Scalable, Available, Stable, Performant, and Intelligent System Design Patterns

An updated and curated list of readings to illustrate best practices and patterns in building scalable, available, stable, performant, and intelligent 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.

If your system goes slow 🚥

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 and checking how scalability and performance problems are solved at tech companies. The section of intelligence are created for those who work with data and machine learning at big (data) and deep (learning) scale.

If your system goes down 🚧

"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 and stability matters!

If you are having a system design interview 🌊

Look at some interview notes and real-world architectures with completed diagrams to get a comprehensive view before designing your system on whiteboard. You can check some talks of engineers from tech giants to know how they build, scale, and optimize their systems. There are some selected books for you (most of them are free)! Good luck 🍀

If you are building your dream team 🎡

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 section.

Community power 🚠🚡🚠

Contributions are greatly welcome! You may want to take a look at the contribution guidelines.

If you find this project helpful, please share on your chat groups, on Twitter, or on Weibo so more people can be helped! Power is gained by sharing knowledge, not hoarding it. Thank you! 🌺

Contents

• Principle
• Scalability
• Availability
• Stability
• Performance
• Intelligence
• Architecture
• Interview
• Organization
• Talk
• Book

Principle

• Designs, Lessons and Advice from Building Large Distributed Systems - Jeff Dean, Google
• How To Design A Good API and Why it Matters - Joshua Bloch, CMU & Google
• On Efficiency, Reliability, Scaling - James Hamilton, VP at AWS
• Things to Keep in Mind When Building a Platform for the Enterprise - Heidi Williams, VP Platform at Box
• Principles of Chaos Engineering
• Finding the Order in Chaos
• The Twelve-Factor App
• Clean Architecture
• High Cohesion and Low Coupling
• CAP Theorem and Trade-offs
• CP Databases and AP Databases
• Stateless vs Stateful Scalability
• Scale Up vs Scale Out
• Scale Up vs Scale Out: Hidden Costs
• Best Practices for Scaling Out
• Best Practices for Continuous Delivery
• ACID and BASE
• Blocking/Non-Blocking and Sync/Async
• Performance and Scalability of Databases
• Database Isolation Levels and Effects on Performance and Scalability
• The Probability of Data Loss in Large Clusters
• SQL vs NoSQL
• SQL vs NoSQL - Lesson Learned at Salesforce
• NoSQL Databases: Survey and Decision Guidance
• How Sharding Works
• Consistent Hashing
• Consistent Hashing: Algorithmic Tradeoffs
• Don’t be tricked by the Hashing Trick
• Uniform Consistent Hashing at Netflix
• Eventually Consistent - Werner Vogels, CTO at Amazon
• Cache is King
• Anti-Caching
• Understand Latency
• Latency Numbers Every Programmer Should Know
• The Calculus of Service Availability
• Architecture Issues When Scaling Web Applications: Bottlenecks, Database, CPU, IO
• Common Bottlenecks
• Life Beyond Distributed Transactions
• Relying on Software to Redirect Traffic Reliably at Various Layers
• Breaking Things on Purpose
• Avoid Over Engineering
• Scalability Worst Practices
• Use Solid Technologies - Don’t Re-invent the Wheel - Keep It Simple!
• Simplicity by Distributing Complexity
• Why Over-Reusing is Bad
• Performance is a Feature
• Make Performance Part of Your Workflow
• The Benefits of Server Side Rendering over Client Side Rendering
• Writing Code that Scales
• Automate and Abstract: Lessons at Facebook
• AWS Do's and Don'ts
• (UI) Design Doesn’t Scale - Stanley Wood, Design Director at Spotify
• Linux Performance
• Building Fast and Resilient Web Applications - Ilya Grigorik
• Accept Partial Failures, Minimize Service Loss
• RACI (Responsible, Accountable, Consulted, Informed) at Etsy
• Design for Loose-coupling
• Design for Resiliency
• Design for Self-healing
• Design for Scaling Out
• Design for Evolution
• Mistakes to Avoid while Creating an Internal Product at Skyscanner
• Learn from Mistakes at Reddit
• Code Review Best Practices at Palantir

