Learning to build distributed systems is hard, especially if they are large scale. It's not that there is a lack of information out there. You can find academic papers, engineering blogs, and even books on the subject. The problem is that the available information is spread out all over the place, and if you were to put it on a spectrum from theory to practice, you would find a lot of material at the two ends, but not much in the middle. That is why I decided to write a book to teach the fundamentals of distributed systems so that you don’t have to spend countless hours scratching your head to understand how everything fits together. This is the guide I wished existed when I first started out, and it's based on my experience building large distributed systems that scale to millions of requests per second and billions of devices. If you develop the back-end of web or mobile applications (or would like to!), this book is for you. When building distributed systems, you need to be familiar with the network stack, data consistency models, scalability and reliability patterns, and much more. Although you can build applications without knowing any of that, you will end up spending hours debugging and re-designing their architecture, learning lessons that you could have acquired in a much faster and less painful way. Table of contents 1 Introduction 1.1 Communication 1.2 Coordination 1.3 Scalability 1.4 Resiliency 1.5 Operations 1.6 Anatomy of a distributed system Communication 2 Reliable links 2.1 Reliability 2.2 Connection lifecycle 2.3 Flow control 2.4 Congestion control 2.5 Custom protocols 3 Secure links 3.1 Encryption 3.2 Authentication 3.3 Integrity 3.4 Handshake 4 Discovery 5 APIs 5.1 HTTP 5.2 Resources 5.3 Request methods 5.4 Response status codes 5.5 OpenAPI 5.6 Evolution Coordination 6 System models 7 Failure detection 8 Time 8.1 Physical clocks 8.2 Logical clocks 8.3 Vector clocks 9 Leader election 9.1 Raft leader election 9.2 Practical considerations 10 Replication 10.1 State machine replication 10.2 Consensus 10.3 Consistency models 10.4 Chain replication 10.5 Solving the CAP theorem 10.6 Coordination avoidance 11 Transactions 11.1 ACID 11.2 Isolation 11.3 Atomicity 11.4 Asynchronous transactions Scalability 12 Functional decomposition 12.1 Microservices 12.2 API gateway 12.3 CQRS 12.4 Messaging 13 Partitioning 13.1 Sharding strategies 13.2 Rebalancing 14 Duplication 14.1 Network load balancing 14.2 Replication 14.3 Caching Resiliency 15 Common failure causes 15.1 Single point of failure 15.2 Unreliable network 15.3 Slow processes 15.4 Unexpected load 15.5 Cascading failures 15.6 Risk management 16 Downstream resiliency 16.1 Timeout 16.2 Retry 16.3 Circuit breaker 17 Upstream resiliency 17.1 Load shedding 17.2 Load leveling 17.3 Rate-limiting 17.4 Bulkhead 17.5 Health endpoint 17.6 Watchdog Testing and operations 18 Testing 18.1 Scope 18.2 Size 18.3 Practical considerations 19 Continuous delivery and deployment 19.1 Review and build 19.2 Pre-production 19.3 Production 19.4 Rollbacks 20 Monitoring 20.1 Metrics 20.2 Service-level indicators 20.3 Service-level objectives 20.4 Alerts 20.5 Dashboards 20.6 On-call 21 Observability 21.1 Logs 21.2 Traces 21.3 Putting it all together 22 Final words