14.2 CAP theorem and eventual consistency

NoSQL databases offer flexibility and scalability, but come with trade-offs. The CAP theorem explains why we can't have it all in distributed systems. It's about choosing between consistency, availability, and partition tolerance based on our needs.

Eventual consistency is a key concept in NoSQL. It sacrifices immediate consistency for better availability and partition tolerance. This approach works well for many modern applications, but requires careful design to handle potential inconsistencies.

CAP Theorem Fundamentals

Core Components of CAP Theorem

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• Consistency ensures all nodes in a distributed system see the same data at the same time
  • Achieved through synchronous replication and strict coordination between nodes
  • Guarantees clients always receive the most up-to-date data
• Availability ensures the system remains operational and responsive, even in the presence of node failures
  • Achieved through redundancy and fault-tolerant design
  • Allows clients to always receive a response, even if some nodes are down
• Partition tolerance enables the system to continue functioning despite network partitions or communication failures between nodes
  • Crucial for distributed systems spanning multiple locations or networks
  • Ensures the system remains available and consistent, even when network connectivity is lost

CAP Theorem and Distributed Systems

• CAP theorem states that in a distributed system, it is impossible to simultaneously provide more than two out of three guarantees: Consistency, Availability, and Partition tolerance
  • Known as Brewer's theorem, named after computer scientist Eric Brewer
  • Highlights the fundamental trade-offs in designing distributed systems
• Distributed systems consist of multiple nodes working together to provide a unified service or application
  • Nodes communicate and coordinate through a network (local area network or the internet)
  • Examples include distributed databases (Cassandra, MongoDB), distributed file systems (Hadoop HDFS), and distributed computing platforms (Apache Spark)
• CAP theorem implies that distributed systems must prioritize two out of three guarantees based on their specific requirements and use cases
  • CA systems (consistent and available) sacrifice partition tolerance (relational databases, distributed locking systems)
  • CP systems (consistent and partition-tolerant) sacrifice availability (distributed databases with strong consistency, distributed consensus systems like ZooKeeper)
  • AP systems (available and partition-tolerant) sacrifice strong consistency (eventually consistent databases like Cassandra, distributed caching systems like Memcached)

Consistency Models

Strong Consistency

• Strong consistency ensures all clients always see the latest updated data
  • Achieved through synchronous replication and strict coordination between nodes
  • Provides linearizability, meaning operations appear to execute atomically and in a sequential order
• Examples of strongly consistent systems include traditional relational databases (MySQL with synchronous replication) and distributed locking systems (Apache ZooKeeper)
• Strong consistency simplifies application development by providing a single source of truth
  • Suitable for applications requiring strict data consistency (financial systems, inventory management)
  • Sacrifices availability and partition tolerance, as updates cannot proceed until all nodes are in sync

Eventual Consistency

• Eventual consistency allows for temporary inconsistencies between nodes, but ensures all nodes eventually converge to the same state
  • Achieved through asynchronous replication and reconciliation processes
  • Provides a weaker consistency guarantee compared to strong consistency
• Examples of eventually consistent systems include NoSQL databases (Cassandra, Amazon DynamoDB) and distributed caching systems (Memcached)
• Eventual consistency enables higher availability and partition tolerance
  • Suitable for applications that can tolerate temporary inconsistencies (social media feeds, content management systems)
  • Requires careful application design to handle potential inconsistencies and conflicts

Trade-offs and Considerations

• Choosing between strong consistency and eventual consistency depends on the specific requirements and characteristics of the application
  • Strong consistency prioritizes data accuracy and consistency at the cost of availability and partition tolerance
  • Eventual consistency prioritizes availability and partition tolerance at the cost of temporary inconsistencies
• Trade-offs must be carefully evaluated based on factors such as data consistency requirements, scalability needs, latency constraints, and failure scenarios
  • Applications with strict consistency requirements (financial transactions) may opt for strong consistency
  • Applications with high availability and scalability requirements (global content delivery networks) may choose eventual consistency
• Hybrid approaches can be employed to balance consistency and availability
  • Using strong consistency for critical data and eventual consistency for less critical data within the same system
  • Implementing compensating transactions or conflict resolution mechanisms to handle inconsistencies in eventually consistent systems