CAP Theorem

• Stands for Consistency, Availability and Partition Tolerance
  • Consistency - Every read receives the most recent write or an error
  • Availability - Every request receives a response, without guarantee that it contains the most recent version of the information
  • Partition Tolerance - The system continues to operate despite arbitrary partitioning due to network failures

• Note: Networks aren’t reliable, so you’ll need to support partition tolerance. You’ll need to make a software tradeoff between consistency and availability.

• Refer Database CAP Theorem

• You either go for CP or AP pattern

Consistency Patterns

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Weak Consistency

• After a write, reads may or may not see it.
• Used in:
  • memcached
  • Real time use cases such as VoIP, Video Chat, Realtime Multiplayer game

Eventual Consistency

• After a write, reads will eventually see it (typically within milliseconds). Data is replicated asynchronously.
• Used in:
  • DNS
  • Email

Strong Consistency

• After a write, reads will see it. Data is replicated synchronously.
• Used in:
  • File Systems
  • RDBMS

Availability Patterns

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Failover

• Active-Passive Failover
• Active-Active Failover

Replication

• Master-Slave Replication
• Master-Master Replication