[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-72708":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":10,"languages":10,"totalLinesOfCode":10,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":24,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":30,"readmeContent":31,"aiSummary":32,"trendingCount":15,"starSnapshotCount":15,"syncStatus":33,"lastSyncTime":34,"discoverSource":35},72708,"system-design-resources","InterviewReady\u002Fsystem-design-resources","InterviewReady","These are the best resources for System Design on the Internet","https:\u002F\u002Finterviewready.io",null,18180,2452,314,1,0,4,20,48,12,45,"GNU General Public License v3.0",false,"main",true,[26,27,28,29],"cache","fault-tolerance","scalability","system-design","2026-06-12 02:03:07","# System Design Resources\nThese are the best resources for System Design on the Internet.\n\n# Table of Contents\n\n- [Video Processing](#video-processing)\n- [Cluster and Workflow Management](#cluster-and-workflow-management)\n- [Intra-Service Messaging](#intra-service-messaging)\n- [Message Queue Antipattern](#message-queue-antipattern)\n- [Service Mesh](#service-mesh)\n- [Practical System Design](#practical-system-design)\n- [Distributed File System](#distributed-file-system)\n- [Time Series Databases](#time-series-databases)\n- [Rate Limiting](#rate-limiting)\n- [In Memory Database - Redis](#in-memory-database---redis)\n- [Network Protocols](#network-protocols)\n- [Chess Engine Design](#chess-engine-design)\n- [Subscription Management System](#subscription-management-system)\n- [Google Docs](#google-docs)\n- [API Design](#api-design)\n- [NoSQL Database Internals](#nosql-database-internals)\n- [NoSQL Database Algorithms](#nosql-database-algorithms)\n- [Database Replication](#database-replication)\n- [Containers and Docker](#containers-and-docker)\n- [Capacity Estimation](#capacity-estimation)\n- [Publisher Subscriber](#publisher-subscriber)\n- [Event Driven Architectures](#event-driven-architectures)\n- [Software Architectures](#software-architectures)\n- [Microservices](#microservices)\n- [Distributed Transactions consistency Patterns](#distributed-transactions-consistency-patterns)\n- [Load Balancing](#load-balancing)\n- [Alerts and Anomaly Detection](#alerts-and-anomaly-detection)\n- [Distributed Logging](#distributed-logging)\n- [Metrics and Text Search Engine](#metrics-and-text-search-engine)\n- [Single Point of Failure](#single-point-of-failure)\n- [Location Based Services](#location-based-services)\n- [Batch Processing](#batch-processing)\n- [Real Time Stream Processing](#real-time-stream-processing)\n- [Caching](#caching)\n- [Distributed Consensus](#distributed-consensus)\n- [Authorization](#authorization)\n- [Content Delivery Network](#content-delivery-network)\n- [Testing Distributed Systems](#testing-distributed-systems)\n- [System Design Resources](#system-design-resources)\n\n##\n## Video Processing\n- [Transcoding Videos at Scale](https:\u002F\u002Fwww.egnyte.com\u002Fblog\u002Fpost\u002Ftranscoding-how-we-serve-videos-at-scale)\n- [Facebook Video Broadcasting](https:\u002F\u002Fengineering.fb.com\u002Fios\u002Funder-the-hood-broadcasting-live-video-to-millions\u002F)\n- [Netflix Video