What's Next?

Congratulations

You have completed all 12 lessons. You now understand Lamport clocks, vector clocks, consistent hashing, leader election, two-phase commit, quorums, CRDTs, gossip protocols, Bloom filters, rate limiting, circuit breakers, and LRU caches.

These are the algorithms and patterns that power Amazon, Google, Netflix, and every other major distributed system.

What to Explore Next

You have covered the foundational algorithms. Here is where to go deeper:

  • Raft consensus -- The readable alternative to Paxos. A leader is elected per term; all writes go through the leader. Implemented by etcd (Kubernetes), CockroachDB, and TiKV.
  • Paxos -- The original consensus protocol by Lamport. Notoriously difficult to understand but foundational to Google Chubby and Spanner.
  • MVCC (Multi-Version Concurrency Control) -- How databases like PostgreSQL and CockroachDB implement snapshot isolation without locking.
  • Saga pattern -- Managing long-running distributed transactions without 2PC.
  • Consistent Hashing with Virtual Nodes -- How Cassandra and DynamoDB use virtual nodes for better load balancing.
  • Replication logs -- WAL shipping, statement-based replication, and row-based replication.

Build Something

  • A key-value store -- implement a simple distributed key-value store with consistent hashing and replication.
  • A rate-limited API server -- build a Node.js server with per-client rate limiting backed by Redis.
  • A Raft implementation -- implement the Raft leader election protocol across multiple processes.

References

  • Designing Data-Intensive Applications by Martin Kleppmann (O'Reilly, 2017) -- the best book on distributed systems for practitioners. Covers replication, partitioning, transactions, consistency, and consensus with clarity and depth.
  • The Raft Paper -- "In Search of an Understandable Consensus Algorithm" by Ongaro and Ousterhout. Readable and complete.
  • Amazon DynamoDB Paper -- the original Dynamo paper. Consistent hashing, quorums, and vector clocks in production.
  • Google Bigtable Paper -- how Google stores petabytes of structured data.
  • Jepsen -- Kyle Kingsbury's analyses of consistency and failure modes in distributed databases.
  • Martin Kleppmann's Blog -- deep dives into distributed systems research.
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