Section 1.1: Defining Consistency

When considering different replication models—be it single leader, multi-leader, or leaderless systems—a crucial question arises: What constitutes consistency in a distributed system? For this chapter, we define consistency as the capability of a distributed system to behave as though it were a single replica. This means that once a client accesses the latest value of an object, no other client should encounter an outdated version of that object.

The idea of linearizability serves as our guiding principle in establishing this form of consistency. It guarantees that all operations on data seem atomic and instantaneous from the viewpoint of all clients interacting with the system.

This level of consistency is crucial based on specific use cases and the trade-offs that a system is prepared to make. In scenarios demanding strict management of shared resource access (such as locks and leader election), linearizability becomes indispensable. It offers a framework for synchronizing operations across the distributed system, thereby maintaining the integrity and order of transactions, akin to a single, unified system.

Section 1.2: Importance of Linearizability

In distributed systems, ensuring orderly access to shared resources and electing a leader requires mechanisms that guarantee a sequential execution of operations. Linearizability ensures that once a lock is obtained or a leader is chosen, all nodes in the system acknowledge this change without delay, preventing any conflicts or inconsistencies.

When enforcing constraints, such as maintaining uniqueness, linearizability ensures that all operations aligned with these constraints are perceived instantaneously and uniformly across all nodes, thereby preserving data integrity. Additionally, it aids in synchronizing operations across various communication channels, ensuring that timing is consistent from all nodes' perspectives.

The relationship between linearizability and the CAP theorem is particularly noteworthy. The CAP theorem asserts that a distributed system can only concurrently achieve two of the following three guarantees: Consistency (all nodes see the same data simultaneously), Availability (every request gets a response), and Partition Tolerance (the system remains operational despite network partitions). By prioritizing linearizability, a system opts for robust consistency, sometimes at the expense of availability or partition tolerance.

The video title is "Massimo Boninsegni - Supersolid phases of dipolar bosons - YouTube". The video discusses advanced concepts in phases of dipolar bosons, adding depth to our understanding of consensus in distributed systems.

Implementing Linearizability

As we investigate various replication strategies, their compatibility with linearizability becomes evident. Single leader systems can achieve this consistency, provided that all followers replicate changes synchronously. In contrast, multi-leader systems, which often involve parallel or asynchronous writes, typically fall short of maintaining linearizability. Leaderless systems face similar challenges, especially when the sum of write quorums and read quorums exceeds the total number of nodes.

Maintaining order in single leader systems is usually managed through a monotonically increasing sequence number, a concept introduced in our prior discussions. The leader generates this number and shares it with the followers to ensure causal consistency.

However, achieving order in multi-leader or leaderless systems is more complex. Leslie Lamport’s introduction of Lamport Timestamps provides a simple yet effective mechanism for ordering events in distributed systems.

Total order broadcast (TOB) is another concept worth considering as it ensures reliable transmission and total ordering of events. While linearizability is a stringent guarantee ensuring immediate execution of operations, total order broadcast operates asynchronously.

By combining total order broadcast with linearizable storage, we can effectively build systems that are both reliable and consistent.

Exploring Consensus Algorithms

Consensus remains a significant challenge in distributed systems, requiring a foundation built upon replication, system models, linearizability, and total order broadcast. At its core, consensus involves multiple nodes agreeing on a specific outcome, which is crucial for various facets of distributed computing.

One practical approach to managing distributed transactions is through a two-phase commit process. Here’s a simplified overview:

1. Initiation by the Coordinator: A client begins the transaction by sending a request to a coordinator, usually a single leader.
2. Preparation Phase: The coordinator queries all participating nodes to see if they can commit the transaction.
3. Commit Phase: If all nodes agree, the coordinator proceeds to commit the transaction across all nodes.

Formalizing consensus reveals four key properties: Uniform Agreement, Integrity, Validity, and Termination. These properties ensure that all nodes reach a consistent decision, contributing to the overall integrity of the system.

Leveraging ZooKeeper for Consensus and Service Discovery

Creating a consensus algorithm is challenging, and often it's best to rely on established solutions. For my implementation of leader election in a simplified database, I opted for ZooKeeper. ZooKeeper streamlines the process of leader election, essential for single leader replication.

Using ZooKeeper's features, such as the LeaderLatch, we can delegate the intricate task of leader election. The LeaderLatch creates ephemeral sequential znodes under a specified path, with the node holding the smallest sequence number being elected as the leader.

For service discovery, we also create a persistent node in ZooKeeper for the service registry path, allowing new instances to create their ephemeral nodes. Each node maintains information about its status, including whether it is a leader.

With ZooKeeper managing both leader election and service discovery, we streamline the complexities of distributed systems, making our implementation efficient and robust.