Encoding at Scale](https:\u002F\u002Fnetflixtechblog.com\u002Fhigh-quality-video-encoding-at-scale-d159db052746)\n- [Netflix Shot based encoding](https:\u002F\u002Fnetflixtechblog.com\u002Foptimized-shot-based-encodes-now-streaming-4b9464204830)\n\n##\n## Cluster and Workflow Management\n- [Facebook Cluster Management](https:\u002F\u002Fengineering.fb.com\u002Fdata-center-engineering\u002Ftwine\u002F)\n- [Google Autopilot - Autoscaling](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3342195.3387524)\n- [Netflix Workflow Orchestration](https:\u002F\u002Fnetflixtechblog.com\u002Fnetflix-conductor-a-microservices-orchestrator-2e8d4771bf40)\n- [Opensource Workflow Management](https:\u002F\u002Fgithub.com\u002Fspotify\u002Fluigi)\n- [Meta Hardware Management](https:\u002F\u002Fengineering.fb.com\u002F2020\u002F12\u002F09\u002Fdata-center-engineering\u002Fhow-facebook-keeps-its-large-scale-infrastructure-hardware-up-and-running\u002F)\n- [Meta Capacity Assignment](https:\u002F\u002Fengineering.fb.com\u002F2022\u002F09\u002F06\u002Fdata-center-engineering\u002Fviewing-the-world-as-a-computer-global-capacity-management\u002F)\n- [Amazon EC2](https:\u002F\u002Fwww.allthingsdistributed.com\u002F2015\u002F07\u002Funder-the-hood-of-the-amazon-ec2-container-service.html)\n\n##\n## Intra-Service Messaging\n- [What is a message queue](https:\u002F\u002Fwww.cloudamqp.com\u002Fblog\u002Fwhat-is-message-queuing.html)\n- [AirBnb Idempotency](https:\u002F\u002Fmedium.com\u002Fairbnb-engineering\u002Favoiding-double-payments-in-a-distributed-payments-system-2981f6b070bb)\n- [Meta Async Task Computing](https:\u002F\u002Fengineering.fb.com\u002F2023\u002F01\u002F31\u002Fproduction-engineering\u002Fmeta-asynchronous-computing\u002F)\n\n## Message Queue Antipattern\n- [DB as queue Antipattern](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FDatabase-as-IPC)\n- [Using a database as a message queue](https:\u002F\u002Fsoftwareengineering.stackexchange.com\u002Fquestions\u002F231410\u002Fwhy-database-as-queue-so-bad)\n- [Anti-pattern of DB as a queue](http:\u002F\u002Fmikehadlow.blogspot.com\u002F2012\u002F04\u002Fdatabase-as-queue-anti-pattern.html)\n- [Drawbacks of DB as a queue](https:\u002F\u002Fwww.cloudamqp.com\u002Fblog\u002Fwhy-is-a-database-not-the-right-tool-for-a-queue-based-system.html)\n\n##\n## Service Mesh\n- [Kubernetes Service Mesh](https:\u002F\u002Fakomljen.com\u002Fkubernetes-service-mesh\u002F)\n- [Kubernetes Sidecar](https:\u002F\u002Fwww.weave.works\u002Fblog\u002Fintroduction-to-service-meshes-on-kubernetes-and-progressive-delivery)\n- [Service Mesh](https:\u002F\u002Fwww.weave.works\u002Fblog\u002Fintroduction-to-service-meshes-on-kubernetes-and-progressive-delivery)\n- [NginX Service Mesh](https:\u002F\u002Fdocs.nginx.com\u002Fnginx-service-mesh\u002Fabout\u002Fwhat-is-nsm\u002F)\n- [Data Plane and Control Plane](https:\u002F\u002Fblog.envoyproxy.io\u002Fservice-mesh-data-plane-vs-control-plane-2774e720f7fc)\n\n##\n## Practical System Design\n- [Facebook Messenger Optimisations](https:\u002F\u002Fspectrum.ieee.org\u002Fhow-facebooks-software-engineers-prepare-messenger-for-new-years-eve)\n- [YouTube Architecture](http:\u002F\u002Fhighscalability.com\u002Fyoutube-architecture)\n- [YouTube