An updated and curated list of selected readings to illustrate High Scalability, High Availability, High Stability, High Performance, and High Intelligence Back-end Designs. Concepts are explained in the articles of notable engineers (Werner Vogels, James Hamilton, Martin Kleppmann, etc) and credible references (highscalability.com, infoq.com, official engineering blogs). Case studies are taken from battle-tested systems those are serving millions to billions of users (Netflix, Alibaba, Flipkart, LINE, Spotify, etc).
> Understand your problems: performance problem (slow for a single user) or scalability problem (fast for a single user but slow under heavy load) by reviewing [design principles](#principles). You can also check some [talks](#talks) of elite engineers from tech giants (Google, Facebook, Instagram, etc) to see how they build and scale their systems.
> Checking out some [interview notes](#interview) and [compeleted architectures](#architectures) to get a comprehensive view. Before designing Whatsapp or Twitter on whiteboard, you must understand thoroughly fundamental building blocks (IPC, OSI, TCP/IP, Gossip, etc). It is even better to take a course on Distributed Systems or Distributed Computing. Good luck!
> If you find this project helpful, please help me [share on Twitter](https://ctt.ec/V8B2p) or [share on Weibo](http://t.cn/RnjFLCB). Thank you very much :bow:
* [Database Isolation Levels and Effects on Performance and Scalability](http://highscalability.com/blog/2011/2/10/database-isolation-levels-and-their-effects-on-performance-a.html)
* [The Benefits of Server Side Rendering Over Client Side Rendering](https://medium.com/walmartlabs/the-benefits-of-server-side-rendering-over-client-side-rendering-5d07ff2cefe8)
* [Automate and Abstract: Lessons from Facebook on Engineering for Scale](https://architecht.io/lessons-from-facebook-on-engineering-for-scale-f5716f0afc7a)
* [Techniques for Splitting Up a Codebase into Microservices and Artifacts at LinkedIn](https://engineering.linkedin.com/blog/2016/02/q-a-with-jim-brikman--splitting-up-a-codebase-into-microservices)
* [Organize Monolith Before Breaking it into Services at Weebly](https://medium.com/weebly-engineering/how-to-organize-your-monolith-before-breaking-it-into-services-69cbdb9248b0)
* [Lessons learned running Docker in production at Treehouse](https://medium.com/treehouse-engineering/lessons-learned-running-docker-in-production-5dce99ece770)
* [Mesos, Docker and Ochopod in Localization Services at Autodesk](http://cloudengineering.autodesk.com/blog/2015/11/mesos-docker-and-ochopod-in-autodesk-localization-services.html)
* [PowerfulSeal: Testing Tool for Kubernetes Clusters at Bloomberg](https://www.techatbloomberg.com/blog/powerfulseal-testing-tool-kubernetes-clusters/)
* [Making 10x Improvement in Release Times with Docker and Amazon ECS at Nextdoor](https://engblog.nextdoor.com/how-nextdoor-made-a-10x-improvement-in-release-times-with-docker-and-amazon-ecs-35aab52b726f)
* [Distributed Tracing with Pintrace at Pinterest](https://medium.com/@Pinterest_Engineering/distributed-tracing-at-pinterest-with-new-open-source-tools-a4f8a5562f6b)
* [Analyzing Distributed Trace Data at Pinterest](https://medium.com/@Pinterest_Engineering/analyzing-distributed-trace-data-6aae58919949)
* [Distributed Tracing at Uber](https://eng.uber.com/distributed-tracing/)
* [The Log: What Every Software Engineer Should Know](https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying)
* [Using Logs to Build a Solid Data Infrastructure - Martin Kleppmann](https://www.confluent.io/blog/using-logs-to-build-a-solid-data-infrastructure-or-why-dual-writes-are-a-bad-idea/)