Scalability

• Microservices and Orchestration
  • Microservices Resource Guide - Martin Fowler, Chief Scientist at ThoughtWorks
  • Microservices Patterns
  • Advantages and Drawbacks of Microservices
  • Microservices Scale Cube
  • Thinking Inside the Container (8 parts) at Riot Games
  • Containerization at Pinterest
  • Techniques for Splitting Up a Codebase into Microservices and Artifacts at LinkedIn
  • The Evolution of Container Usage at Netflix
  • Dockerizing MySQL at Uber
  • Testing of Microservices at Spotify
  • Organize Monolith Before Breaking it into Services at Weebly
  • Lessons learned running Docker in production at Treehouse
  • Inside a SoundCloud Microservice
  • Operate Kubernetes Reliably at Stripe
  • Kubernetes Traffic Routing (2 parts) at Rakuten
  • Agrarian-Scale Kubernetes (3 parts) at New York Times
  • Nanoservices at BBC Online
  • PowerfulSeal: Testing Tool for Kubernetes Clusters at Bloomberg
  • Conductor: Microservices Orchestrator at Netflix
  • Making 10x Improvement in Release Times with Docker and Amazon ECS at Nextdoor
  • K8Guard: Auditing System for Kubernetes Clusters at Target.com
  • Deconstructing Monolithic Applications into (Kafka-driven) Services at Heroku
  • Docker Containers that Power Over 100.000 Online Shops at Shopify
• Distributed Caching
  • Read-Through, Write-Through, Write-Behind, and Refresh-Ahead Caching
  • Eviction Policy and Expiration Policy
  • EVCache: Caching for a Global Netflix
  • Memsniff: Robust Memcache Traffic Analyzer at Box
  • Caching with Consistent Hashing and Cache Smearing at Etsy
  • Analysis of Photo Caching at Facebook
  • Cache Efficiency Exercise at Facebook
  • tCache: Scalable Data-aware Java Caching at Trivago
  • Reduce Memcached Memory Usage by 50% at Trivago
  • Caching Internal Service Calls at Yelp
  • Scaling Live Streaming for Large Events (with Distributed Cache) at Hulu
  • Estimating the Cache Efficiency using Big Data at Allegro
  • Caching (with Hashing) at Zenefits
  • Distributed Cache (Akka, Kubernetes) at Zalando
  • Application Data Caching from RAM to SSD at NetFlix
  • Tradeoffs of Replicated Cache at Skyscanner
• Distributed Tracking and Tracing
  • Zipkin: Distributed Systems Tracing at Twitter
  • Canopy: Scalable Distributed Tracing & Analysis at Facebook
  • Pintrace: Distributed Tracing at Pinterest
  • Real-time Distributed Tracing at LinkedIn
  • Tracking Service Infrastructure at Scale at Shopify
  • Distributed Tracing at HelloFresh
  • Analyzing Distributed Trace Data at Pinterest
  • Distributed Tracing at Uber
  • JVM Profiler: Tracing Distributed JVM Applications at Uber
  • Data Checking at Dropbox
  • Tracing Distributed Systems at Showmax
  • osquery Across the Enterprise at Palantir
• Distributed Logging
  • The Problem with Logging - Jeff Atwood
  • The Log: What Every Software Engineer Should Know
  • Using Logs to Build a Solid Data Infrastructure - Martin Kleppmann
  • Scalable and Reliable Log Ingestion at Pinterest
  • Building DistributedLog at Twitter: High-performance replicated log service
  • Logging Service with Spark at CERN Accelerator
  • Logging and Aggregation at Quora
  • BookKeeper: Distributed Log Storage at Yahoo
  • LogDevice: Distributed Data Store for Logs at Facebook
  • LogFeeder: Log Collection System at Yelp
  • Collection and Analysis of Daemon Logs at Badoo
• Distributed Security (Monitoring, Authentication, etc)
  • Approach to Security at Scale at Dropbox
  • Aardvark and Repokid: AWS Least Privilege for Distributed, High-Velocity Development at Netflix
  • LISA: Distributed Firewall at LinkedIn
  • Distributed Security Alerting at Slack
  • Secure Infrastructure To Store Bitcoin In The Cloud at Coinbase
  • BinaryAlert: Real-time Serverless Malware Detection at Airbnb
  • Scalable IAM Architecture to Secure Access to 100 AWS Accounts at Segment
  • OAuth Audit Toolbox at Indeed
  • Active Directory Password Blacklisting at Yelp
  • Secure Infrastructure to Store Bitcoin in the Cloud at Coinbase
  • Syscall Auditing at Scale at Slack
  • Athenz: Fine-Grained, Role-Based Access Control at Yahoo
  • WebAuthn Support for Secure Sign In at Dropbox
  • Job-based Forecasting Workflow for Observability Anomaly Detection at Uber
  • Alibaba Monitoring System
  • Smart Monitoring System for Anomaly Detection on Business Trends at Alibaba
  • Security Development Lifecycle (SDL) at Slack
  • Unprivileged Container Builds at Kinvolk
  • Diffy: Differencing Engine for Digital Forensics in the Cloud at Netflix
  • Detecting Credential Compromise in AWS at Netflix
  • Scalable User Privacy at Spotify
• Distributed Messaging, Queuing, and Event Streaming
  • Scaling Push Messaging for Millions of Devices at Netflix
  • Samza: Stream Processing System for Latency Insighs at LinkedIn
  • Delaying Asynchronous Message Processing with RabbitMQ at Indeed
  • Bullet: Forward-Looking Query Engine for Streaming Data at Yahoo
  • EventHorizon: Tool for Watching Events Streaming at Etsy
  • Benchmarking Streaming Computation Engines at Yahoo
  • Cherami: Message Queue System for Transporting Async Tasks at Uber
  • Qyu: Distributed Task Execution System for Complex Workflows at FindHotel
  • Messaging Service at Riot Games
  • Event Stream Analytics with Druid (Search Engine meet Column DB) at Walmart
  • Debugging Production with Event Logging at Zillow
  • Kafka the Message Broker
    • When to use RabbitMQ or Kafka
    • Kafka at Scale at LinkedIn
    • Real-time Data Pipeline with Kafka at Yelp
    • Building Reliable Reprocessing and Dead Letter Queues with Kafka at Uber
    • Audit Kafka End-to-End at Uber
    • Kafka for PaaS at Rakuten
    • Publishing with Kafka at The New York Times
    • Kafka Streams on Heroku
    • Kafka in Platform Events Architecture at Salesforce
    • Kafka in Socket Architecture (with a Comprehensive Comparison Table) at Trello
    • Analytics Pipeline (Kafka, Dataflow, BigQuery) at Teads.tv
  • Data Deduplication Techniques
    • Exactly-once Semantics are Possible: How Kafka Does it
    • Real-time Deduping at Scale with Kafka-based Pipleline at Tapjoy
    • Delivering Billions of Messages Exactly Once: Deduping at Segment