scalability 2012](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=w5WVu624fY8)\n- [Distributed Design Patterns](http:\u002F\u002Fhoricky.blogspot.com\u002F2010\u002F10\u002Fscalable-system-design-patterns.html)\n- [Monolith to Microservice](https:\u002F\u002Fmartinfowler.com\u002Farticles\u002Fbreak-monolith-into-microservices.html)\n- [Zerodha Tech Stack](https:\u002F\u002Fzerodha.tech\u002Fblog\u002Fhello-world\u002F)\n\n##\n## Distributed File System\n- [Open Source Distributed File System](https:\u002F\u002Fdocs.ceph.com\u002Fen\u002Flatest\u002Farchitecture\u002F)\n- [Amazon S3 Performance hacks](https:\u002F\u002Faws.amazon.com\u002Fblogs\u002Faws\u002Famazon-s3-performance-tips-tricks-seattle-hiring-event\u002F)\n- [Amazon S3 object expiration](https:\u002F\u002Faws.amazon.com\u002Fblogs\u002Faws\u002Famazon-s3-object-expiration\u002F)\n\n##\n## Time Series Databases\n- [Pinterest Time Series Database](https:\u002F\u002Fmedium.com\u002Fpinterest-engineering\u002Fgoku-building-a-scalable-and-high-performant-time-series-database-system-a8ff5758a181)\n- [Uber Time Series DB](https:\u002F\u002Feng.uber.com\u002Faresdb\u002F)\n- [TimeSeries Relational DB](https:\u002F\u002Fblog.timescale.com\u002Fblog\u002Ftime-series-data-why-and-how-to-use-a-relational-database-instead-of-nosql-d0cd6975e87c)\n- [Facebook Gorilla Time Series DB](http:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol8\u002Fp1816-teller.pdf)\n\n##\n## Rate Limiting\n- [Circuit Breaker Algorithm](https:\u002F\u002Fmartinfowler.com\u002Fbliki\u002FCircuitBreaker.html)\n- [Uber Rate Limiter](https:\u002F\u002Fgithub.com\u002Fuber-go\u002Fratelimit\u002Fblob\u002Fmaster\u002Fratelimit.go)\n\n##\n## In Memory Database - Redis\n- [Redis Official Documentation](https:\u002F\u002Fredis.com\u002F)\n- [Learn Redis through Redis University](https:\u002F\u002Funiversity.redis.com\u002F)\n- [Redis Open Source Repo](https:\u002F\u002Fgithub.com\u002Fredis\u002Fredis)\n- [Redis Architecture](https:\u002F\u002Fmedium.com\u002Fopstree-technology\u002Fredis-cluster-architecture-replication-sharding-and-failover-86871e783ac0)\n\n##\n## Network Protocols\n- [What is HTTP](https:\u002F\u002Fengineering.cred.club\u002Fhead-of-line-hol-blocking-in-http-1-and-http-2-50b24e9e3372)\n- [QUIC Protocol](https:\u002F\u002Fwww.akamai.com\u002Fblog\u002Fperformance\u002Fhttp3-and-quic-past-present-and-future)\n- [TCP Protocol algorithms](https:\u002F\u002Fee.lbl.gov\u002Fpapers\u002Fcongavoid.pdf) (First 10 pages are important)\n- [WebRTC](https:\u002F\u002Fwebrtc.github.io\u002Fwebrtc-org\u002Fblog\u002F2012\u002F07\u002F23\u002Fa-great-introduction-to-webrtc.html)\n- [WebSockets](https:\u002F\u002Fdatatracker.ietf.org\u002Fdoc\u002Fhtml\u002Frfc6455#section-1.2)\n- [Dynamic Source Routing using QUIC](https:\u002F\u002Ffb.watch\u002FfSEbI4KHlA\u002F)\n\n##\n## Chess Engine Design\n- [Chess Engine Building](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=U4ogK0MIzqk)\n\n##\n## Subscription Management System\n- [Subscription Manager](https:\u002F\u002Fnetflixtechblog.com\u002Fbuilding-a-rule-based-platform-to-manage-netflix-membership-skus-at-scale-e3c0f82aa7bc)\n\n##\n## Google Docs\n- [Operational