Consistency Model in Distributed System

It might be difficult to guarantee that all data copies in a distributed system stay consistent over several nodes. The guidelines for when and how data updates are displayed throughout the system are established by consistency models. Various approaches, including strict consistency or eventual consistency, help developers manage trade-offs between data reliability, availability, and performance. Multiple consistency models and their effects on distributed system behavior and design will be discussed in this article.

What are Consistency models in distributed systems?

In distributed systems, consistency models establish criteria for data synchronization and specify how users and applications should interpret data changes across several nodes. In a distributed system, it specifically controls how data is accessed and changed across numerous nodes and how clients are informed of these updates. These models range from strict to relaxed approaches.

In distributed systems, consistency models come in a variety of forms. Every consistency model has advantages and disadvantages, and the system's particular requirements will determine which model is best.

Types of Consistency Models

1. Strong Consistency

In a strongly consistent system, all nodes in the system agree on the order in which operations occurred. Reads will always return the most recent version of the data, when an update occurs on one server, this model makes sure every other server in the system reflects this change immediately. This model provides the highest level of consistency, but it can be slower and require more resources in a distributed environment since all servers must stay perfectly in sync.

2. Sequential Consistency Model

It is a consistency model in distributed systems that ensures all operations across processes appear in a single, unified order. In this model, every read and write operation from any process appears to happen in sequence, regardless of where it occurs in the system. Importantly, all processes observe this same sequence of operations, maintaining a sense of consistency and order across the system.

3. Causal Consistency Model

The Causal Consistency Model is a type of consistency in distributed systems that ensures that related events happen in a logical order. In simpler terms, if two operations are causally related (like one action causing another), the system will make sure they are seen in that order by all users. However, if there’s no clear relationship between two operations, the system doesn’t enforce an order, meaning different users might see the operations in different sequences.

4. Weak Consistency Model

A weakly consistent system provides no guarantees about the ordering of operations or the state of the data at any given time. Clients may see different versions of the data depending on which node they connect to. This model provides the highest availability and scalability but at the cost of consistency.

5. Session Consistency

Session Consistency guarantees that all of the data and actions a user engages with within a single session remain consistent. Consider it similar to online shopping: session consistency ensures that an item will always be in your cart until you check out or log out, regardless of how you explore the page.

• In a distributed system, this means if you're interacting with different servers or services, you'll always get the same view of your data during your session.
• It might not reflect the most recent updates from other users, but for your own actions, it will remain consistent and reliable.

6. Monotonic Reads and Writes

Once a piece of data has been read or written, monotonic reads and writes guarantee that the data will always be viewed in a predictable and consistent order in subsequent reads or writes.

• Monotonic Reads: If you read a value from a system, the next time you read it, you will either get the same value or a more recent one. You won’t get an older value after seeing a newer one.
• Monotonic Writes: This ensures that once a write happens, all future writes will follow in the correct order. If you update a record or send a message, the system guarantees that it won’t reverse the order of your updates.

When to choose which Consistency Model?

• Strong Consistency:
  • Select strong consistency if you need to be absolutely certain that every node or data replica always reflects the most recent state.
  • When operations rely on the most recent data and consistency is crucial, like in banking systems or inventory management, it is important.
• Sequential Consistency Model:
  • Opt for sequential consistency when the order of operations matters but a perfect global order isn’t necessary.
  • It’s useful when you need to ensure that operations are executed in a predictable sequence, but don’t require every operation to be fully synchronized across all nodes.
• Causal Consistency Model:
  • Choose causal consistency when you want to ensure that related operations are seen by all nodes in the correct order, but don’t need a strict global order for all operations.
  • It’s good for systems where the relationship between actions matters (like user interactions or social media updates), but strict sequencing is not necessary.
• Weak Consistency Model:
  • Use weak consistency when availability and performance are more critical than absolute consistency, and it's acceptable for the data to be temporarily out of sync.
  • It’s ideal for systems where real-time consistency isn’t critical, but you still want to ensure that the data will eventually become consistent.
• Session Consistency:
  • Choose session consistency when a user's experience within a session needs to be consistent, but across different sessions, you may tolerate some inconsistency.
  • It’s useful for scenarios where users expect to interact with the same data during their session, but data may not need to be synchronized immediately between sessions.
• Monotonic Reads and Writes:
  • This model is best when you need to guarantee that a user’s view of the data will only move forward (not backward) in time.
  • Choose this when the sequence of user actions is important, and you want to avoid confusing the user with inconsistent or outdated information.

Conclusion

Consistency models help balance data accuracy, speed, and availability in distributed systems. Strong Consistency ensures up-to-date data, while Weak Consistency prioritizes speed. Causal and Sequential Consistency keep actions in order, useful for collaborative apps. Session Consistency keeps data steady within sessions, and Monotonic Reads/Writes provide predictable interactions. Choosing the right model enhances system performance and user experience.