* [Scalable and Reliable Log Ingestion at Pinterest](https://medium.com/@Pinterest_Engineering/scalable-and-reliable-data-ingestion-at-pinterest-b921c2ee8754)
* [Logging Service with Spark at CERN Accelerator](https://databricks.com/blog/2017/12/14/the-architecture-of-the-next-cern-accelerator-logging-service.html)
* [LogDevice: Distributed Data Store for Logs at Facebook](https://code.facebook.com/posts/357056558062811/logdevice-a-distributed-data-store-for-logs/)
* [Kafka Streams on Heroku](https://blog.heroku.com/kafka-streams-on-heroku)
* [Kafka in Platform Events Architecture at Salesforce](https://engineering.salesforce.com/how-apache-kafka-inspired-our-platform-events-architecture-2f351fe4cf63)
* [Bullet: Forward-Looking Query Engine for Streaming Data at Yahoo](https://yahooeng.tumblr.com/post/161855616651/open-sourcing-bullet-yahoos-forward-looking)
* [Benchmarking Streaming Computation Engines at Yahoo](https://yahooeng.tumblr.com/post/135321837876/benchmarking-streaming-computation-engines-at)
* [Exactly-once Semantics are Possible: Here’s How Kafka Does it](https://www.confluent.io/blog/exactly-once-semantics-are-possible-heres-how-apache-kafka-does-it/)
* [Delivering Billions of Messages Exactly Once: Deduping at Segment](https://segment.com/blog/exactly-once-delivery/)
* [Deduplication For Efficient Storage (From 50 PB To 32 PB) At Mail.Ru](https://medium.com/@andrewsumin/efficient-storage-how-we-went-down-from-50-pb-to-32-pb-99f9c61bf6b4)
* [Search Service (Half a Trillion Documents and Query Average Latency < 100ms) at Twitter (2014)](https://blog.twitter.com/engineering/en_us/a/2014/building-a-complete-tweet-index.html)
* [Manas: High Performing Customized Search System at Pinterest](https://medium.com/@Pinterest_Engineering/manas-a-high-performing-customized-search-system-cf189f6ca40f)
* [Sherlock: Near Real Time Search Indexing at Flipkart](https://tech.flipkart.com/sherlock-near-real-time-search-indexing-95519783859d)
* [Nebula: Storage Platform to Build Search Backends at Airbnb](https://medium.com/airbnb-engineering/nebula-as-a-storage-platform-to-build-airbnbs-search-backends-ecc577b05f06)
* [MemSQL Architecture - The Fast (MVCC, InMem, LockFree, CodeGen) And Familiar (SQL)](http://highscalability.com/blog/2012/8/14/memsql-architecture-the-fast-mvcc-inmem-lockfree-codegen-and.html)
* [zBase: High Performance, Elastic, Distributed Key-Value Store at Zynga](https://www.zynga.com/blogs/engineering/zbase-high-performance-elastic-distributed-key-value-store-2)
* [Avoid Pitfalls in Scaling Cassandra Cluster: Lessons and Remedies at Walmart Labs](https://medium.com/walmartlabs/avoid-pitfalls-in-scaling-your-cassandra-cluster-lessons-and-remedies-a71ca01f8c04)
* [Storing Images in Cassandra at Walmart Scale](https://medium.com/walmartlabs/building-object-store-storing-images-in-cassandra-walmart-scale-a6b9c02af593)
* [Scale to serve 100+ million reads/writes using Spark and Cassandra at Dream11](https://medium.com/dream11-tech-blog/leaderboard-dream11-4efc6f93c23e)
* [Benchmarking Cassandra Scalability on AWS at Netflix](https://medium.com/netflix-techblog/benchmarking-cassandra-scalability-on-aws-over-a-million-writes-per-second-39f45f066c9e)
* [eBay: Building Mission-Critical Multi-Data Center Applications with MongoDB](https://www.mongodb.com/blog/post/ebay-building-mission-critical-multi-data-center-applications-with-mongodb)
* [The AWS and MongoDB Infrastructure of Parse (acquired by Facebook)](https://medium.baqend.com/parse-is-gone-a-few-secrets-about-their-infrastructure-91b3ab2fcf71)
