Introduction

Why Distributed Systems?

When a single machine is not enough — too slow, too small, or too unreliable — you distribute the workload across multiple machines. This is how every major internet service operates: Google, Amazon, Netflix, and WhatsApp all run on distributed systems.

  • Scale -- distribute load across hundreds or thousands of machines. No single machine can serve a billion users.
  • Fault tolerance -- when one machine fails (and it will), the system keeps running. Replication and consensus protocols keep data safe.
  • Latency -- serve users from servers close to them. Data replicated globally means fast reads everywhere.

But distribution introduces hard problems: machines fail, networks partition, clocks drift, and messages are lost or delayed. Every distributed system must make trade-offs between consistency, availability, and partition tolerance — the famous CAP theorem.

What You Will Build

In this course, you will implement the core algorithms and data structures that power real distributed systems — in JavaScript:

  • Lamport clocks and vector clocks — how to order events when there is no global clock.
  • Consistent hashing — how DynamoDB and Cassandra distribute data across nodes without reshuffling everything when a node is added or removed.
  • Leader election — how Kafka and Kubernetes elect a coordinator.
  • Quorum-based replication — how to guarantee strong consistency with N replicas.
  • CRDTs — conflict-free data structures that merge automatically in eventually consistent systems.
  • Bloom filters — probabilistic membership tests used in Cassandra, Chrome, and Bitcoin.
  • Rate limiting — the token bucket algorithm used by every production API.
  • Circuit breakers — the pattern that prevents cascading failures in microservices.
  • LRU caches — the eviction policy behind CPU caches, Redis, and database buffer pools.
  • Gossip protocols — how Cassandra and DynamoDB propagate cluster membership.
  • Two-phase commit — the atomic protocol for distributed transactions.

What You Will Learn

This course contains 12 lessons organized into 6 chapters:

  1. Clocks & Ordering -- Lamport clocks and vector clocks for logical time.
  2. Data Distribution -- Consistent hashing for distributing data across nodes.
  3. Consensus -- Leader election and two-phase commit for coordination.
  4. Replication -- Quorum-based operations, G-Counter CRDTs, and gossip protocols.
  5. Probabilistic Structures -- Bloom filters for space-efficient membership testing.
  6. Fault Tolerance -- Rate limiting, circuit breakers, and LRU caching.

Each lesson explains the concept, shows the algorithm, and gives you an exercise to implement it in JavaScript.

Let's get started.

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