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Advanced 20 min readModule: Module 5: CAP Theorem & Distributed Consistency

The CAP Theorem & Eventual Consistency

Understand the fundamental tradeoffs of distributed systems: Consistency (CP) vs Availability (AP) during network partitions.

What You Will Learn in This Lesson

  • The 3 pillars of CAP Theorem: Consistency, Availability, Partition Tolerance
  • Why network partitions (P) are unavoidable in distributed systems
  • Strong Consistency (PostgreSQL) vs Eventual Consistency (Cassandra, DynamoDB)

Introduction & Core Concept

Eric Brewer's CAP Theorem states that any distributed data store can simultaneously provide at most two out of three guarantees: Consistency, Availability, and Partition Tolerance.
WHY DOES THIS MATTER IN THE REAL WORLD?

When network cables disconnect or routers fail between data centers, you MUST choose between returning an error (CP) or returning potentially stale data (AP).

CAP Tradeoff Spectrum

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CP (Consistency + Partition Tolerance): Banking, Stock Exchanges (e.g. Spanner, CockroachDB)
AP (Availability + Partition Tolerance): Social Feeds, DNS, Shopping Carts (e.g. DynamoDB, Cassandra)

Line-by-Line Technical Breakdown

1The PACELC theorem extends CAP by evaluating Latency vs Consistency during normal non-partitioned operations.

Try It Yourself (Interactive Editor)

Modify the code in real-time and click Run to test live browser output and console logs.

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Industry Best Practices & Professional Standards

  • Design systems with Eventual Consistency unless strict financial correctness is mandatory.

Lesson Summary & Core Takeaways

  • The CAP theorem defines the fundamental physics of distributed data storage.