Transform](http:\u002F\u002Fwww.codecommit.com\u002Fblog\u002Fjava\u002Funderstanding-and-applying-operational-transformation)\n- [Google Docs](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=uOFzWZrsPV0&list=PLX)\n- [Lumiere](https:\u002F\u002Fwww.arxiv.org\u002Fabs\u002F2401.12945)\n\n\n## \n## API Design\n\n- [API Design at Airbnb](https:\u002F\u002Fmedium.com\u002Fairbnb-engineering\u002Fbuilding-services-at-airbnb-part-1-c4c1d8fa811b)\n- [Swagger APIs](https:\u002F\u002Fswagger.io\u002Fdocs\u002Fspecification\u002Fabout\u002F)\n\n##\n## NoSQL Database Internals\n\n- [Cassandra Architecture](https:\u002F\u002Fdocs.datastax.com\u002Fen\u002Farchived\u002Fcassandra\u002F3.0\u002Fcassandra\u002Farchitecture\u002FarchIntro.html)\n- [Google BigTable Architecture](https:\u002F\u002Fstatic.googleusercontent.com\u002Fmedia\u002Fresearch.google.com\u002Fen\u002F\u002Farchive\u002Fbigtable-osdi06.pdf)\n- [Amazon Dynamo DB Internals](https:\u002F\u002Fwww.allthingsdistributed.com\u002F2007\u002F10\u002Famazons_dynamo.html)\n- [Design Patterns in Amazon Dynamo DB](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=HaEPXoXVf2k)\n- [Internals of Amazon Dynamo DB](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=yvBR71D0nAQ)\n\n##\n## NoSQL Database Algorithms\n\n- [Hyperloglog Algorithm](https:\u002F\u002Fodino.org\u002Fmy-favorite-data-structure-hyperloglog\u002F)\n- [Log Structured Merge Tree](https:\u002F\u002Fwww.cs.umb.edu\u002F~poneil\u002Flsmtree.pdf)\n- [Sorted String Tables and Compaction Strategies](https:\u002F\u002Fgithub.com\u002Fscylladb\u002Fscylla\u002Fwiki\u002FSSTable-compaction-and-compaction-strategies)\n- [Leveled Compaction Cassandra](https:\u002F\u002Fwww.datastax.com\u002Fblog\u002Fleveled-compaction-apache-cassandra)\n- [Scylla DB Compaction](https:\u002F\u002Fgithub.com\u002Fscylladb\u002Fscylla\u002Fwiki\u002FSSTable-compaction-and-compaction-strategies)\n- [Indexing in Cassandra](https:\u002F\u002Fwww.bmc.com\u002Fblogs\u002Fcassandra-clustering-columns-partition-composite-key\u002F)\n\n##\n## Database Replication\n\n- [Database replication](https:\u002F\u002Fdev.mysql.com\u002Fdoc\u002Frefman\u002F8.0\u002Fen\u002Freplication.html)\n- [Netflix Data replication - Change Data Capture](https:\u002F\u002Fnetflixtechblog.com\u002Fdblog-a-generic-change-data-capture-framework-69351fb9099b)\n- [LinkedIn Logging Usecases](https:\u002F\u002Fengineering.linkedin.com\u002Fdistributed-systems\u002Flog-what-every-software-engineer-should-know-about-real-time-datas-unifying)\n- [Uber Trillions of indexes in LedgerStore](https:\u002F\u002Fwww.uber.com\u002Fen-IN\u002Fblog\u002Fhow-ledgerstore-supports-trillions-of-indexes)\n\n##\n## Containers and Docker\n\n- [Facebook Twine Containerization](https:\u002F\u002Fengineering.fb.com\u002Fdeveloper-tools\u002Fzookeeper-twine\u002F)\n- [CloudFlare Containerization](https:\u002F\u002Fblog.cloudflare.com\u002Fcloud-computing-without-containers\u002F)\n- [Docker Architecture](https:\u002F\u002Fdocs.docker.com\u002Fget-started\u002Foverview\u002F#docker-architecture)\n\n##\n## Capacity Estimation\n\n- [Google Capacity Estimation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=modXC5IWTJI)\n- [Scalability at YouTube 