An updated and curated list of selected readings to illustrate best practices in building high scalability, high availability, high stability, high performance, and high intelligence large-scale systems. Concepts are explained in the articles of prominent engineers and credible references. Case studies are taken from battle-tested systems that serve millions to billions of users.
> Understand your problems: scalability problem (fast for a single user but slow under heavy load) or performance problem (slow for a single user) by reviewing some [design principles](#principle) and checking how [scalability](#scalability) and [performance](#performance) problems are solved at tech companies. The section of [intelligence](#intelligence) are created for those who work with data and machine learning at big (data) and deep (learning) scale.
> "Even if you lose all one day, you can build all over again if you retain your calm!" - Thuan Pham, CTO of Uber. So, keep calm and mind the [availability](#availability) and [stability](#stability) matters!
> Look at some [interview notes](#interview) and [real-world architectures with completed diagrams](#architecture) to get a comprehensive view before designing your system on whiteboard. You can check some [talks](#talk) of engineers from tech giants to know how they build, scale, and optimize their systems. There are some selected [books](#book) for you (most of them are free)! Good luck :four_leaf_clover:
> The goal of scaling team is not growing team size but increasing team output and value. You can find out how tech companies reach that goal in various aspects: hiring, management, organization, culture, and communication in the [organization](#organization) section.
> If you find this project helpful, please share on your chat groups, [on Twitter](https://ctt.ec/V8B2p), or [on Weibo](http://t.cn/RnjFLCB) so more people can be helped! Power is gained by sharing knowledge, not hoarding it. Thank you! :hibiscus:
* [Designs, Lessons and Advice from Building Large Distributed Systems - Jeff Dean, Google](https://www.cs.cornell.edu/projects/ladis2009/talks/dean-keynote-ladis2009.pdf)
* [Things to Keep in Mind When Building a Platform for the Enterprise - Heidi Williams, VP Platform at Box](https://blog.box.com/blog/4-things-to-keep-in-mind-when-building-a-platform-for-the-enterprise/)
* [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)
* [Mistakes to Avoid while Creating an Internal Product at Skyscanner](https://medium.com/@SkyscannerEng/9-mistakes-to-avoid-while-creating-an-internal-product-63d579b00b1a)
* [Learn from Mistakes at Reddit](http://highscalability.com/blog/2013/8/26/reddit-lessons-learned-from-mistakes-made-scaling-to-1-billi.html)
* [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)
* [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)
* [Docker Containers that Power Over 100.000 Online Shops at Shopify](https://shopifyengineering.myshopify.com/blogs/engineering/docker-at-shopify-how-we-built-containers-that-power-over-100-000-online-shops)
* [Scaling Live Streaming for Large Events (with Distributed Cache) at Hulu](https://medium.com/hulu-tech-blog/scaling-hulu-live-streaming-for-large-events-march-madness-and-beyond-bedd73874f2)
* [Application Data Caching from RAM to SSD at NetFlix](https://medium.com/netflix-techblog/evolution-of-application-data-caching-from-ram-to-ssd-a33d6fa7a690)
* [Distributed Tracing with Pintrace at Pinterest](https://medium.com/@Pinterest_Engineering/distributed-tracing-at-pinterest-with-new-open-source-tools-a4f8a5562f6b)
* [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/)
* [Aardvark and Repokid: AWS Least Privilege for Distributed, High-Velocity Development at Netflix](https://medium.com/netflix-techblog/introducing-aardvark-and-repokid-53b081bf3a7e)
