Understanding Distributed Systems

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