1. Overview
Distributed systems have become increasingly popular due to their ability to scale and handle massive amounts of data. One of the critical challenges in designing distributed systems is ensuring data consistency across multiple nodes.
In this tutorial, we’ll explore three consistency models commonly used in distributed systems: eventual consistency, strong eventual consistency, and strong consistency. We’ll discuss their trade-offs and provide examples of systems that implement these models.
2. Eventual Consistency
Eventual consistency is a model that guarantees that updates will propagate through the system and eventually be applied to all nodes, given enough time. In other words, if no new updates are made to a particular data item, eventually, all nodes will converge on the same value for that item:
Eventual consistency is one of the three components of the BASE (Basically Available, Soft State, Eventually Consistent) model, often used as an alternative to the traditional ACID (Atomicity, Consistency, Isolation, Durability) model in distributed systems.
This model isn’t suitable for all types of systems. For example, systems that require strict consistency, such as financial systems, may be unable to tolerate eventual consistency. Amazon’s DynamoDB, a key-value store, employs eventual consistency.
3. Strong Eventual Consistency (SEC)
Strong eventual consistency guarantees that when all nodes have received the same set of updates, they’ll be in the same state. Regardless of the order in which updates were applied. This is achieved through conflict-free replicated data types (CRDTs) or operational transformation (OT):
SEC works only with specific data types that can be replicated and merged without conflicts. For example, SEC works well with data represented as a set or a counter since these data types can easily merge across nodes without conflicts. Riak, a distributed key-value store, supports strong eventual consistency through CRDTs.
However, SEC may allow the system to have temporary inconsistencies. Often, these inconsistencies are likely to be caused by propagation delays rather than reconciliation conflicts.
4. Strong Consistency
Strong consistency guarantees that every read operation returns the latest write operation’s result, regardless of the node on which the read operation is executed. This is typically achieved using consensus algorithms like Paxos or Raft:
Strong consistency aims to provide a higher level of consistency. Unlike eventual consistency, strong consistency ensures that all nodes see the same data without any temporary inconsistencies.
This approach benefits systems that require strict consistency, such as financial systems or real-time data processing applications. Some examples of distributed systems that employ strong consistency include Google’s Spanner.
However, strong consistency can come at the cost of performance and scalability since it requires more coordination and communication between nodes to maintain consistency.
5. Conclusion
The choice between eventual consistency, strong eventual consistency, and strong consistency depends on the specific requirements and constraints of the distributed system.
By understanding the trade-offs between these different approaches to consistency, system designers and developers can make informed decisions about the appropriate level of consistency for their applications.