* [LISA: Distributed Firewall at LinkedIn](https://www.slideshare.net/MikeSvoboda/2017-lisa-linkedins-distributed-firewall-dfw)
* [Distributed Security Alerting at Slack](https://slack.engineering/distributed-security-alerting-c89414c992d6)
* [Secure Infrastructure To Store Bitcoin In The Cloud at Coinbase](https://engineering.coinbase.com/how-coinbase-builds-secure-infrastructure-to-store-bitcoin-in-the-cloud-30a6504e40ba)
* [Active Directory Password Blacklisting at Yelp](https://engineeringblog.yelp.com/2018/04/ad-password-blacklisting.html)
* [Secure Infrastructure to Store Bitcoin in the Cloud at Coinbase](https://engineering.coinbase.com/how-coinbase-builds-secure-infrastructure-to-store-bitcoin-in-the-cloud-30a6504e40ba)
* [Athenz: Fine-Grained, Role-Based Access Control at Yahoo](https://yahooeng.tumblr.com/post/160481899076/open-sourcing-athenz-fine-grained-role-based)
* [Smart Monitoring System for Anomaly Detection on Business Trends at Alibaba](https://www.usenix.org/conference/srecon17asia/program/presentation/wang)
* [Diffy: Differencing Engine for Digital Forensics in the Cloud at Netflix](https://medium.com/netflix-techblog/netflix-sirt-releases-diffy-a-differencing-engine-for-digital-forensics-in-the-cloud-37b71abd2698)
* [Samza: Stream Processing System for Latency Insighs at LinkedIn](https://engineering.linkedin.com/blog/2018/04/samza-aeon--latency-insights-for-asynchronous-one-way-flows)
* [Bullet: Forward-Looking Query Engine for Streaming Data at Yahoo](https://yahooeng.tumblr.com/post/161855616651/open-sourcing-bullet-yahoos-forward-looking)
* [Qyu: Distributed Task Execution System for Complex Workflows at FindHotel](http://blog.findhotel.net/2018/03/qyu-a-distributed-task-execution-system-for-complex-workflows/)
* [Kafka the Message Broker](https://martin.kleppmann.com/papers/kafka-debull15.pdf)
* [When to use RabbitMQ or Kafka](https://content.pivotal.io/blog/understanding-when-to-use-rabbitmq-or-apache-kafka)
* [Kafka at Scale at LinkedIn](https://engineering.linkedin.com/kafka/running-kafka-scale)
* [Real-time Data Pipeline with Kafka at Yelp](https://engineeringblog.yelp.com/2016/07/billions-of-messages-a-day-yelps-real-time-data-pipeline.html)
* [Building Reliable Reprocessing and Dead Letter Queues with Kafka at Uber](https://eng.uber.com/reliable-reprocessing/)
* [Audit Kafka End-to-End at Uber](https://eng.uber.com/chaperone/)
* [Kafka for PaaS at Rakuten](https://techblog.rakuten.co.jp/2016/01/28/rakuten-paas-kafka/)
* [Publishing with Kafka at The New York Times](https://open.nytimes.com/publishing-with-apache-kafka-at-the-new-york-times-7f0e3b7d2077)
* [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)
* [Kafka in Socket Architecture (with a Comprehensive Comparison Table) at Trello](https://tech.trello.com/why-we-chose-kafka/)
* [Analytics Pipeline (Kafka, Dataflow, BigQuery) at Teads.tv](http://highscalability.com/blog/2018/4/9/give-meaning-to-100-billion-events-a-day-the-analytics-pipel.html)
* [Exactly-once Semantics are Possible: 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)
* [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)
* [Stop Using Shiny New Things and Love MySQL - Lesson at Pinterest](https://medium.com/@Pinterest_Engineering/learn-to-stop-using-shiny-new-things-and-love-mysql-3e1613c2ce14)
* [MySQL System Design at Booking.com](https://www.percona.com/live/mysql-conference-2015/sessions/bookingcom-evolution-mysql-system-design)
* [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)
* [Scaling MySQL at Wix](https://www.wix.engineering/single-post/scaling-to-100m-mysql-is-a-better-nosql)