2012](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=G-lGCC4KKok)\n- [Back of envelope Calculations at AWS](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=-3qetLv2Yp0)\n- [Capacity Estimation](http:\u002F\u002Fstatic.googleusercontent.com\u002Fmedia\u002Fresearch.google.com\u002Fen\u002F\u002Fpeople\u002Fjeff\u002Fstanford-295-talk.pdf)\n\n##\n## Publisher Subscriber\n\n- [Oracle Publisher Subscriber](https:\u002F\u002Fdocs.oracle.com\u002Fcd\u002FB10501_01\u002Fappdev.920\u002Fa96590\u002Fadg15pub.htm)\n- [Amazon Pub Sub Messaging](https:\u002F\u002Faws.amazon.com\u002Fpub-sub-messaging\u002F)\n- [Asynchronous processing](http:\u002F\u002Fblog.codepath.com\u002F2013\u002F01\u002F06\u002Fasynchronous-processing-in-web-applications-part-2-developers-need-to-understand-message-queues\u002F)\n- [Async Request Response](https:\u002F\u002Fwww.enterpriseintegrationpatterns.com\u002Fpatterns\u002Fconversation\u002FRequestResponse.html)\n\n##\n## Event Driven Architectures\n\n- [Martin Fowler- Event Driven Architecture](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=STKCRSUsyP0)\n- [Event Driven Architecture](https:\u002F\u002Fmartinfowler.com\u002Farticles\u002F201701-event-driven.html)\n\n##\n## Software Architectures\n\n- [Hexagonal Architecture](https:\u002F\u002Fnetflixtechblog.com\u002Fready-for-changes-with-hexagonal-architecture-b315ec967749)\n- [Hexagonal architecture (Alistair Cockburn)](https:\u002F\u002Falistair.cockburn.us\u002Fhexagonal-architecture\u002F)\n- [The Clean Code by Robert C. Martin (Uncle Bob)](https:\u002F\u002Fblog.cleancoder.com\u002Funcle-bob\u002F2012\u002F08\u002F13\u002Fthe-clean-architecture.html)\n- [CQRS](https:\u002F\u002Fmartinfowler.com\u002Fbliki\u002FCQRS.html)\n- [DomainDrivenDesign](https:\u002F\u002Fmartinfowler.com\u002Fbliki\u002FDomainDrivenDesign.html)\n\n##\n## Microservices\n\n- [Monolith Architecture](https:\u002F\u002Fbuttercms.com\u002Fbooks\u002Fmicroservices-for-startups\u002Fshould-you-always-start-with-a-monolith\u002F)\n- [Monoliths vs Microservices](https:\u002F\u002Farticles.microservices.com\u002Fmonolithic-vs-microservices-architecture-5c4848858f59)\n- [Microservices](http:\u002F\u002Fhighscalability.com\u002Fblog\u002F2018\u002F4\u002F5\u002Fdo-you-have-too-many-microservices-five-design-attributes-th.html)\n- [Uber Nanoservices antipattern](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=kb-m2fasdDY)\n- [Uber Domain oriented microservice](https:\u002F\u002Feng.uber.com\u002Fmicroservice-architecture\u002F)\n\n##\n## Distributed Transactions consistency Patterns\n\n- [Transactional outbox](https:\u002F\u002Fmicroservices.io\u002Fpatterns\u002Fdata\u002Ftransactional-outbox.html)\n- [SAGAS Long lived transactions (LLTs)](https:\u002F\u002Fwww.cs.cornell.edu\u002Fandru\u002Fcs711\u002F2002fa\u002Freading\u002Fsagas.pdf)\n\n##\n## Load Balancing\n\n- [Load Balancer with Sticky Sessions](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F10494431\u002Fsticky-and-non-sticky-sessions)\n- [NetScaler what is load balancing](https:\u002F\u002Fwww.netscaler.com\u002Farticles\u002Fwhat-is-load-balancing)\n- [Nginx Load Balancing](https:\u002F\u002Fwww.nginx.com\u002Fresources\u002Fglossary\u002Fload-balancing\u002F)\n- [Consistent