    • Deduplication For Efficient Storage (From 50 PB To 32 PB) At Mail.Ru
• Distributed Searching
  • Search Architecture of Instagram
  • Search Architecture of eBay
  • Improving Search Engine Efficiency by over 25% at eBay
  • Indexing and Querying Telemetry Logs with Lucene at Palantir
  • Search Federation Architecture at LinkedIn (2018)
  • Search at Slack
  • Search and Recommendations at DoorDash
  • Search Service at Twitter (2014)
  • Autocomplete Search (2 parts) at Traveloka
  • Nautilus: Travel Search Engine of Expedia
  • Nautilus: Search Engine at Dropbox
  • Galene: Search Architecture of LinkedIn
  • Manas: High Performing Customized Search System at Pinterest
  • Sherlock: Near Real Time Search Indexing at Flipkart
  • Nebula: Storage Platform to Build Search Backends at Airbnb
  • ELK (Elasticsearch, Logstash, Kibana) Stack
    • Predictions in Real Time with ELK at Uber
    • Scaling Elasticsearch Clusters at Uber
    • Elasticsearch Performance Tuning Practice at eBay
    • Elasticsearch at Kickstarter
    • Distributed Troubleshooting Platform with ELK Stack at Target.com
    • ELK at Robinhood
    • Log Parsing with Logstash and Google Protocol Buffers at Trivago
    • Fast Order Search using Data Pipeline and Elasticsearch at Yelp
    • Sharding out Elasticsearch at Vinted
• Distributed Storage
  • In-memory Storage
    • Introduction to In-memory Data - Viktor Gamov, Solutions Architect at Hazelcast
    • MemSQL Architecture - The Fast (MVCC, InMem, LockFree, CodeGen) And Familiar (SQL)
    • Optimizing Memcached Efficiency at Quora
    • Real-Time Data Warehouse with MemSQL on Cisco UCS
    • Moving to MemSQL (with Horizontally Scalable, ACID Compliant, MySQL Compatibility) at Tapjoy
    • MemSQL and Kinesis for Real-time Insights at Disney-ABC TV
  • Durable Storage (S3, HDFS)
    • Scaling HDFS at Uber
    • Reasons for Choosing S3 over HDFS at Databricks
    • Quantcast File System on Amazon S3
    • Data Sink with S3 at Deliveroo
    • Using S3 in Netflix Chukwa
    • Yahoo Cloud Object Store - Object Storage at Exabyte Scale
    • Ambry: Distributed Immutable Object Store at LinkedIn
    • Hammerspace: Persistent, Concurrent, Off-heap Storage at Airbnb
• Relational Databases (MySQL, MSSQL, PostgreSQL)
  • Stop Using Shiny New Things and Love MySQL - Lesson at Pinterest
  • Microsoft SQL versus MySQL
  • SQL Database Performance Tuning
  • Scaling PostgreSQL Using CUDA
  • Scaling Distributed Joins
  • MySQL System Design at Booking.com
  • PostgreSQL at Twitch
  • Scaling MySQL-based Financial Reporting System at Airbnb
  • Scaling MySQL at Wix
  • MaxScale (MySQL) Database Proxy at Airbnb
  • Switching from Postgres to MySQL at Uber
  • Handling Growth with Postgres at Instagram
  • Scaling the Analytics Database (Postgres) at TransferWise
  • Updating a 50 Terabyte PostgreSQL Database at Adyen
  • Replication
    • MySQL Parallel Replication (4 parts) at Booking.com
    • Mitigating MySQL Replication Lag and Reducing Read Load at Github
    • Black-Box Auditing: Verifying End-to-End Replication Integrity between MySQL and Redshift at Yelp
    • Monitoring MySQL Delayed Replication at IMVU
    • Partitioning Main MySQL Database at Airbnb
    • Herb: Multi-DC Replication Engine for Schemaless Datastore at Uber
  • Sharding (Horizontal Partitioning)
    • Sharding MySQL at Pinterest
    • Sharding MySQL at MailChimp
    • Sharding MySQL at Twilio
    • Sharding MySQL (3 parts) at Evernote
    • Sharding Layer of Schemaless Datastore at Uber
    • Sharding & IDs at Instagram
    • Solr: Improving Performance for Batch Indexing at Box
• NoSQL Databases
  • Key-Value Databases (DynamoDB, Voldemort, Manhattan)
    • Scaling Mapbox infrastructure with DynamoDB Streams
    • Manhattan: Twitter’s distributed key-value database
    • Sherpa: Yahoo’s distributed NoSQL key-value store
    • Riak inside Chat Service Architecture at Riot Games
    • MPH: Fast and Compact Immutable Key-Value Stores at Indeed
    • zBase: High Performance, Elastic, Distributed Key-Value Store at Zynga
    • Venice: Distributed Key-Value Database at Linkedin
    • DynamoDB Hot Shards at Segment
  • Columnar Databases (Cassandra, HBase, Redshift)
    • Consistent Hashing in Cassandra
    • Understanding Gossip (Cassandra Internals)
    • When NOT to use Cassandra?
    • Avoid Pitfalls in Scaling Cassandra Cluster at Walmart
    • Storing Images in Cassandra at Walmart
    • Cassandra at Instagram
    • Scale Ad Analytics with Cassandra at Yelp
    • Store Billions of Messages with Cassandra at Discord
    • Scale to 100+ Million Reads/Writes using Spark and Cassandra at Dream11
    • Moving Food Feed from Redis to Cassandra at Zomato
    • Benchmarking Cassandra Scalability on AWS at Netflix
    • Imgur Notification: From MySQL to HBASE at Imgur
    • Improving HBase Backup Efficiency at Pinterest
    • HBase Practice at Xiaomi
    • ClickHouse - Open Source Distributed Column Database at Yandex
    • Scaling Redshift without Scaling Costs at GIPHY
    • Service Decomposition at Scale (with Cassandra) at Intuit QuickBooks
    • Cassandra for Keeping Counts In Sync at SoundCloud
    • cstar: Cassandra Orchestration Tool at Spotify
  • Document Databases (MongoDB, SimpleDB, CouchDB)
    • eBay: Building Mission-Critical Multi-Data Center Applications with MongoDB
    • MongoDB at Baidu: Multi-Tenant Cluster Storing 200+ Billion Documents across 160 Shards
    • Migrating Mongo Data at Addepar
    • The AWS and MongoDB Infrastructure of Parse (acquired by Facebook)
    • Migrating Mountains of Mongo Data at Addepar
    • Couchbase Ecosystem at LinkedIn
    • SimpleDB at Zendesk
    • Espresso: Distributed Document Store at LinkedIn
  • Graph Databases
    • Handling Billions of Edges in a Graph Database
    • Neo4j case studies with Walmart, eBay, AirBnB, NASA, etc
    • FlockDB: Distributed Graph Database for Storing Adjancency Lists at Twitter
    • JanusGraph: Scalable Graph Database backed by Google, IBM and Hortonworks
    • Amazon Neptune
  • Datastructure Databases (Redis, Hazelcast)
    • Using Redis To Scale at Twitter
    • Scaling Job Queue with Redis at Slack