* [MySQL Parallel Replication (4 parts) at Booking.com](https://medium.com/booking-com-infrastructure/evaluating-mysql-parallel-replication-part-4-annex-under-the-hood-eb456cf8b2fb)
* [Mitigating MySQL Replication Lag and Reducing Read Load at Github](https://githubengineering.com/mitigating-replication-lag-and-reducing-read-load-with-freno/)
* [Black-Box Auditing: Verifying End-to-End Replication Integrity between MySQL and Redshift at Yelp](https://engineeringblog.yelp.com/2018/04/black-box-auditing.html)
* [Partitioning Main MySQL Database at Airbnb](https://medium.com/airbnb-engineering/how-we-partitioned-airbnb-s-main-database-in-two-weeks-55f7e006ff21)
* [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 at Walmart](https://medium.com/walmartlabs/avoid-pitfalls-in-scaling-your-cassandra-cluster-lessons-and-remedies-a71ca01f8c04)
* [Storing Images in Cassandra at Walmart](https://medium.com/walmartlabs/building-object-store-storing-images-in-cassandra-walmart-scale-a6b9c02af593)
* [Benchmarking Cassandra Scalability on AWS at Netflix](https://medium.com/netflix-techblog/benchmarking-cassandra-scalability-on-aws-over-a-million-writes-per-second-39f45f066c9e)
* [Service Decomposition at Scale (with Cassandra) at Intuit QuickBooks](https://quickbooks-engineering.intuit.com/service-decomposition-at-scale-70405ac2f637)
* [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)
* [What is Time-Series Data & Why We Need a Time-Series Database](https://blog.timescale.com/what-the-heck-is-time-series-data-and-why-do-i-need-a-time-series-database-dcf3b1b18563)
* [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)
* [Practical Guide to Monitoring and Alerting with Time Series at Scale](https://www.usenix.org/conference/srecon17americas/program/presentation/wilkinson)
* [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)
* [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)
* [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)
* [Accelerator: Data Processing Framework with Fast Data Access and Parallel Execution at eBay](https://www.ebayinc.com/stories/blogs/tech/announcing-the-accelerator-processing-1-000-000-000-lines-per-second-on-a-single-computer/)
* [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 Repository at Microsoft](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)
* [Single Repository at Google](https://cacm.acm.org/magazines/2016/7/204032-why-google-stores-billions-of-lines-of-code-in-a-single-repository/fulltext)
* [Scaling Infrastructure and (Git) Workflow at Adyen](https://medium.com/adyen/from-0-100-billion-scaling-infrastructure-and-workflow-at-adyen-7b63b690dfb6)
* [Resilience Engineering with Project Waterbear at LinkedIn](https://engineering.linkedin.com/blog/2017/11/resilience-engineering-at-linkedin-with-project-waterbear)
* [Resiliency against Traffic Oversaturation at iHeartRadio](https://tech.iheart.com/resiliency-against-traffic-oversaturation-77c5ed92a5fb)
* [Resiliency in Distributed Systems at GO-JEK](https://blog.gojekengineering.com/resiliency-in-distributed-systems-efd30f74baf4)
* [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/)
* [Ensuring Resilience to Disaster at Quora](https://engineering.quora.com/Ensuring-Quoras-Resilience-to-Disaster)
* [Site Resiliency at Expedia](https://www.infoq.com/presentations/expedia-website-resiliency?utm_source=presentations_about_Case_Study&utm_medium=link&utm_campaign=Case_Study)
* [Every Day is Monday in Operations (11 parts) at LinkedIn ](https://www.linkedin.com/pulse/introduction-every-day-monday-operations-benjamin-purgason)
* [Architectural Patterns for High Availability - Adrian Cockcroft, Director of Architecture at Netflix](https://www.infoq.com/presentations/Netflix-Architecture)
