Raft in Kahuna: Consensus and High Availability System
Overview
Raft serves as the foundation for ensuring consistency and high availability in Kahuna's distributed data storage system. Operating at the core of Kahuna's architecture. Raft manages data replication across multiple nodes through a partition-based approach where each partition is independently governed by a dedicated Raft group. This document describes how Raft functions within the Kahuna ecosystem and the mechanisms that enable reliable distributed data management.
Consensus and Replication Mechanisms
Raft implements a leader-based consensus protocol within each partition group. When client applications submit write requests, the designated leader node for that partition coordinates the operation across all replicas in the group.
The leader election process automatically selects a coordinator for each Raft group. This leader becomes the sole node authorized to handle client write operations and assumes responsibility for propagating log entries (representing data changes) to follower nodes. This centralized decision-making approach streamlines coordination and maintains operation ordering.
Log replication follows a structured process where the leader appends new operations to its local log before transmitting these entries to followers. Once a majority of nodes within the group have acknowledged receipt and storage of the entry, the leader marks it as committed. This majority-based commitment strategy ensures that data changes persist even when some nodes experience failures.
Fault Tolerance and Recovery Systems
Raft's resilience to node failures comes from its replicated log architecture. If a leader node becomes unavailable, the remaining nodes in the group initiate a new election process based on their current log state. This automated failover mechanism minimizes system downtime while preserving fault tolerance capabilities.
Data consistency across the system is achieved through strict ordering guarantees. Raft ensures that all committed log entries are applied to each node's state machine in identical sequence. This strong consistency model is essential for maintaining transactional integrity throughout the Kahuna ecosystem, especially when handling complex operations.
Operational Architecture
Kahuna implements a partitioned data model where information is segmented into discrete partitions. Each partition functions as an independent Raft group with its own consensus process. This partitioned design enables horizontal scalability since consensus operations occur independently across different data segments rather than requiring system-wide agreement.
When network partitions or communication disruptions occur, Raft's intrinsic recovery processes help affected nodes synchronize upon reconnection. The leader's log serves as the authoritative record, allowing followers to reconcile any discrepancies and reestablish consistency within the group.
Raft Group Configuration
Within Kahuna, each Region corresponds to a distinct Raft group typically comprising three replica nodes. The operational structure assigns one replica as the leader responsible for processing all read and write requests directed to that partition. The remaining replicas function as followers, continuously replicating data changes from the leader to maintain synchronized states.
Leadership Management
Leadership transitions occur through a voting mechanism triggered when the current leader becomes unresponsive or experiences failures. Each leadership election establishes a new "term" represented by a monotonically increasing counter value. The protocol strictly enforces that only one leader can exist per term within each Raft group, preventing split-brain scenarios.
Transaction Support
Raft plays a crucial role in Kahuna's transaction processing by ensuring changes to individual partitions are durably replicated before confirming transaction commitment. This integration helps the transaction layer maintain atomicity guarantees even when operations span multiple nodes or partitions.
Multi-Raft Management
Kahuna's architecture efficiently manages thousands of concurrent Raft groups (partitions) on individual nodes. The system employs sophisticated batch processing techniques and intelligent scheduling algorithms to coordinate multiple Raft groups simultaneously without compromising performance or reliability.
Kommander Implementation
Kahuna implements the Raft protocol through the Kommander library. This library provides comprehensive functionality for message processing, log persistence, and state machine interactions. Kommander utilizes either RocksDB or SQLite as the underlying storage mechanism for both Raft protocol logs and the actual key-value data managed by the system.