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Types of Data Consistency Models

 

Types of Data Consistency Models

In distributed systems and databases, consistency models define how data is read and written across multiple nodes. The key types are:

1. Strong Consistency

🔹 Definition:

  • Every read receives the most recent write.
  • No stale or outdated data is ever read.
  • Achieved using synchronous replication.

🔹 Example:

  • Google Spanner ensures strong consistency across data centers.
  • A banking system that updates an account balance immediately after a transaction.

🔹 Pros & Cons:

✅ No stale reads.
✅ Ensures correctness.
❌ High latency due to synchronization.
❌ Not highly scalable.

2. Eventual Consistency (BASE Model)

🔹 Definition:

  • Data eventually becomes consistent across all nodes.
  • Temporary inconsistencies (stale reads) may occur.
  • Suitable for highly available and scalable systems.

🔹 Example:

  • DNS Systems take time to propagate changes across the internet.
  • Amazon DynamoDB, Apache Cassandra use eventual consistency for performance.

🔹 Pros & Cons:

✅ Highly available & scalable.
✅ Faster reads and writes.
❌ Users may see outdated data.

Variants of Eventual Consistency:

  1. Causal Consistency → Operations that are causally related are seen in order.
  2. Read-Your-Writes Consistency → A user always sees their own updates.
  3. Monotonic Reads Consistency → A user never sees older versions after reading a newer one.

3. Sequential Consistency

🔹 Definition:

  • All operations appear in the same order to all nodes.
  • Different nodes may see delays, but the sequence is always correct.

🔹 Example:

  • Multiplayer games ensure all players see the same events in the same order.

🔹 Pros & Cons:

✅ Easier debugging.
✅ Maintains logical order.
❌ More latency than eventual consistency.

4. Linearizability (Strict Consistency)

🔹 Definition:

  • Strongest form of consistency.
  • Every read returns the most recent write as if all operations occurred instantly.

🔹 Example:

  • Single-leader databases (e.g., Zookeeper, Etcd) use linearizability.
  • Stock trading platforms require linearizability to prevent race conditions.

🔹 Pros & Cons:

✅ Ensures correctness in critical applications.
❌ Poor performance in distributed environments.

5. Quorum Consistency

🔹 Definition:

  • A write is considered committed after N majority replicas acknowledge it.
  • Reads must check at least M nodes to ensure freshness.

🔹 Example:

  • Apache Cassandra and DynamoDB use quorum-based reads/writes.

🔹 Pros & Cons:

✅ Balances consistency and availability.
✅ Customizable (tunable consistency).
❌ Increased read/write latency.

Summary Table

Consistency Type Guarantees Performance Use Cases
Strong Consistency Always latest data Slow Financial transactions
Eventual Consistency Data syncs over time Fast Social media feeds, DNS
Sequential Consistency Operations in order Medium Multiplayer games
Linearizability Latest data, atomicity Very Slow Stock trading, Etcd, Zookeeper
Quorum Consistency Tunable balance Medium DynamoDB, Cassandra

Would you like an example implementation of any of these in .NET? 🚀

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