Types of Weak Consistency Models
While “weak consistency” itself is a broad term, there are several specific models within this category that offer different guarantees and trade-offs:
1. Eventual Consistency
The most common weak consistency model. Updates are eventually propagated across all replicas, but there’s no defined timeframe for this. Clients might see stale data until the updates reach their local replica.
Drawbacks: Highly available and scalable, allows for efficient data distribution and replication. Data might be inconsistent across different nodes for a period, making it unsuitable for applications requiring strict data freshness.
2. Read Your Own Writes (RYOW)
Clients always see their own latest writes, but might not see updates from other clients immediately. Provides some level of consistency for individual users, simplifies development by reducing the need to reason about complex ordering guarantees.
Drawbacks: Data can still be inconsistent for other users until updates propagate, not suitable for applications requiring global consistency.
3. Causal Consistency
Guarantees that causally related operations are seen in the same order by all clients. For example, if client A updates a value, and then client B reads that value, all clients will see the update from A before any subsequent operations from B. Offers stronger consistency guarantees than eventual consistency while still maintaining some level of scalability.
Drawbacks: More complex to implement compared to eventual consistency, might introduce some performance overhead.
4. Monotonic Reads
Guarantees that successive reads by a single client will always return data in the same or newer version. However, different clients might see different versions of the data at the same time. Provides some level of consistency for individual clients, simplifies reasoning about data updates within a single session.
Drawbacks: Data can still be inconsistent across different clients, not suitable for applications requiring strong global consistency.
5. Session Consistency
Guarantees that all operations within a single client session are seen in the same order by all clients. However, different sessions might see different versions of the data. Useful for applications where consistency within a user’s workflow is important, but global consistency is not essential.
Drawbacks: Requires additional mechanisms to track and manage client sessions, can introduce complexity in distributed systems.
Weak Consistency in System Design
Weak consistency is a relaxed approach to data consistency in distributed systems. It doesn’t guarantee that all clients will see the same version of the data at the same time, or that updates will be reflected immediately across all nodes. This means there may be a temporary lag between a write operation and when the update is visible to all clients.
Important Topisc for Weak Consistency in System Design
- Importance of Weak Consistency in Systems
- Characteristics of Weak Consistency
- Key Principles of Weak Consistency
- Weak Consistency Comparison with Other Consistency Models
- Types of Weak Consistency Models
- Challenges with Weak Consistency
- Real-World Example of Weak Consistency
- Impact of weak consistency on system performance, scalability, and availability
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