* [Chubby: Lock Service for Loosely Coupled Distributed Systems at Google](https://blog.acolyer.org/2015/02/13/the-chubby-lock-service-for-loosely-coupled-distributed-systems/)
* [Scaling High-Availability Infrastructure in the Cloud at Twilio](https://www.twilio.com/engineering/2011/12/12/scaling-high-availablity-infrastructure-in-cloud)
* [Always Use Timeouts If Possible](https://www.javaworld.com/article/2824163/application-performance/stability-patterns-applied-in-a-restful-architecture.html)
* [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/)
* [Linux Performance Analysis in 60.000 Milliseconds at Netflix](https://medium.com/netflix-techblog/linux-performance-analysis-in-60-000-milliseconds-accc10403c55)
* [Performance Testing with SSDs (2 parts) at MailChimp](https://devs.mailchimp.com/blog/performance-testing-with-ssds-pt-2/)
* [Live Downsizing Google Cloud Persistent Disks (PD-SSD) at Mixpanel](https://engineering.mixpanel.com/2018/07/31/live-downsizing-google-cloud-pds-for-fun-and-profit/)
* [Server Side Rendering at Wix](https://www.youtube.com/watch?v=f9xI2jR71Ms)
* [30x Performance Improvements on MySQLStreamer at Yelp](https://engineeringblog.yelp.com/2018/02/making-30x-performance-improvements-on-yelps-mysqlstreamer.html)
* [Optimizing APIs through Dynamic Polyglot Runtime, Fully Asynchronous, and Reactive Programming at Netflix](https://medium.com/netflix-techblog/optimizing-the-netflix-api-5c9ac715cf19)
* [Performance Monitoring with Riemann and Clojure at Walmart](https://medium.com/walmartlabs/performance-monitoring-with-riemann-and-clojure-eafc07fcd375)
* [Profiling C++ (Part 1: Optimization, Part 2: Measurement and Analysis) at Riot Games](https://engineering.riotgames.com/news/profiling-optimisation)
* [Diagnosing Networking Issues in the Linux Kernel at Mixpanel](https://code.mixpanel.com/2015/03/26/diagnosing-networking-issues-in-the-linux-kernel/)
* [Hardware-Assisted Video Transcoding at Dailymotion](https://medium.com/dailymotion-engineering/hardware-assisted-video-transcoding-at-dailymotion-66cd2db448ae)
* [Performance Optimization by Tuning Garbage Collection](https://confluence.atlassian.com/enterprise/garbage-collection-gc-tuning-guide-461504616.html)
* [Garbage Collection Optimization for High-Throughput and Low-Latency Java Applications at LinkedIn](https://engineering.linkedin.com/garbage-collection/garbage-collection-optimization-high-throughput-and-low-latency-java-applications)
* [Analyzing V8 Garbage Collection Logs at Alibaba](https://www.linux.com/blog/can-nodejs-scale-ask-team-alibaba)
* [Python Garbage Collection for Dropping 50% Memory Growth Per Request at Instagram](https://instagram-engineering.com/copy-on-write-friendly-python-garbage-collection-ad6ed5233ddf)
* [Performance Impact of Removing Out of Band Garbage Collector (OOBGC) at Github](https://githubengineering.com/removing-oobgc/)
* [Using Java Large Heap (110 GB) at Expedia](https://techblog.expedia.com/2015/09/25/solving-problems-with-very-large-java-heaps/)
* [Optimizing 360 Photos at Scale at Facebook](https://code.facebook.com/posts/129055711052260/optimizing-360-photos-at-scale/)
* [Reducing Image File Size in the Photos Infrastructure at Etsy](https://codeascraft.com/2017/05/30/reducing-image-file-size-at-etsy/)
* [Improving GIF Performance at Pinterest](https://medium.com/@Pinterest_Engineering/improving-gif-performance-on-pinterest-8dad74bf92f1)
* [Optimizing Video Playback Performance at Pinterest](https://medium.com/@Pinterest_Engineering/optimizing-video-playback-performance-caf55ce310d1)
* [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/)
* [Boosting Site Speed Using Brotli Compression at LinkedIn](https://engineering.linkedin.com/blog/2017/05/boosting-site-speed-using-brotli-compression)
