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RALE Architecture

Distributed Consensus Engine design

Learn about RALE's distributed consensus architecture, components, and PostgreSQL integration.

Architecture Features

Distributed Consensus

Raft-based consensus protocol ensuring cluster consistency and coordination.

Real-time Monitoring

Comprehensive monitoring and health checks for all cluster components.

Database Integration

Seamless PostgreSQL integration with ACID transaction support.

High Availability

Automatic failover and recovery with minimal downtime.

System Components

RALE Daemon (raled)

Core consensus engine that manages distributed state and coordinates cluster operations.

Key Features

Consensus protocol
State management
Leader election
Log replication

RALE Controller (ralectrl)

Management interface for cluster administration and monitoring operations.

Key Features

Cluster management
Configuration
Monitoring
Health checks

Database Integration

PostgreSQL integration layer for persistent storage and transaction coordination.

Key Features

PostgreSQL backend
ACID compliance
Transaction coordination
Data persistence

Network Layer

High-performance networking for inter-node communication and consensus.

Key Features

Inter-node communication
Consensus messaging
Heartbeat monitoring
Fault tolerance

Consensus Architecture

RALE implements a distributed consensus protocol that ensures consistency and coordination across all cluster nodes.

Leader Node

Coordinates consensus decisions and manages cluster state

Follower Nodes

Participate in consensus and maintain replica state

PostgreSQL Backend

Persistent storage with ACID transaction guarantees

PostgreSQL Integration Benefits

ACID Compliance

RALE leverages PostgreSQL's proven ACID transaction model to ensure data consistency and reliability.

  • • Atomic operations across all nodes
  • • Consistent state replication
  • • Isolation of concurrent operations
  • • Durable storage guarantees

High Performance

Optimized for high-throughput consensus operations with minimal latency overhead.

  • • Sub-millisecond consensus decisions
  • • Optimized network protocols
  • • Efficient state synchronization
  • • Minimal resource overhead