineering/priceaggregator-an-intelligent-system-for-hotel-price-fetching-part-3-52acfc705081) * [Phoenix:测试平台(3 部分) Tinder](https://medium.com/tinder-engineering/phoenix-tinders-testing-platform-part-iii-520728b9537) * [Netflix 的六边形架构](https://netflixtechblog.com/ready-for-changes-with-hexagonal-architecture-b315ec967749) * [LINE 贴纸服务架构](https://www.slideshare.net/linecorp/architecture-sustaining-line-sticker-services) * [Palantir 的 Stack Overflow Enterprise](https://medium.com/@palantir/terraforming-stack-overflow-enterprise-in-aws-47ee431e6be7) * [Netflix 的关注动态、兴趣动态和为你精选的架构](https://medium.com/@Pinterest_Engineering/building-a-dynamic-and-responsive-pinterest-7d410e99f0a9)
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