    • Moving persistent data out of Redis at Github
    • Storing Hundreds of Millions of Simple Key-Value Pairs in Redis at Instagram
    • Redis in Chat Architecture of Twitch (from 27:22)
    • Learn Redis the hard way (in production) at Trivago
    • Optimizing Session Key Storage in Redis at Deliveroo
    • Optimizing Redis Storage at Deliveroo
    • Memory Optimization in Redis at Wattpad
    • Sending an e-mail to millions of users (with Redis) at Drivy
    • Redis Fleet at Heroku
• Time Series Databases (TSDB)
  • What is Time-Series Data & Why We Need a Time-Series Database
  • Time Series Data: Why and How to Use a Relational Database instead of NoSQL
  • Practical Guide to Monitoring and Alerting with Time Series at Scale
  • Beringei: High-performance Time Series Storage Engine at Facebook
  • Atlas: In-memory Dimensional Time Series Database at Netflix
  • Heroic: Time Series Database at Spotify
  • Roshi: Distributed Storage System for Time-Series Event at SoundCloud
  • Building a Scalable Time Series Database on PostgreSQL
  • Scaling Time Series Data Storage at Netflix
• HTTP Caching (Reverse Proxy, CDN)
  • Reverse Proxy (Nginx, Varnish, Squid, rack-cache)
  • Stop Worrying and Love the Proxy
  • Playing HTTP Tricks with Nginx
  • Using CDN to Improve Site Performance at Coursera
  • Strategy: Caching 404s Saved 66% On Server Time at The Onion
  • Increasing Application Performance with HTTP Cache Headers
  • Zynga Geo Proxy: Reducing Mobile Game Latency at Zynga
  • Google AMP at Condé Nast
  • Running A/B Tests on Hosting Infrastructure (CDNs) at Deliveroo
  • HAProxy with Kubernetes for User-facing Traffic at SoundCloud
  • Bandaid: Service Proxy at Dropbox
  • CDN in LIVE's Encoder Layer at LINE
• Load Balancing
  • Introduction to Modern Network Load Balancing and Proxying
  • Load Balancing infrastructure to support more than 1.3 billion users at Facebook
  • DHCPLB: DHCP Load Balancer at Facebook
  • Katran: Scalable Network Load Balancer at Facebook
  • Load Balancing with Eureka at Netflix
  • Edge Load Balancing at Netflix
  • Zuul 2: Cloud Gateway at Netflix
  • Load Balancing at Yelp
  • Load Balancing at Github
  • Consistent Hashing to Improve Load Balancing at Vimeo
  • UDP Load Balancing at 500 pixel
  • QALM: QoS Load Management Framework at Uber
  • Traffic Steering using Rum DNS at LinkedIn
• Rate Limiting
  • Rate Limiting for Scaling to Millions of Domains at Cloudfare
  • Cloud Bouncer: Distributed Rate Limiting at Yahoo
  • Scaling API with Rate Limiters at Stripe
  • Rate Limiting at Etsy
  • Rate Limiter at BloomReach
  • Distributed Rate Limiting at Allegro
  • Ratequeue: Core Queueing-And-Rate-Limiting System at Twilio
• Autoscaling
  • A Horror Movie Featuring Auto Scaling Groups, EBS Volumes, Terraform, and Bash
  • Autoscaling Pinterest
  • Autoscaling Based on Request Queuing at Square
  • Autoscaling Applications at PayPal
  • Autoscaling Jenkins at Trivago
  • Scryer: Predictive Auto Scaling Engine at Netflix
  • Automated Infrastructure Cost Optimization at Scale with AWS EC2 RI at Quora
• Concurrency
  • Message-Passing Concurrency
  • Software Transactional Memory
  • Dataflow Concurrency
  • Shared-State Concurrency
  • Concurrency series by Larry Osterman (Principal SDE at Microsoft)
    • Part 8 – Concurrency for scalability
    • Part 9 - APIs that enable scalable programming
    • Part 10 - How do you know if you’ve got a scalability issue?
    • Part 11 – Hidden scalability issues
    • Part 12 – Hidden scalability issues (cont)
  • Concurrency with Erlang
    • Erlang in WhatsApp
    • Erlang in Riot Chat Server
    • How Discord Scaled Elixir to Five Millions Concurrent Users
    • Mnesia: A Distributed DBMS Rooted in Concurrency
    • Mesia and CAP
  • Running Concurrent Queries in GoSocial (Go and Neo4j) at Medium
  • The Secret To 10 Million Concurrent Connections
• Parallel Computing
  • SPMD (Single Program Multiple Data): The Genetic Pattern
  • Master/Worker Pattern
  • Loop Parallelism Pattern: Extracting parallel tasks from loops
  • Fork/Join Pattern: Good for recursive data processing
  • Map-Reduce: Born for Simplified Data Processing on Large Clusters
  • On the Death of Map-Reduce - Henry Robinson, Cloudera
  • Server-side Optimization to Parallelize the Rendering of Web Pages at Yelp
  • Accelerator: Data Processing Framework with Fast Data Access and Parallel Execution at eBay
• Event-Driven Architecture
  • Pub-Sub Messaging
    • Autoscaling Pub-Sub Consumers at Spotify
    • Pulsar: Pub-Sub Messaging at Scale at Yahoo
    • Wormhole: Pub-Sub system at Facebook (2013)
    • Pub-Sub in Chatting Architecture at LINE
  • Domain Events
    • Domain Events: Simple and Reliable Solution
    • Domain-Driven Design in Organizing Monolith Before Breaking it into Services at Weebly
    • Modelling for Domain Driven Design at Moonpig
  • Event Sourcing
    • Event Sourced Architectures for High Availability
    • Event Sourcing and Stream Processing at Scale
    • Scaling Event Sourcing for Netflix Downloads
    • Scaling Event-Sourcing at Jet.com
    • Event Sourcing (2 parts) at eBay
  • Command & Query Responsibility Segregation (CQRS)
    • Exploring CQRS and Event Sourcing - MSDN (with free ebook)
    • CQRS Simple Architecture
    • Building Scalable Applications Using Event Sourcing and CQRS with Kafka
  • Stream Processing, Event Sourcing, Reactive, CEP, etc - Martin Kleppmann
    • Point-To-Point and Its Differences from Pub-Sub
    • Store-Forward
    • Request-Reply
    • Enterprise Service Bus
• Distributed Source Code, Dependencies, and Configurations Management
  • Distributed Version Control Systems: A Not-So-Quick Guide Through
  • DGit: Distributed Git at Github
  • Stemma: Distributed Git Server at Palantir
  • Configuration Management for Distributed Systems at Flickr
  • Git Repository at Microsoft
  • How Microsoft Solved Git’s Problem with Large Repositories
  • Single Repository at Google
  • Scaling Infrastructure and (Git) Workflow at Adyen
  • Dotfiles Distribution at Booking.com
  • Secret Detector: Preventing Secrets in Source Code at Yelp
  • Managing Software Dependency at Scale at LinkedIn
  • ZooKeeper at Twitter