* [JanusGraph: Scalable Graph Database backed by Google, IBM and Hortonworks](https://architecht.io/google-ibm-back-new-open-source-graph-database-project-janusgraph-1d74fb78db6b)
* [Storing Hundreds of Millions of Simple Key-Value Pairs in Redis at Instagram](https://engineering.instagram.com/storing-hundreds-of-millions-of-simple-key-value-pairs-in-redis-1091ae80f74c)
* [Partitioning Main MySQL Database at Airbnb](https://medium.com/airbnb-engineering/how-we-partitioned-airbnb-s-main-database-in-two-weeks-55f7e006ff21)
* [PostgreSQL at Twitch](https://blog.twitch.tv/how-twitch-uses-postgresql-c34aa9e56f58)
* [Scaling MySQL-based Financial Reporting System at Airbnb](https://medium.com/airbnb-engineering/tracking-the-money-scaling-financial-reporting-at-airbnb-6d742b80f040)
* [Time Series Data: Why and How to Use a Relational Database instead of NoSQL](https://blog.timescale.com/time-series-data-why-and-how-to-use-a-relational-database-instead-of-nosql-d0cd6975e87c)
* [Beringei: High-performance Time Series Storage Engine at Facebook](https://code.facebook.com/posts/952820474848503/beringei-a-high-performance-time-series-storage-engine/)
* [Atlas: In-memory Dimensional Time Series Database at Netflix](https://medium.com/netflix-techblog/introducing-atlas-netflixs-primary-telemetry-platform-bd31f4d8ed9a)
* [Heroic: Time Series Database at Spotify](https://labs.spotify.com/2015/11/17/monitoring-at-spotify-introducing-heroic/)
* [Roshi: Distributed Storage System for Time-Series Event at SoundCloud](https://developers.soundcloud.com/blog/roshi-a-crdt-system-for-timestamped-events)
* [Building a Scalable Time Series Database on PostgreSQL](https://blog.timescale.com/when-boring-is-awesome-building-a-scalable-time-series-database-on-postgresql-2900ea453ee2)
* [Using CDN to Improve Site Performance at Coursera](https://building.coursera.org/blog/2015/07/09/improving-coursera-global-site-performance-a-head-to-head-cdn-battle-with-production-traffic/)
* [Strategy: Caching 404s Saved 66% On Server Time at The Onion](http://highscalability.com/blog/2010/3/26/strategy-caching-404s-saved-the-onion-66-on-server-time.html)
* [HAProxy with Kubernetes for User-facing Traffic at SoundCloud](https://developers.soundcloud.com/blog/how-soundcloud-uses-haproxy-with-kubernetes-for-user-facing-traffic)
* [Introduction to Modern Network Load Balancing and Proxying](https://blog.envoyproxy.io/introduction-to-modern-network-load-balancing-and-proxying-a57f6ff80236)
* [Load Balancing infrastructure to support more than 1.3 billion users at Facebook](https://www.usenix.org/conference/srecon15europe/program/presentation/shuff)
* [DHCPLB: Open Source Load Balancer for DHCP at Facebook](https://code.facebook.com/posts/1734309626831603/dhcplb-an-open-source-load-balancer/)
* [Load Balancing with Eureka at Netflix](https://medium.com/netflix-techblog/netflix-shares-cloud-load-balancing-and-failover-tool-eureka-c10647ef95e5)
* [Load Balancing at Yelp](https://engineeringblog.yelp.com/2017/05/taking-zero-downtime-load-balancing-even-further.html)
* [Load Balancing at Github](https://githubengineering.com/introducing-glb/)
* [Consistent Hashing to Improve Load Balancing at Vimeo](https://medium.com/vimeo-engineering-blog/improving-load-balancing-with-a-new-consistent-hashing-algorithm-9f1bd75709ed)
* [UDP Load Balancing at 500 pixel](https://developers.500px.com/udp-load-balancing-with-keepalived-167382d7ad08)
* [A Horror Movie Featuring Auto Scaling Groups, EBS Volumes, Terraform, and Bash](https://blog.gruntwork.io/yak-shaving-series-1-all-i-need-is-a-little-bit-of-disk-space-6e5ef1644f67)