hashing](https:\u002F\u002Fmichaelnielsen.org\u002Fblog\u002Fconsistent-hashing\u002F)\n- [Minimizing connection churn](https:\u002F\u002Fnetflixtechblog.com\u002Fcurbing-connection-churn-in-zuul-2feb273a3598#5e4d)\n- [Google Subsetting Algorithm](https:\u002F\u002Fqueue.acm.org\u002Fdetail.cfm?id=3570937)\n\n##\n## Alerts and Anomaly Detection\n\n- [Outlier Detection](https:\u002F\u002Ftowardsdatascience.com\u002Foutlier-detection-with-isolation-forest-3d190448d45e)\n- [Anomaly Detection](https:\u002F\u002Ftowardsdatascience.com\u002Fmachine-learning-for-anomaly-detection-and-condition-monitoring-d4614e7de770)\n- [Uber Real Time Monitoring and Root Cause Analysis Argos](https:\u002F\u002Feng.uber.com\u002Fargos-real-time-alerts\u002F)\n- [Microsoft Anomaly Detection](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=12Xq9OLdQwQ&t=0s)\n- [Facebook Data Engineering](https:\u002F\u002Fengineering.fb.com\u002F2016\u002F05\u002F09\u002Fcore-data\u002Fintroducing-fblearner-flow-facebook-s-ai-backbone\u002F)\n- [LinkedIn Real Time Alerting](https:\u002F\u002Fengineering.linkedin.com\u002Fblog\u002F2019\u002F06\u002Fsmart-alerts-in-thirdeye--linkedins-real-time-monitoring-platfor)\n- [LinkedIn Isolation Forests](https:\u002F\u002Fengineering.linkedin.com\u002Fblog\u002F2019\u002Fisolation-forest)\n\n##\n## Distributed Logging\n\n- [Uber Distributed Request Tracing](https:\u002F\u002Feng.uber.com\u002Fdistributed-tracing\u002F)\n- [Pintrest Logging](https:\u002F\u002Fmedium.com\u002F@Pinterest_Engineering\u002Fopen-sourcing-singer-pinterests-performant-and-reliable-logging-agent-610fecf35566)\n- [Google Monitoring Infrastructure](https:\u002F\u002Fwww.facebook.com\u002Fatscaleevents\u002Fvideos\u002F959344524420015\u002F)\n\n##\n## Metrics and Text Search Engine\n\n- [Facebook real-time text search engine](https:\u002F\u002Fwww.facebook.com\u002Fwatch\u002F?v=432864835468)\n- [Elastic Search Time Based Querying](https:\u002F\u002Fwww.elastic.co\u002Fguide\u002Fen\u002Felasticsearch\u002Fguide\u002Fcurrent\u002Ftime-based.html)\n- [Elastic Search Aggregation](https:\u002F\u002Fwww.elastic.co\u002Fguide\u002Fen\u002Felasticsearch\u002Fguide\u002Fcurrent\u002Faggregations.html)\n\n##\n## Single Point of Failure\n\n- [Avoiding Single Points of Failure](https:\u002F\u002Fmedium.com\u002Fthe-cloud-architect\u002Fpatterns-for-resilient-architecture-part-3-16e8601c488e)\n- [Netflix Multi-Region Availability](https:\u002F\u002Fnetflixtechblog.com\u002Factive-active-for-multi-regional-resiliency-c47719f6685b)\n- [Oracle Single Points of failure](https:\u002F\u002Fdocs.oracle.com\u002Fcd\u002FE19693-01\u002F819-0992\u002Ffjdch\u002Findex.html)\n- [DNS single point of failure 2004](http:\u002F\u002Fwww.tenereillo.com\u002FGSLBPageOfShame.htm)\n- [DNS traffic management by Shopify](https:\u002F\u002Fshopify.engineering\u002Fintroduction-dns-traffic-management)\n- [Sharding](https:\u002F\u002Fmedium.com\u002F@jeeyoungk\u002Fhow-sharding-works-b4dec46b3f6)\n\n##\n## Location Based Services\n\n- [Google S2 library](https:\u002F\u002Fblog.christianperone.com\u002F2015\u002F08\u002Fgoogles-s2-geometry-on-the-sphere-cells-and-hilbert-curve\u002F)\n\n##\n## Batch