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11 - CAP Theorem & Distributed Systems

Understanding CAP Theorem

The CAP Theorem states that a distributed data system can only guarantee two of the following three properties:

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Breaking Down the Properties

Consistency (C):

  • All nodes see the same data at the same time
  • Read returns the most recent write
  • Linearizability

Availability (A):

  • Every request receives a response
  • No guarantee the data is the latest
  • System remains operational

Partition Tolerance (P):

  • System continues to operate despite network partitions
  • Messages may be lost between nodes
  • This is not optional in distributed systems

CP vs AP Systems

CP (Consistency + Partition Tolerance)
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AP (Availability + Partition Tolerance)
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PACELC Theorem

An extension of CAP for when there's no partition:

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Eventual Consistency

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Eventual Consistency Patterns

Read Repair:

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Hinted Handoff:

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Anti-Entropy:

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Consistency Levels

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Conflict Resolution

Last-Write-Wins (LWW)
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CRDTs (Conflict-free Replicated Data Types)
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Distributed Transactions

Two-Phase Commit (2PC)
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Saga Pattern
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Consensus Algorithms

Raft (Simpler than Paxos)
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Paxos
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Practical System Design

Choosing Consistency Model
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Design Patterns
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Next: Connection Pooling & Performance