Availability

• Resilience Engineering: Learning to Embrace Failure
  • Resilience Engineering with Project Waterbear at LinkedIn
  • Resiliency against Traffic Oversaturation at iHeartRadio
  • Resiliency in Distributed Systems at GO-JEK
  • Practical NoSQL Resilience Design Pattern for the Enterprise at eBay
  • Ensuring Resilience to Disaster at Quora
  • Resilience at Shopify
  • Site Resiliency at Expedia
• Failover
  • The Evolution of Global Traffic Routing and Failover
  • Testing for Disaster Recovery Failover Testing
  • Designing a Microservices Architecture for Failure
  • ELB for Automatic Failover at GoSquared
  • Eliminate the Database for Higher Availability at American Express
  • Failover with Redis Sentinel at Vinted
  • High-availability SaaS Infrastructure at FreeAgent
• Availability in Globally Distributed Storage Systems
• NodeJS High Availability at Yahoo
• Every Day is Monday in Operations (11 parts) at LinkedIn
• How Robust Monitoring Powers High Availability for LinkedIn Feed
• Architectural Patterns for High Availability - Adrian Cockcroft, Director of Architecture at Netflix
• Supporting Global Events at Facebook
• Backends High Availability at BlaBlaCar
• Chubby: Lock Service for Loosely Coupled Distributed Systems at Google
• Tips for High Availability at Netflix
• Scaling High-Availability Infrastructure in the Cloud at Twilio

