In distributed systems, maintaining data consistency across multiple nodes is challenging, especially when balancing performance and fault tolerance. Here’s a concise overview:
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What is Consistency?
- Ensures all nodes in a distributed system have the same data at any point in time.
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Single Node Problems:
- Even with one node, failures (e.g., crashes) can lead to inconsistencies in data.
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Splitting Data:
- Data is divided (sharded) across multiple nodes to scale horizontally, leading to consistency challenges.
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Data Replication:
- Data is replicated for availability, but ensuring consistency between replicas requires careful coordination.
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The Two Generals Problem:
- Distributed nodes must coordinate, but unreliable communication (e.g., network failures) can prevent them from agreeing.
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Leader Assignment:
- A leader node ensures consistent updates across the system. Protocols like Paxos or Raft manage leader elections.
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CAP Theorem:
- The CAP Theorem explains the trade-offs between Consistency, Availability, and Partition Tolerance.
CAP Theorem Consistency Availability Consistency Strong Weak Available Weak Strong Partition-Tolerant Always Always -
Two-Phase Commit (2PC):
- Ensures distributed transaction consistency with a vote mechanism to commit or abort a transaction.
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Eventual Consistency:
- Data may be temporarily inconsistent but will eventually become consistent. This model is used in systems like Amazon DynamoDB.
- Consistency ensures all nodes have identical data, but managing this in distributed systems is complex.
- Trade-offs: Achieving both consistency and availability is challenging (CAP Theorem).
- Solutions: Use leader election, Two-Phase Commit, and eventual consistency based on system needs.