* [Improving Homepage Performance at Zillow](https://www.zillow.com/engineering/improving-homepage-performance/)
* [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/)
* [Big Data Processing at Uber](https://cdn.oreillystatic.com/en/assets/1/event/160/Big%20data%20processing%20with%20Hadoop%20and%20Spark%2C%20the%20Uber%20way%20Presentation.pdf)
* [Analytics Pipeline at Lyft](https://cdn.oreillystatic.com/en/assets/1/event/269/Lyft_s%20analytics%20pipeline_%20From%20Redshift%20to%20Apache%20Hive%20and%20Presto%20Presentation.pdf)
* [Big Data Analytics and ML Techniques at LinkedIn](https://cdn.oreillystatic.com/en/assets/1/event/269/Big%20data%20analytics%20and%20machine%20learning%20techniques%20to%20drive%20and%20grow%20business%20Presentation%201.pdf)
* [Self-Serve Reporting Platform on Hadoop at LinkedIn](https://cdn.oreillystatic.com/en/assets/1/event/137/Building%20a%20self-serve%20real-time%20reporting%20platform%20at%20LinkedIn%20Presentation%201.pdf)
* [Analytics Platform for Tracking Item Availability at Walmart](https://medium.com/walmartlabs/how-we-build-a-robust-analytics-platform-using-spark-kafka-and-cassandra-lambda-architecture-70c2d1bc8981)
* [Metacat: Making Big Data Discoverable and Meaningful at Netflix](https://medium.com/netflix-techblog/metacat-making-big-data-discoverable-and-meaningful-at-netflix-56fb36a53520)
* [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)
* [Concourse: Generating Personalized Content Notifications in Near-Real-Time at LinkedIn](https://engineering.linkedin.com/blog/2018/05/concourse--generating-personalized-content-notifications-in-near)
* [Altus Care: Applying a Chatbot to Platform Engineering at eBay](https://www.ebayinc.com/stories/blogs/tech/altus-care-apply-chatbot-to-ebay-platform-engineering/)
* [Box Graph: Spontaneous Social Network at Box](https://blog.box.com/blog/box-graph-how-we-built-spontaneous-social-network/)
* [PricingNet: Pricing Modelling with Neural Networks at Skyscanner](https://hackernoon.com/pricingnet-modelling-the-global-airline-industry-with-neural-networks-833844d20ea6)
* [Scaling Gradient Boosted Trees for Click-Through-Rate Prediction at Yelp](https://engineeringblog.yelp.com/2018/01/building-a-distributed-ml-pipeline-part1.html)
* [Deep Learning for Image Classification Experiment at Mercari](https://medium.com/mercari-engineering/mercaris-image-classification-experiment-using-deep-learning-9b4e994a18ec)
* [Deep Learning for Frame Detection in Product Images at Allegro](https://allegro.tech/2016/12/deep-learning-for-frame-detection.html)
* [Content-based Video Relevance Prediction at Hulu](https://medium.com/hulu-tech-blog/content-based-video-relevance-prediction-b2c448e14752)
* [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/)
* [Mapping the World of Music Using Machine Learning (2 parts) at iHeartRadio](https://tech.iheart.com/mapping-the-world-of-music-using-machine-learning-part-2-aa50b6a0304c)
* [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)
* [Improving Video Thumbnails with Deep Neural Nets at YouTube](https://youtube-eng.googleblog.com/2015/10/improving-youtube-video-thumbnails-with_8.html)
* [Quantile Regression for Delivering On Time at Instacart](https://tech.instacart.com/how-instacart-delivers-on-time-using-quantile-regression-2383e2e03edb)
* [Cross-Lingual End-to-End Product Search with Deep Learning at Zalando](https://jobs.zalando.com/tech/blog/search-deep-neural-network/)
* [Machine Learning at Jane Street](https://blog.janestreet.com/real-world-machine-learning-part-1/)
* [Large-Scale Machine Learning Pipeline for Job Recommendations at Indeed](http://engineering.indeedblog.com/blog/2016/04/building-a-large-scale-machine-learning-pipeline-for-job-recommendations/)