Stability

• Circuit Breaker
  • Circuit Breaking in Distributed Systems
  • Circuit Breakers for Distributed Services at LINE
  • Applying Circuit Breaker to Channel Gateway at LINE
  • Lessons in Resilience at SoundCloud
  • Circuit Breaker for Scaling Containers
  • Protector: Circuit Breaker for Time Series Databases at Trivago
  • Improved Production Stability with Circuit Breakers at Heroku
  • Circuit Breakers at Zendesk
  • Circuit Breakers at Traveloka
• Always Use Timeouts If Possible
• Crash Early: Better Error Now Than Response Tomorrow
• Fault Tolerance (Timeouts and Retries, Thread Separation, Semaphores, Circuit Breakers) at Neflix
• Crash-safe Replication for MySQL at Booking.com
• Bulkheads: Partition and Tolerate Failure in One Part
• Steady State: Always Put Logs on Separate Disk
• Throttling: Maintain a Steady Pace
• Multi-Clustering: Improving Resiliency and Stability of a Large-scale Monolithic API Service at LinkedIn
• Determinism (4 parts) in League of Legends Server

Performance

• Performance Optimization on OS, Storage, Database, Network
  • Improving Performance with Background Data Prefetching at Instagram
  • Compression Techniques to Solve Network I/O Bottlenecks at eBay
  • Optimizing Web Servers for High Throughput and Low Latency at Dropbox
  • Linux Performance Analysis in 60.000 Milliseconds at Netflix
  • Performance Testing with SSDs (2 parts) at MailChimp
  • Live Downsizing Google Cloud Persistent Disks (PD-SSD) at Mixpanel
  • Decreasing RAM Usage by 40% Using jemalloc with Python & Celery at Zapier
  • Reducing Memory Footprint at Slack
  • Performance Improvements at Pinterest
  • Server Side Rendering at Wix
  • 30x Performance Improvements on MySQLStreamer at Yelp
  • Optimizing APIs through Dynamic Polyglot Runtime, Fully Asynchronous, and Reactive Programming at Netflix
  • Performance Monitoring with Riemann and Clojure at Walmart
  • Performance Tracking Dashboard for Live Games at Zynga
  • Optimizing CAL Report Hadoop MapReduce Jobs at eBay
  • Performance Tuning on Quartz Scheduler at eBay
  • Profiling C++ (Part 1: Optimization, Part 2: Measurement and Analysis) at Riot Games
  • Diagnosing Networking Issues in the Linux Kernel at Mixpanel
  • Hardware-Assisted Video Transcoding at Dailymotion
• Performance Optimization by Tuning Garbage Collection
  • Garbage Collection Optimization for High-Throughput and Low-Latency Java Applications at LinkedIn
  • Analyzing V8 Garbage Collection Logs at Alibaba
  • Python Garbage Collection for Dropping 50% Memory Growth Per Request at Instagram
  • Performance Impact of Removing Out of Band Garbage Collector (OOBGC) at Github
  • Using Java Large Heap (110 GB) at Expedia
  • Debugging Java Memory Leaks at Allegro
  • Optimizing JVM at Alibaba
• Performance Optimization on Video, Image, Page Load
  • Optimizing 360 Photos at Scale at Facebook
  • Reducing Image File Size in the Photos Infrastructure at Etsy
  • Improving GIF Performance at Pinterest
  • Optimizing Video Playback Performance at Pinterest
  • Optimizing Video Stream for Low Bandwidth with Dynamic Optimizer at Netflix
  • Adaptive Video Streaming at YouTube
  • Reducing Video Loading Time by Prefetching during Preroll at Dailymotion
  • Boosting Site Speed Using Brotli Compression at LinkedIn
  • Improving Homepage Performance at Zillow
  • The Process of Optimizing for Client Performance at Expedia