* [Concurrency series by Larry Osterman (Principal SDE at Microsoft)](https://social.msdn.microsoft.com/Profile/Larry%2bOsterman%2b%5BMSFT%5D/activity)
* [Part 8 – Concurrency for scalability](https://blogs.msdn.microsoft.com/larryosterman/2005/02/28/concurrency-part-8-concurrency-for-scalability/)
* [Part 9 - APIs that enable scalable programming](https://blogs.msdn.microsoft.com/larryosterman/2005/03/02/concurrency-part-9-apis-that-enable-scalable-programming/)
* [Part 10 - How do you know if you’ve got a scalability issue?](https://blogs.msdn.microsoft.com/larryosterman/2005/03/03/concurrency-part-10-how-do-you-know-if-youve-got-a-scalability-issue/)
* [Mnesia: A Distributed DBMS Rooted in Concurrency](https://www.developer.com/db/article.php/3864331/Mnesia-A-Distributed-DBMS-Rooted-in-Concurrency.htm)
* [Mesia and CAP](https://medium.com/@jlouis666/mnesia-and-cap-d2673a92850)
* [The Secret To 10 Million Concurrent Connections](http://highscalability.com/blog/2013/5/13/the-secret-to-10-million-concurrent-connections-the-kernel-i.html)
* [Loop Parallelism Pattern: Extracting parallel tasks from loops](https://www.cs.umd.edu/class/fall2001/cmsc411/projects/unroll/main.htm)
* [Fork/Join Pattern: Good for recursive data processing](http://highscalability.com/learn-how-exploit-multiple-cores-better-performance-and-scalability)
* [Map-Reduce: Born for Simplified Data Processing on Large Clusters](http://static.googleusercontent.com/media/research.google.com/en/us/archive/mapreduce-osdi04.pdf)
* [On the Death of Map-Reduce - Henry Robinson, Cloudera](http://the-paper-trail.org/blog/the-elephant-was-a-trojan-horse-on-the-death-of-map-reduce-at-google/)
* [Server-side Optimization to Parallelize the Rendering of Web Pages at Yelp](https://engineeringblog.yelp.com/2017/07/generating-web-pages-in-parallel-with-pagelets.html)
* [Autoscaling Pub-Sub Consumers at Spotify](https://labs.spotify.com/2017/11/20/autoscaling-pub-sub-consumers/)
* [Pulsar: Pub-Sub Messaging at Scale at Yahoo](https://yahooeng.tumblr.com/post/150078336821/open-sourcing-pulsar-pub-sub-messaging-at-scale)
* [Wormhole: Pub-Sub system at Facebook (2013)](https://code.facebook.com/posts/188966771280871/wormhole-pub-sub-system-moving-data-through-space-and-time/)
* [Domain-Driven Design in Organizing Monolith Before Breaking it into Services at Weebly](https://medium.com/weebly-engineering/how-to-organize-your-monolith-before-breaking-it-into-services-69cbdb9248b0)
* [Building Scalable Applications Using Event Sourcing and CQRS with Kafka](https://initiate.andela.com/event-sourcing-and-cqrs-a-look-at-kafka-e0c1b90d17d8)
* [Stemma: Distributed Git Server at Palantir](https://medium.com/@palantir/stemma-distributed-git-server-70afbca0fc29)
* [Configuration Management for Distributed Systems at Flickr](https://code.flickr.net/2016/03/24/configuration-management-for-distributed-systems-using-github-and-cfg4j/)
* [Git Repo at Microsoft - The Largest Git Repo on The Planet](https://blogs.msdn.microsoft.com/bharry/2017/05/24/the-largest-git-repo-on-the-planet/)
* [How Microsoft Solved Git’s Problem with Large Repositories](https://www.infoq.com/news/2017/02/GVFS)
* [Every Day Is Monday in Operations (11 parts) at LinkedIn ](https://www.linkedin.com/pulse/introduction-every-day-monday-operations-benjamin-purgason)
* [Practical Guide to Monitoring and Alerting with Time Series at Scale](https://www.usenix.org/conference/srecon17americas/program/presentation/wilkinson)
* [Architectural Patterns for High Availability - Adrian Cockcroft, Director of Architecture at Netflix](https://www.infoq.com/presentations/Netflix-Architecture)