Processing\n\n- [Map Reduce Architecture](https:\u002F\u002Fstatic.googleusercontent.com\u002Fmedia\u002Fresearch.google.com\u002Fen\u002F\u002Farchive\u002Fmapreduce-osdi04.pdf)\n\n##\n## Real Time Stream Processing\n\n- [LinkedIn Brooklin- Real-time data streaming](https:\u002F\u002Fengineering.linkedin.com\u002Fblog\u002F2019\u002Fbrooklin-open-source)\n- [Netflix Real Time Stream Processing](https:\u002F\u002Fnetflixtechblog.com\u002Fkeystone-real-time-stream-processing-platform-a3ee651812a)\n- [KSQLDB for Kafka](https:\u002F\u002Fdocs.ksqldb.io\u002Fen\u002Flatest\u002Foperate-and-deploy\u002Fhow-it-works\u002F)\n- [Netflix Psyberg](https:\u002F\u002Fnetflixtechblog.com\u002F1-streamlining-membership-data-engineering-at-netflix-with-psyberg-f68830617dd1)\n\n##\n## Caching\n\n- [Google Guava Cache](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fguava\u002Fwiki\u002FCachesExplained)\n- [Caching (See the README)](https:\u002F\u002Fgithub.com\u002Fben-manes\u002Fcaffeine\u002F)\n- [Caching](http:\u002F\u002Fhighscalability.com\u002Fblog\u002F2016\u002F1\u002F25\u002Fdesign-of-a-modern-cache.html)\n- [Microsoft Caching Guide](https:\u002F\u002Fdocs.microsoft.com\u002Fen-us\u002Fprevious-versions\u002Fmsp-n-p\u002Fdn589802(v%3dpandp.10))\n- [Caching patterns](https:\u002F\u002Fhazelcast.com\u002Fblog\u002Fa-hitchhikers-guide-to-caching-patterns\u002F)\n- [Uber's Integrated Cache for 40M RPS](https:\u002F\u002Fwww.uber.com\u002Fen-IN\u002Fblog\u002Fhow-uber-serves-over-40-million-reads-per-second-using-an-integrated-cache)\n\n##\n## Distributed consensus\n\n- [Paxos](http:\u002F\u002Fifeanyi.co\u002Fposts\u002Funderstanding-consensus\u002F)\n- [Raft](https:\u002F\u002Fraft.github.io\u002F)\n\n##\n## Authorization\n\n- [Designing an Authorization Model for an Enterprise](https:\u002F\u002Fcerbos.dev\u002Fblog\u002Fdesigning-an-authorization-model-for-an-enterprise)\n- [The Architectural Patterns of Cloud-native Authorization Systems](https:\u002F\u002Fwww.aserto.com\u002Fblog\u002F5-laws-cloud-native-authorization)\n\n##\n## Content Delivery Network\n\n- [AWS CloudFront CDN with S3](https:\u002F\u002Faws.amazon.com\u002Fblogs\u002Fnetworking-and-content-delivery\u002Famazon-s3-amazon-cloudfront-a-match-made-in-the-cloud\u002F)\n\n##\n## Testing Distributed Systems\n\n- [Deterministic Testing](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=4fFDFbi3toc)\n- [TLA+ by Leslie Lamport](https:\u002F\u002Flamport.azurewebsites.net\u002Ftla\u002Ftla.html)\n- [Jepsen](https:\u002F\u002Fjenpsen.io)\n\n##\n## System Design Resources\n\n- [Designing Data-Intensive Applications Book](https:\u002F\u002Famzn.to\u002F3SyNAOy)\n- [WhitePapers](https:\u002F\u002Finterviewready.io\u002Fblog\u002Fwhite-papers-worth-reading-for-software-engineers)\n- [InterviewReady Videos](https:\u002F\u002Finterviewready.io?source=github)\n- [System Design Online Judge](https:\u002F\u002Finterviewready.io\u002Fquestion-list\u002Fsystem-design-judge)\n","InterviewReady\u002Fsystem-design-resources 是一个汇集了互联网上最佳系统设计资源的项目。该项目涵盖了从视频处理、集群和工作流管理到微服务、分布式一致性等多个方面的内容，为开发者提供了丰富的学习材料和技术实践案例。它不仅适合准备技术面试的求职者深入研究，也适用于希望提升自己在构建可扩展、容错性强的分布式系统方面技能的软件工程师。此外，对于正在设计复杂系统架构的技术领导者来说，这些资源同样具有很高的参考价值。",2,"2026-06-11 03:43:17","high_star"]