Intelligence

• Big Data
  • Data Platform at Netflix
  • Data Platform at Flipkart
  • Data Pipeline Management Platform at Khan Academy
  • Data Infrastructure at Airbnb
  • Data Infrastructure at LinkedIn
  • Data Infrastructure at GO-JEK
  • Data Ingestion Infrastructure at Pinterest
  • Data Analytics Architecture at Pinterest
  • Big Data Processing (2 parts) at Spotify
  • Big Data Processing at Uber
  • Analytics Pipeline at Lyft
  • ML Data Pipelines for Real-Time Fraud Prevention at PayPal
  • Big Data Analytics and ML Techniques at LinkedIn
  • Self-Serve Reporting Platform on Hadoop at LinkedIn
  • Analytics Platform for Tracking Item Availability at Walmart
  • HALO: Hardware Analytics and Lifecycle Optimization at Facebook
  • RBEA: Real-time Analytics Platform at King
  • Gimel: Analytics Data Processing Platform at PayPal
  • AthenaX: Streaming Analytics Platform at Uber
  • Keystone: Real-time Stream Processing Platform at Netflix
  • Databook: Turning Big Data into Knowledge with Metadata at Uber
  • Maze: Funnel Visualization Platform at Uber
  • Metacat: Making Big Data Discoverable and Meaningful at Netflix
  • SpinalTap: Change Data Capture System at Airbnb
  • TensorFlowOnSpark: Distributed Deep Learning on Big Data Clusters at Yahoo
  • CaffeOnSpark: Distributed Deep Learning on Big Data Clusters at Yahoo
  • Experimentation Platform at Airbnb
  • Smart Product Platform at Zalando
  • Log Analysis Platform at LINE
  • Data Visualisation Platform at Myntra
• Distributed Machine Learning
  • Michelangelo: Machine Learning Platform at Uber
  • Horovod: Open Source Distributed Deep Learning Framework for TensorFlow at Uber
  • COTA: Improving Customer Care with NLP & Machine Learning at Uber
  • Repo-Topix: Topic Extraction Framework at Github
  • Concourse: Generating Personalized Content Notifications in Near-Real-Time at LinkedIn
  • Altus Care: Applying a Chatbot to Platform Engineering at eBay
  • Box Graph: Spontaneous Social Network at Box
  • PricingNet: Pricing Modelling with Neural Networks at Skyscanner
  • Scaling Gradient Boosted Trees for Click-Through-Rate Prediction at Yelp
  • Learning with Privacy at Scale at Apple
  • Deep Learning for Image Classification Experiment at Mercari
  • Deep Learning for Frame Detection in Product Images at Allegro
  • Content-based Video Relevance Prediction at Hulu
  • Training ML Models with Airflow and BigQuery at WePay
  • Improving Photo Selection With Deep Learning at TripAdvisor
  • Machine Learning (2 parts) at Condé Nast
  • Machine Learning Applications In The E-commerce Domain (4 parts) at Rakuten
  • Mapping the World of Music Using Machine Learning (2 parts) at iHeartRadio
  • Venue Rating System at Foursquare
  • Using Machine Learning to Improve Streaming Quality at Netflix
  • Improving Video Thumbnails with Deep Neural Nets at YouTube
  • Quantile Regression for Delivering On Time at Instacart
  • Cross-Lingual End-to-End Product Search with Deep Learning at Zalando
  • Machine Learning at Jane Street
  • Machine Learning for Ranking Answers End-to-End at Quora
  • Clustering Similar Stories Using LDA at Flipboard
  • Similarity Search at Flickr
  • Large-Scale Machine Learning Pipeline for Job Recommendations at Indeed
  • Deep Learning from Prototype to Production at Taboola
  • Atom Smashing using Machine Learning at CERN
  • Mapping Tags at Medium
  • Clustering with the Dirichlet Process Mixture Model in Scala at Monsanto
  • Map Pins with DBSCAN & Random Forests at Foursquare
  • Detecting and Preventing Fraud at Uber
  • Forecasting at Uber
  • Financial Forecasting at Uber
  • Productionizing ML with Workflows at Twitter
  • GUI Testing Powered by Deep Learning at eBay
  • Scaling Machine Learning to Recommend Driving Routes at Pivotal
  • Real-Time Predictions at DoorDash
  • Real-Time News Alerting at Bloomberg
  • Machine Intelligence at Dropbox
  • Modeling User Journeys via Semantic Embeddings at Etsy
  • Automated Fake Account Detection at LinkedIn

Architecture

• Systems We Make
• Tech Stack (2 parts) at Uber
• Tech Stack at Medium
• Tech Stack at Shopify
• Services (2 parts) at Airbnb
• Back-end at LinkedIn
• Back-end at Flickr
• Real-time Presence Platform at LinkedIn
• Real-time User Action Counting System for Ads at Pinterest
• API Platform at Riot Games
• Games Platform at The New York Times
• Simone: Distributed Simulation Service at Netflix
• Seagull: Distributed System that Helps Running > 20 Million Tests Per Day at Yelp
• MySQL Realtime Traffic Emulator at KakaoTalk
• Architecture of Sticker Services at LINE
• Stack Overflow Enterprise at Palantir
• Distributed Cron at Quora
• Architectures of Finance and Banking Systems
  • Reference Architecture For The Open Banking Standard
  • Building a Modern Bank Backend at Monzo
  • Reinventing the Trading Platform for Scale at Wealthsimple
  • Architecture for Core Banking System at Margo Bank
  • Architecture of Nubank
  • Tech Stack at TransferWise
  • Tech Stack at Addepar

Interview

• Designing Large-Scale Systems
  • My Scaling Hero - Jeff Atwood (a dose of Endorphins before your interview, JK)
  • Software Engineering Advice from Building Large-Scale Distributed Systems - Jeff Dean
  • Introduction to Architecting Systems for Scale
  • Anatomy of a System Design Interview
  • 8 Things You Need to Know Before a System Design Interview
  • Top 10 System Design Interview Questions
  • Top 10 Common Large-Scale Software Architectural Patterns in a Nutshell
  • Cloud Big Data Design Patterns - Lynn Langit
  • How NOT to design Netflix in your 45-minute System Design Interview?
• Explaining Low-Level Systems (OS, Network/Protocol, Database, Storage)
  • OSI and TCP/IP Cheat Sheet
  • The Precise Meaning of I/O Wait Time in Linux
  • Paxos Made Live – An Engineering Perspective
  • How to do Distributed Locking
  • SQL Transaction Isolation Levels Explained
• "What Happens When... and How" Questions
  • What Happens When You Type google.com into Browser and Press Enter?
  • Netflix: What Happens When You Press Play?
  • Monzo: How Peer-To-Peer Payments Work
  • Transit and Peering: How Your Requests Reach GitHub
  • How Expedia Finds your Flights: A Detailed View
  • How Spotify Streams Music