* [Crash-safe Replication for MySQL at Booking.com](https://medium.com/booking-com-infrastructure/better-crash-safe-replication-for-mysql-a336a69b317f)
* [Bulkheads: Partition and Tolerate Failure in One Part](https://skife.org/architecture/fault-tolerance/2009/12/31/bulkheads.html)
* [Steady State: Always Put Logs on Separate Disk](https://docs.microsoft.com/en-us/sql/relational-databases/policy-based-management/place-data-and-log-files-on-separate-drives)
* [Throttling: Maintain a Steady Pace](http://www.sosp.org/2001/papers/welsh.pdf)
* [Multi-Clustering: Improving Resiliency and Stability of a Large-scale Monolithic API Service at LinkedIn](https://engineering.linkedin.com/blog/2017/11/improving-resiliency-and-stability-of-a-large-scale-api)
* [Improving Performance with Background Data Prefetching at Instagram](https://engineering.instagram.com/improving-performance-with-background-data-prefetching-b191acb39898)
* [Compression Techniques to Solve Network I/O Bottlenecks at eBay](https://www.ebayinc.com/stories/blogs/tech/how-ebays-shopping-cart-used-compression-techniques-to-solve-network-io-bottlenecks/)
* [Optimizing Web Servers for High Throughput and Low Latency at Dropbox](https://blogs.dropbox.com/tech/2017/09/optimizing-web-servers-for-high-throughput-and-low-latency/)
* [Boosting Site Speed Using Brotli Compression at LinkedIn](https://engineering.linkedin.com/blog/2017/05/boosting-site-speed-using-brotli-compression)
* [Linux Performance Analysis in 60.000 Milliseconds at Netflix](https://medium.com/netflix-techblog/linux-performance-analysis-in-60-000-milliseconds-accc10403c55)
* [Improving Video Thumbnails with Deep Neural Nets at YouTube](https://youtube-eng.googleblog.com/2015/10/improving-youtube-video-thumbnails-with_8.html)
* [Optimizing APIs through Dynamic Polyglot Runtime, Fully Asynchronous, and Reactive Programming at Netflix](https://medium.com/netflix-techblog/optimizing-the-netflix-api-5c9ac715cf19)
* [Optimizing Video Stream for Low Bandwidth with Dynamic Optimizer at Netflix](https://medium.com/netflix-techblog/optimized-shot-based-encodes-now-streaming-4b9464204830)
* [Reducing Video Loading Time by Prefetching during Preroll at Dailymotion](http://engineering.dailymotion.com/reducing-video-loading-time-prefetching-video-during-preroll/)
* [30x Performance Improvements on MySQLStreamer at Yelp](https://engineeringblog.yelp.com/2018/02/making-30x-performance-improvements-on-yelps-mysqlstreamer.html)
* [Performance Monitoring with Riemann and Clojure at Walmart](https://medium.com/walmartlabs/performance-monitoring-with-riemann-and-clojure-eafc07fcd375)
* [Using Java Large Heap (110 GB) for Boosting Site Perpormance at Expedia](https://techblog.expedia.com/2015/09/25/solving-problems-with-very-large-java-heaps/)
* [The Process of Optimizing for Client Performance at Expedia](https://techblog.expedia.com/2018/03/09/go-fast-or-go-home-the-process-of-optimizing-for-client-performance/)
* [Scalable Deep Learning Platform On Spark In Baidu](https://www.slideshare.net/JenAman/scalable-deep-learning-platform-on-spark-in-baidu)
* [Horovod: Uber’s Open Source Distributed Deep Learning Framework for TensorFlow](https://eng.uber.com/horovod/)
* [Scaling Gradient Boosted Trees for Click-Through-Rate Prediction at Yelp](https://engineeringblog.yelp.com/2018/01/building-a-distributed-ml-pipeline-part1.html)
* [TensorFlowOnSpark: Distributed Deep Learning on Big Data Clusters at Yahoo](https://yahooeng.tumblr.com/post/157196488076/open-sourcing-tensorflowonspark-distributed-deep)
* [CaffeOnSpark: Distributed Deep Learning on Big Data Clusters at Yahoo](https://yahooeng.tumblr.com/post/139916828451/caffeonspark-open-sourced-for-distributed-deep)
