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of 20What is the CAP Theorem and what do the three letters stand for?
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The CAP Theorem, proposed by Eric Brewer, states that in any distributed system, you can guarantee at most two of the following three properties simultaneously:
- Consistency (C): All nodes see the same data at the same time. Every read receives the most recent write or an error.
- Availability (A): The system remains operational and responsive, meaning every request receives a response (success or failure).
- Partition Tolerance (P): The system continues to operate despite network failures that prevent communication between nodes.
The theorem essentially says you must choose between CP (Consistency + Partition Tolerance) or AP (Availability + Partition Tolerance) when network partitions occur, as CA (Consistency + Availability) is impossible in a distributed system with network partitions.
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Why is it impossible to achieve all three properties of CAP simultaneously in a distributed system?
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It's impossible because of the fundamental nature of network partitions in distributed systems. When a network partition occurs (nodes cannot communicate), you face a choice:
- Choose Consistency: Stop serving requests on partitioned nodes to maintain data consistency. This sacrifices availability.
- Choose Availability: Continue serving requests on all nodes, but risk serving stale data, sacrificing consistency.
You cannot have both because:
- To maintain consistency during a partition, you must either reject requests (losing availability) or wait for network healing (also losing availability)
- To maintain availability, you must serve requests with potentially stale data (losing consistency)
Partition tolerance is mandatory in distributed systems because network failures are inevitable.
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Explain the difference between strong consistency and eventual consistency.
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Strong Consistency:
- All nodes see the same data simultaneously
- Reads always return the most recent write
- Updates are atomic across all nodes
- Higher latency due to synchronization overhead
- Example: Traditional RDBMS with ACID properties
Eventual Consistency:
- Nodes may temporarily have different data
- The system will eventually reach a consistent state
- Lower latency as updates can proceed without waiting for all nodes
- Better availability during network issues
- Example: DNS, Amazon DynamoDB, Cassandra
Key Difference:
Strong consistency prioritizes correctness over performance, while eventual consistency prioritizes availability and performance over immediate consistency.
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What are some real-world examples of systems that choose CP vs AP?
What is the BASE model and how does it relate to CAP Theorem?
Explain different consistency models: Strong, Weak, and Eventual consistency.
What are vector clocks and how do they help with consistency in distributed systems?
How does partition tolerance affect system design decisions?
How do NoSQL databases typically handle the CAP theorem trade-offs?
Explain the concept of quorum-based consistency and how it relates to CAP.
What is the difference between ACID and BASE properties?
What is the difference between consistency and consensus in distributed systems?
What are conflict-free replicated data types (CRDTs) and how do they help with consistency?
How does the CAP theorem apply to microservices architecture?
How do consensus algorithms like Raft and Paxos relate to the CAP theorem?
What is causal consistency and how does it differ from other consistency models?
How would you design a distributed cache considering CAP theorem constraints?
What are some strategies for handling network partitions in distributed systems?
How do modern cloud databases handle CAP theorem trade-offs?
What factors should you consider when choosing between CP and AP systems for a specific use case?
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CAP Theorem & Consistency Models, in short videos.
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