Скачать или смотреть Understanding How Raft Achieves Linearizability

Explore the Raft consensus algorithm and learn how it maintains linearizability in distributed systems through quorum and leadership verification.
---
This video is based on the question https://stackoverflow.com/q/63933968/ asked by the user 'databasechaser' ( https://stackoverflow.com/u/6722596/ ) and on the answer https://stackoverflow.com/a/63934801/ provided by the user 'kuujo' ( https://stackoverflow.com/u/1766816/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How is Raft linearlizable?

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding How Raft Achieves Linearizability

Distributed systems are becoming increasingly important in our digital world, especially when it comes to maintaining consistency across multiple nodes. Among the many consensus algorithms out there, Raft is widely recognized for its simplicity and effectiveness. But a common question many newcomers to distributed systems have is: How is Raft linearizable?

In this post, we'll break down the concept of linearizability, how Raft implements it, and the important role of quorum in ensuring consistency. Let’s dive in!

What is Linearizability?

Linearizability is a key property in the realm of distributed systems. It ensures that:

Each operation appears to occur instantaneously at some point between its start and end time.

This allows clients to observe operations in a consistent global order that aligns with real-time, making the system behave more predictably.

How Raft Achieves Linearizability

Consensus via Quorum

Raft ensures linearizability primarily through its leader-based approach and a process called quorum. Here's how it works:

Leader Election: In a Raft cluster, one node is elected as the leader, while the others act as followers. The leader is responsible for managing the log and orchestrating commits.

Log Commit: When the leader commits a log entry, it must receive acknowledgment from a quorum — that is, more than half of the nodes in the cluster. This guarantee of majority approval means that at least one node has persisted the log entry, reinforcing its consistency.

Read Operations and Leadership Verification

To maintain linearizability for read operations, the process is slightly more complex:

Leader Handling: All read requests should be managed by the leader. This minimizes inconsistencies because the leader has access to the most up-to-date state.

Verification of Leadership: Before responding to a read request, the leader must check that it has not been superseded by a newer leader. This step is crucial. If a new leader has assumed control, it may have a different log and state, which could lead to inconsistent results.

Relaxed Consistency in Real-World Implementations

While Raft's theoretical framework centers on strict linearizability:

In practice, many implementations relax these consistency requirements for reads. For instance, allowing reads from followers helps improve performance but could mean reading stale data.

However, note that when relaxed models are used, the guarantees of linearizability may not hold consistently for client interactions.

Client Request Handling and Sessions

To strengthen the commitment guarantees for client requests:

Raft uses a mechanism known as sessions. Sessions help to prevent problems such as dropped or duplicated requests. Without these sessions, a single client request could lead to multiple commits in the Raft log, undermining linearizability.

Conclusion

Raft's approach to maintaining linearizability through quorum is what makes it both robust and accessible. By ensuring that both write and read operations are effectively managed, Raft provides a dependable foundation for building distributed systems.

Understanding these concepts may seem daunting at first, but they are integral to mastering distributed systems. As you continue exploring, remember that elements such as leader verification and session management play crucial roles in achieving linearizable consistency.



For anyone diving into distributed systems, understanding how Raft ensures linearizability is a pivotal step. With the principles outlined here, you should have a clearer picture of how Raft effectively coordinates operations across multiple nodes.