* [AIOps in Practice at Baidu](https://www.usenix.org/conference/srecon17asia/program/presentation/qu)
* [Learning with Privacy at Scale - Differential Privacy Team, Apple](https://machinelearning.apple.com/2017/12/06/learning-with-privacy-at-scale.html)
* [Image Classification Experiment Using Deep Learning at Mercari](https://medium.com/mercari-engineering/mercaris-image-classification-experiment-using-deep-learning-9b4e994a18ec)
* [Content-based Video Relevance Prediction at Hulu](https://medium.com/hulu-tech-blog/content-based-video-relevance-prediction-b2c448e14752)
* [PaddlePaddle Fluid: Elastic Deep Learning on Kubernetes at Baidu](http://research.baidu.com/paddlepaddle-fluid-elastic-deep-learning-kubernetes/)
* [Training ML Models with Airflow and BigQuery at WePay](https://wecode.wepay.com/posts/training-machine-learning-models-with-airflow-and-bigquery)
* [Improving Photo Selection With Deep Learning at TripAdvisor](http://engineering.tripadvisor.com/improving-tripadvisor-photo-selection-deep-learning/)
* [Machine Learning (2 parts) at Condé Nast](https://technology.condenast.com/story/handbag-brand-and-color-detection)
* [Machine Learning Applications In The E-commerce Domain (4 parts) at Rakuten](https://techblog.rakuten.co.jp/2017/07/12/machine-learning-applications-in-the-e-commerce-domain-4/)
* [Venue Rating System at Foursquare](https://engineering.foursquare.com/finding-the-perfect-10-how-we-developed-the-foursquare-venue-rating-system-c76b08f7b9b3)
* [Using Machine Learning to Improve Streaming Quality at Netflix](https://medium.com/netflix-techblog/using-machine-learning-to-improve-streaming-quality-at-netflix-9651263ef09f)
* [Architecture of Real-Time Presence Platform at LinkedIn](https://engineering.linkedin.com/blog/2018/01/now-you-see-me--now-you-dont--linkedins-real-time-presence-platf)
* [Architecture of Simone: Distributed Simulation Service at Netflix](https://medium.com/netflix-techblog/https-medium-com-netflix-techblog-simone-a-distributed-simulation-service-b2c85131ca1b)
* [Architecture of Seagull: Distributed System that Helps Running > 20 Million Tests Per Day at Yelp](https://engineeringblog.yelp.com/2017/04/how-yelp-runs-millions-of-tests-every-day.html)
* [Architecture of Cloud Bouncer: Distributed Rate Limiting at Yahoo](https://yahooeng.tumblr.com/post/111288877956/cloud-bouncer-distributed-rate-limiting-at-yahoo)
* [Reference Architecture For The Open Banking Standard](https://hortonworks.com/blog/reference-architecture-open-banking-standard/)
* [Building a Modern Bank Backend at Monzo](https://monzo.com/blog/2016/09/19/building-a-modern-bank-backend/)
* [Choosing an Architecture for Core Banking System at TrustBK](https://blog.trustbk.com/choosing-an-architecture-85750e1e5a03)
* [Reinventing the Trading Platform for Scale at Wealthsimple](https://medium.com/@Wealthsimple/engineering-at-wealthsimple-reinventing-our-trading-platform-for-scale-17e332241b6c)
* [Tech Stack at TransferWise](http://tech.transferwise.com/the-transferwise-stack-heartbeat-of-our-little-revolution/)
* [Practical NoSQL Resilience Design Pattern for the Enterprise at eBay](https://www.ebayinc.com/stories/blogs/tech/practical-nosql-resilience-design-pattern-for-the-enterprise/)
* [Scaling Chat To 70 Million Players at League Of Legends](http://highscalability.com/blog/2014/10/13/how-league-of-legends-scaled-chat-to-70-million-players-it-t.html)
* [Scaling Online Migrations at Stripe](https://stripe.com/blog/online-migrations)