Organization

• Engineering Levels at SoundCloud
• Scaling Engineering Teams at Twitter
• Scaling Decision-Making Across Teams at LinkedIn
• Scaling Data Science Team at GOJEK
• Scaling Agile at Zalando
• Scaling Agile at bol.com
• Lessons Learned from Scaling a Product Team at Intercom
• Hiring, Managing, and Scaling Engineering Teams at Typeform
• Scaling the Datagram Team at Instagram
• Scaling the Design Team at Flexport
• Team Model for Scaling a Design System at Salesforce
• Building Analytics Team (4 parts) at Wish
• From 2 Founders to 1000 Employees at Transferwise
• Lessons Learned Growing a UX Team from 10 to 170 at Adobe
• Five Lessons from Scaling at Pinterest
• Approach Engineering at Vinted

Talk

• Distributed Systems in One Lesson - Tim Berglund, Senior Director of Developer Experience at Confluent
• Building Real Time Infrastructure at Facebook - Jeff Barber and Shie Erlich, Software Engineer at Facebook
• Building Reliable Social Infrastructure for Google - Marc Alvidrez, Senior Manager at Google
• Building a Distributed Build System at Google Scale - Aysylu Greenberg, SDE at Google
• Site Reliability Engineering at Dropbox - Tammy Butow, Site Reliability Engineering Manager at Dropbox
• How Google Does Planet-Scale for Planet-Scale Infra - Melissa Binde, SRE Director for Google Cloud Platform
• Netflix Guide to Microservices - Josh Evans, Director of Operations Engineering at Netflix
• Achieving Rapid Response Times in Large Online Services - Jeff Dean, Google Senior Fellow
• Architecture to Handle 80K RPS Celebrity Sales at Shopify - Simon Eskildsen, Engineering Lead at Shopify
• Lessons of Scale at Facebook - Bobby Johnson, Director of Engineering at Facebook
• Performance Optimization for the Greater China Region at Salesforce - Jeff Cheng, Enterprise Architect at Salesforce
• How GIPHY Delivers a GIF to 300 Millions Users - Alex Hoang and Nima Khoshini, Services Engineers at GIPHY
• High Performance Packet Processing Platform at Alibaba - Haiyong Wang, Senior Director at Alibaba
• Solving Large-scale Data Center and Cloud Interconnection Problems - Ihab Tarazi, CTO at Equinix
• Scaling Dropbox - Kevin Modzelewski, Back-end Engineer at Dropbox
• Scaling Reliability at Dropbox - Sat Kriya Khalsa, SRE at Dropbox
• Scaling with Performance at Facebook - Bill Jia, VP of Infrastructure at Facebook
• Scaling Live Videos to a Billion Users at Facebook - Sachin Kulkarni, Director of Engineering at Facebook
• Scaling Low-latency Live Streams at Facebook (Latencies for Real-time Interactions) - Saral Shodhan, SDE at Facebook
• Scaling Low-latency Live Streams at Facebook (End-to-End Considerations) - Federico Larumbe, SDE at Facebook
• Scaling Infrastructure at Instagram - Lisa Guo, Instagram Engineering
• Scaling Infrastructure at Twitter - Yao Yue, Staff Software Engineer at Twitter
• Scaling Infrastructure at Etsy - Bethany Macri, Engineering Manager at Etsy
• Scaling Real-time Infrastructure at Alibaba for Global Shopping Holiday - Xiaowei Jiang, Senior Director at Alibaba
• Scaling Data Infrastructure at Spotify - Matti (Lepistö) Pehrs, Spotify
• Scaling Pinterest - Marty Weiner, Pinterest’s founding engineer
• Scaling Slack - Bing Wei, Software Engineer (Infrastructure) at Slack
• Scaling Backend at Youtube - Sugu Sougoumarane, SDE at Youtube
• Scaling Backend at Uber - Matt Ranney, Chief Systems Architect at Uber
• Scaling Global CDN at Netflix - Dave Temkin, Director of Global Networks at Netflix
• Scaling Load Balancing Infra to Support 1.3 Billion Users at Facebook - Patrick Shuff, Production Engineer at Facebook
• Scaling (a NSFW site) to 200 Million Views A Day And Beyond - Eric Pickup, Lead Platform Developer at MindGeek
• Scaling Counting Infrastructure at Quora - Chun-Ho Hung and Nikhil Gar, SEs at Quora
• Scaling Git at Microsoft - Saeed Noursalehi, Principal Program Manager at Microsoft
• Scaling Multitenant Architecture Across Multiple Data Centres at Shopify - Weingarten, Engineering Lead at Shopify

Book

• Big Data, Web Ops & DevOps Ebooks - O'Reilly (Online - Free)
• Google Site Reliability Engineering (Online - Free)
• Distributed Systems for Fun and Profit (Online - Free)
• What Every Developer Should Know About SQL Performance (Online - Free)
• Beyond the Twelve-Factor App - Exploring the DNA of Highly Scalable, Resilient Cloud Applications (Free)
• Chaos Engineering - Building Confidence in System Behavior through Experiments (Free)
• The Art of Scalability
• Designing Data-Intensive Applications
• Web Scalability for Startup Engineers
• Scalability Rules: 50 Principles for Scaling Web Sites

Special Thanks

• Jonas Bonér, CTO at Lightbend, for the original inspiration

License

This repo is created and maintained by Binh Nguyen, R&D Engineer at The Agency for Science, Technology and Research of Singapore (A*Star). I am fortunate to be a part of a small but elite Vietnamese team, working remotely on various software projects with international clients. If you want to join us, please message me directly on LinkedIn! Thank You & Happy Coding! ❤️