Strong consistency in Distributed Systems

A stateless system does not retain information or store session state regarding previous interactions or requests from clients. In a stateless system, each client request contains all the necessary information for the server to understand and process it without relying on past interactions. Due to its inherent design that doesn't rely on maintaining session state, enhancing reliability and fault tolerance can be relatively straightforward by scaling the system across multiple nodes or transforming it into a distributed architecture.

Conversely, in a stateful system, the system keeps track of the state, history, or context of each interaction or transaction, preserving information between different requests or sessions. For instance, a database system stores and manages persistent data, maintaining the state across various operations and interactions. Achieving reliability and fault tolerance can be relatively difficult while ensuring that all nodes in the system have the same consistent data. Achieving this consistency while allowing concurrent read and write operations across multiple nodes is challenging.

In a system where strong consistency cannot be compromised, a consensus algorithm like Raft is a great fit. Raft operates with a leader-based replication model where one node is elected as the leader. All write operations go through the leader, ensuring that there's a single source of truth for data changes. This prevents conflicts that might arise due to multiple concurrent writes.

Log entries are replicated from the leader to followers in a consistent manner. The leader ensures that all followers receive the same log entries in the same order. Entries are committed only when a majority of nodes acknowledge them, ensuring that they are applied in the same order across the cluster. Raft employs strict consistency checks to maintain the order of log entries across the cluster. It verifies that logs match and can't be overwritten or changed arbitrarily. This mechanism helps prevent inconsistencies and ensures that all nodes reach a consensus on the same sequence of operations. For detailed explanation, see [https://raft.github.io/

Etcd](https://raft.github.io/), a distributed key-value store, employs the Raft consensus algorithm. It serves as a critical component in Kubernetes, acting as the primary data store to maintain the cluster's state and configuration information. It stores various types of data crucial for Kubernetes' operation, ensuring consistency, high availability, and reliability within the Kubernetes cluster.

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