* [Top 10 Common Large-Scale Software Architectural Patterns in a Nutshell](https://towardsdatascience.com/10-common-software-architectural-patterns-in-a-nutshell-a0b47a1e9013)
* [How NOT to design Netflix in your 45-minute System Design Interview?](https://hackernoon.com/how-not-to-design-netflix-in-your-45-minute-system-design-interview-64953391a054)
* [Distributed Systems in One Lesson - Tim Berglund, Senior Director of Developer Experience at Confluent](https://www.youtube.com/watch?v=Y6Ev8GIlbxc)
* [Building Real Time Infrastructure at Facebook - Jeff Barber and Shie Erlich, Software Engineer at Facebook](https://www.usenix.org/conference/srecon17americas/program/presentation/erlich)
* [Building Reliable Social Infrastructure for Google - Marc Alvidrez, Senior Manager at Google](https://www.usenix.org/conference/srecon16/program/presentation/alvidrez)
* [Site Reliability Engineering at Dropbox - Tammy Butow, Site Reliability Engineering Manager at Dropbox](https://www.youtube.com/watch?v=ggizCjUCCqE)
* [How Google Does Planet-Scale for Planet-Scale Infra - Melissa Binde, SRE Director for Google Cloud Platform](https://www.youtube.com/watch?v=H4vMcD7zKM0)
* [Architecture to Handle 80K RPS Celebrity Sales at Shopify - Simon Eskildsen, Engineering Lead at Shopify](https://www.youtube.com/watch?v=N8NWDHgWA28)
* [Performance Optimization for the Greater China Region at Salesforce - Jeff Cheng, Enterprise Architect at Salesforce](https://www.salesforce.com/video/1757880/)
* [How GIPHY Delivers a GIF to 300 Millions Users - Alex Hoang and Nima Khoshini, Services Engineers at GIPHY](https://vimeo.com/252367076)
* [High Performance Packet Processing Platform at Alibaba - Haiyong Wang, Senior Director at Alibaba](https://www.youtube.com/watch?v=wzsxJqeVIhY&list=PLMu8-hpCxIVENuAue7bd0eCAglLGY_8AW&index=7)
* [Scaling with Performance at Facebook - Bill Jia, VP of Infrastructure at Facebook](https://atscaleconference.com/videos/performance-scale-2018-opening-remarks/)
* [Scaling Live Videos to a Billion Users at Facebook - Sachin Kulkarni, Director of Engineering at Facebook](https://www.youtube.com/watch?v=IO4teCbHvZw)
* [Scaling Low-latency Live Streams at Facebook (Latencies for Real-time Interactions) - Saral Shodhan, SDE at Facebook](https://atscaleconference.com/videos/scaling-low-latency-live-streams/)
* [Scaling Low-latency Live Streams at Facebook (End-to-End Considerations) - Federico Larumbe, SDE at Facebook](https://atscaleconference.com/videos/scaling-low-latency-live-streams-2-of-2/)
* [Scaling Real-time Infrastructure at Alibaba for Global Shopping Holiday - Xiaowei Jiang, Senior Director at Alibaba](https://atscaleconference.com/videos/scaling-alibabas-real-time-infrastructure-for-global-shopping-holiday/)
* [Scaling Load Balancing Infra to Support 1.3 Billion Users at Facebook - Patrick Shuff, Production Engineer at Facebook](https://www.youtube.com/watch?v=bxhYNfFeVF4)
* [Scaling (a NSFW site) to 200 Million Views A Day And Beyond - Eric Pickup, Lead Platform Developer at MindGeek](https://www.youtube.com/watch?v=RlkCdM_f3p4)
* [Beyond the Twelve-Factor App - Exploring the DNA of Highly Scalable, Resilient Cloud Applications (Free)](http://www.oreilly.com/webops-perf/free/beyond-the-twelve-factor-app.csp)
* [Chaos Engineering - Building Confidence in System Behavior through Experiments (Free)](http://www.oreilly.com/webops-perf/free/chaos-engineering.csp?intcmp=il-webops-free-product-na_new_site_chaos_engineering_text_cta)
Copyright Benny (Quoc-Binh) Nguyen, 2018. Licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). This small work is dedicated to [the wolves climbing the hill](https://www.youtube.com/watch?v=gMFc7agO09w).