* [Atom Smashing using Machine Learning at CERN](https://cdn.oreillystatic.com/en/assets/1/event/144/Atom%20smashing%20using%20machine%20learning%20at%20CERN%20Presentation.pdf)
* [Map Pins with DBSCAN & Random Forests at Foursquare](https://engineering.foursquare.com/you-are-probably-here-better-map-pins-with-dbscan-random-forests-9d51e8c1964d)
* [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)
* [Real-time User Action Counting System for Ads at Pinterest](https://medium.com/@Pinterest_Engineering/building-a-real-time-user-action-counting-system-for-ads-88a60d9c9a)
* [Simone: Distributed Simulation Service at Netflix](https://medium.com/netflix-techblog/https-medium-com-netflix-techblog-simone-a-distributed-simulation-service-b2c85131ca1b)
* [Zuul: Edge Service for Dynamic Routing, Monitoring, Resiliency, Security, etc at Netflix](https://medium.com/netflix-techblog/open-sourcing-zuul-2-82ea476cb2b3)
* [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)
* [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/)
* [Reinventing the Trading Platform for Scale at Wealthsimple](https://medium.com/@Wealthsimple/engineering-at-wealthsimple-reinventing-our-trading-platform-for-scale-17e332241b6c)
* [Software Engineering Advice from Building Large-Scale Distributed Systems - Jeff Dean](https://static.googleusercontent.com/media/research.google.com/en//people/jeff/stanford-295-talk.pdf)
* [Anatomy of a System Design Interview](https://hackernoon.com/anatomy-of-a-system-design-interview-4cb57d75a53f)
* [8 Things You Need to Know Before a System Design Interview](http://blog.gainlo.co/index.php/2015/10/22/8-things-you-need-to-know-before-system-design-interviews/)
* [Top 10 System Design Interview Questions ](https://hackernoon.com/top-10-system-design-interview-questions-for-software-engineers-8561290f0444)
* [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)
* [Engineering Levels at SoundCloud](https://developers.soundcloud.com/blog/engineering-levels)
* [Scaling Engineering Teams at Twitter](https://www.youtube.com/watch?v=-PXi_7Ld5kU)
* [Scaling Decision-Making Across Teams at LinkedIn](https://engineering.linkedin.com/blog/2018/03/scaling-decision-making-across-teams-within-linkedin-engineering)
* [Scaling Data Science Team at GOJEK](https://blog.gojekengineering.com/the-dynamics-of-scaling-an-organisation-cb96dbe8aecd)
* [Scaling Agile at Zalando](https://jobs.zalando.com/tech/blog/scaling-agile-zalando/?gh_src=4n3gxh1)
* [Lessons Learned from Scaling a Product Team at Intercom](https://blog.intercom.com/how-we-build-software/)
* [Hiring, Managing, and Scaling Engineering Teams at Typeform](https://medium.com/@eleonorazucconi/toby-oliver-cto-typeform-on-hiring-managing-and-scaling-engineering-teams-86bef9e5a708)
* [Team Model for Scaling a Design System at Salesforce](https://medium.com/salesforce-ux/the-salesforce-team-model-for-scaling-a-design-system-d89c2a2d404b)
* [From 2 Founders to 1000 Employees at Transferwise](https://medium.com/transferwise-ideas/from-2-founders-to-1000-employees-how-a-small-scale-startup-grew-into-a-global-community-9f26371a551b)
* [Lessons Learned Growing a UX Team from 10 to 170 at Adobe](https://medium.com/thinking-design/lessons-learned-growing-a-ux-team-from-10-to-170-f7b47be02262)
* [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)
* [Solving Large-scale Data Center and Cloud Interconnection Problems - Ihab Tarazi, CTO at Equinix](https://atscaleconference.com/videos/solving-large-scale-data-center-and-cloud-interconnection-problems/)
* [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)
This repo is created and maintained by [Binh Nguyen](http://binhnguyennus.com/). Feel free to use it at your convenience! Thank you & Happy coding :heart:
