OxiGDAL Cluster

A comprehensive Pure Rust distributed computing framework for large-scale geospatial data processing. OxiGDAL Cluster provides enterprise-grade clustering, scheduling, and data distribution capabilities designed for processing massive geospatial datasets across multiple nodes.

Features

• Distributed Scheduler: Work-stealing scheduler with priority queues and dynamic load balancing for optimal resource utilization
• Task Graph Engine: DAG-based task dependencies with parallel execution planning and optimization
• Worker Pool Management: Worker registration, health monitoring, automatic failover, and dynamic resource allocation
• Data Locality Optimization: Intelligent task placement to minimize data transfer and maximize cache locality
• Fault Tolerance: Comprehensive error handling including task retry, speculative execution, and distributed checkpointing
• Distributed Cache: Cache coherency protocol with compression support and distributed LRU eviction
• Data Replication: Quorum-based reads/writes with automatic re-replication and consistency guarantees
• Cluster Coordination: Leader election and state management via Raft-based consensus (implemented in oxigdal-ha)
• Advanced Scheduling: Gang scheduling, fair-share scheduling, deadline-based scheduling, and multi-queue support
• Resource Management: Quota management, resource reservation, and comprehensive usage accounting
• Network Optimization: Topology-aware scheduling, bandwidth tracking, and congestion control
• Workflow Engine: Complex workflow orchestration with conditional execution and loop support
• Autoscaling: Dynamic cluster sizing based on load metrics and predictive scaling
• Monitoring & Alerting: Real-time metrics collection, custom alert rules, and anomaly detection
• Security & Access Control: Authentication, role-based access control (RBAC), audit logging, and secret management

Pure Rust

This library is 100% Pure Rust with no C/Fortran dependencies. All functionality works out of the box without requiring external libraries or system dependencies.

Installation

Add to your Cargo.toml:

[dependencies]
oxigdal-cluster = "0.1.3"

Quick Start

Create a distributed cluster and schedule tasks:

use oxigdal_cluster::prelude::*;
use std::sync::Arc;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create a cluster with default configuration
    let cluster = Cluster::new();

    // Start cluster components
    cluster.start().await?;

    // Create a task graph
    let mut task_graph = TaskGraph::new();

    // Add tasks to the graph
    let task1 = Task {
        id: TaskId::from(1),
        name: "process_geospatial_data".to_string(),
        dependencies: vec![],
        resource_requirements: ResourceRequirements::default(),
        status: TaskStatus::Pending,
        // ... other fields
    };

    task_graph.add_task(task1);

    // Submit tasks for execution
    // cluster.scheduler.schedule(&task_graph).await?;

    // Get cluster statistics
    let stats = cluster.get_statistics();
    println!("Cluster stats: {:?}", stats.metrics);

    // Stop cluster when done
    cluster.stop().await?;

    Ok(())
}

Usage

Basic Cluster Setup

use oxigdal_cluster::prelude::*;

// Create a cluster with custom configuration
let cluster = Cluster::new();

// Access individual components
let metrics = &cluster.metrics;
let scheduler = &cluster.scheduler;
let worker_pool = &cluster.worker_pool;
let coordinator = &cluster.coordinator;

Worker Pool Management

use oxigdal_cluster::{WorkerPool, Worker, WorkerId, WorkerStatus};

let worker_pool = WorkerPool::with_defaults();

// Register a new worker
let worker = Worker {
    id: WorkerId::from(1),
    host: "localhost:9090".to_string(),
    status: WorkerStatus::Healthy,
    // ... other configuration
};

Task Scheduling

use oxigdal_cluster::{Task, TaskId, TaskGraph, TaskStatus, ResourceRequirements};

let mut task_graph = TaskGraph::new();

// Create tasks with dependencies and resource requirements
let task = Task {
    id: TaskId::from(1),
    name: "process_tile".to_string(),
    dependencies: vec![],
    resource_requirements: ResourceRequirements {
        memory_mb: 2048,
        cpu_cores: 4,
        gpu_memory_mb: 0,
        // ... other requirements
    },
    status: TaskStatus::Pending,
};

task_graph.add_task(task);

Data Locality Optimization

use oxigdal_cluster::{DataLocalityOptimizer, LocalityConfig};

let config = LocalityConfig {
    enable_data_locality: true,
    data_locality_weight: 0.8,
    network_latency_ms: 1,
    // ... other settings
};

let optimizer = DataLocalityOptimizer::new(config);

// Get placement recommendations for tasks
// let recommendation = optimizer.recommend_placement(&task_id)?;

Distributed Caching

use oxigdal_cluster::{DistributedCache, CacheConfig, CacheKey};

let config = CacheConfig {
    max_size_mb: 4096,
    enable_compression: true,
    compression_threshold_bytes: 1024,
    // ... other settings
};

let cache = DistributedCache::new(config);

// Store and retrieve data
// cache.put(key, value).await?;
// let value = cache.get(&key).await?;

Fault Tolerance & Resilience

use oxigdal_cluster::{FaultToleranceManager, FaultToleranceConfig, CircuitBreakerConfig};

let config = FaultToleranceConfig {
    max_retries: 3,
    initial_retry_delay_ms: 100,
    max_retry_delay_ms: 10000,
    // ... other settings
};

let ft_manager = FaultToleranceManager::new(config);

// Manage resilience across cluster nodes

Workflow Orchestration

use oxigdal_cluster::{WorkflowEngine, Workflow};

let engine = WorkflowEngine::new();

// Create and execute workflows with conditional branching
// let workflow = Workflow::new("tile_processing_pipeline");
// engine.execute(workflow).await?;

Monitoring & Alerting

use oxigdal_cluster::{MonitoringManager, AlertRule, AlertSeverity};

let monitoring = MonitoringManager::new();

// Set up alert rules for cluster metrics
let alert_rule = AlertRule {
    name: "high_memory_usage".to_string(),
    condition: "memory_percent > 90".to_string(),
    severity: AlertSeverity::Warning,
    // ... other properties
};

// monitoring.add_rule(alert_rule)?;

Autoscaling

use oxigdal_cluster::{Autoscaler, AutoscaleConfig, ScaleDecision};

let config = AutoscaleConfig {
    min_nodes: 1,
    max_nodes: 100,
    target_cpu_percent: 70.0,
    scale_up_threshold_percent: 80.0,
    scale_down_threshold_percent: 30.0,
    // ... other settings
};

let autoscaler = Autoscaler::new(config);

// Autoscaler monitors metrics and makes scaling decisions

API Overview

Core Components

Module	Description
task_graph	DAG-based task graph with execution planning and optimization
worker_pool	Worker management, health checks, and dynamic allocation
scheduler	Work-stealing scheduler with multiple scheduling strategies
coordinator	Cluster coordination and Raft-based consensus (optional)
fault_tolerance	Resilience patterns including retries, circuit breakers, and bulkheads
data_locality	Intelligent task placement based on data location and network topology
cache_coherency	Distributed cache with coherency protocol and compression
replication	Data replication with quorum-based consistency
resources	Resource quota, reservation, and accounting management
network	Topology-aware scheduling and bandwidth management
workflow	Complex workflow orchestration with conditional execution
autoscale	Dynamic cluster sizing based on metrics and predictions
monitoring	Real-time metrics collection and alert management
security	Authentication, RBAC, audit logging, and secret management
metrics	Comprehensive cluster-wide metrics collection

Key Types

Task Management

• Task: Individual unit of work with dependencies and resource requirements
• TaskGraph: Directed acyclic graph of tasks with execution planning
• TaskId: Unique identifier for tasks
• TaskStatus: Task execution status (Pending, Running, Completed, Failed, etc.)
• ExecutionPlan: Optimized execution plan for task graph
• ResourceRequirements: CPU, memory, and GPU requirements for tasks

Worker Management

• Worker: Represents a compute node in the cluster
• WorkerId: Unique identifier for workers
• WorkerPool: Manages collection of workers
• WorkerStatus: Health and availability status
• WorkerCapabilities: Worker capabilities and specializations
• WorkerCapacity: Available resources on a worker

Cluster Coordination

• Cluster: Main cluster instance managing all components
• ClusterBuilder: Fluent builder for cluster configuration
• ClusterCoordinator: Raft-based cluster coordination
• NodeId: Unique identifier for cluster nodes
• NodeRole: Role in the cluster (Leader, Follower, Candidate)

Fault Tolerance

• FaultToleranceManager: Central fault tolerance orchestrator
• CircuitBreaker: Fail-fast pattern implementation
• Bulkhead: Resource isolation pattern
• HealthCheck: Worker health monitoring
• RetryDecision: Intelligent retry logic

Data Management

• DistributedCache: Multi-node cache with coherency
• CacheKey: Cache entry identifier
• ReplicationManager: Data replication orchestration
• ReplicaSet: Set of replicas for data
• DataLocalityOptimizer: Intelligent task placement

Scheduling

• Scheduler: Main scheduling engine
• SchedulerConfig: Scheduler configuration
• LoadBalanceStrategy: Work distribution strategy
• SchedulerStats: Scheduler metrics and statistics

Resource Management

• QuotaManager: Resource quota enforcement
• ReservationManager: Resource reservation system
• AccountingManager: Usage tracking and accounting

Monitoring

• ClusterMetrics: Cluster-wide metrics
• MetricsSnapshot: Point-in-time metrics snapshot
• MonitoringManager: Metrics collection and alerting
• AlertRule: Alert configuration
• AlertSeverity: Alert severity levels

Workflow

• Workflow: Complex multi-step workflow definition
• WorkflowEngine: Workflow execution engine
• WorkflowExecution: Active workflow execution
• WorkflowStatus: Workflow execution status

Configuration Examples

Minimal Cluster Configuration

let cluster = Cluster::new();

Custom Configuration with All Features

use oxigdal_cluster::prelude::*;

let cluster = ClusterBuilder::new()
    .with_scheduler_config(SchedulerConfig {
        work_stealing_enabled: true,
        load_balance_strategy: LoadBalanceStrategy::DynamicLoadBalancing,
        // ... other config
    })
    .with_worker_pool_config(worker_pool::WorkerPoolConfig {
        max_workers: 100,
        health_check_interval_ms: 5000,
        // ... other config
    })
    .with_fault_tolerance_config(FaultToleranceConfig {
        max_retries: 3,
        initial_retry_delay_ms: 100,
        // ... other config
    })
    .with_locality_config(LocalityConfig {
        enable_data_locality: true,
        data_locality_weight: 0.8,
        // ... other config
    })
    .build();

Performance Characteristics

Design Features

• Low-latency Scheduling: Work-stealing scheduler with microsecond-level scheduling latency
• High Throughput: Supports scheduling thousands of tasks per second
• Memory Efficient: Distributed cache with configurable compression
• Network Optimized: Topology-aware scheduling reduces inter-node traffic
• Scalable: Tested up to 1000+ nodes in production clusters

Benchmarks

The crate includes comprehensive benchmarks in the benches/ directory. Run benchmarks with:

cargo bench --bench cluster_bench

Examples

A comprehensive example demonstrates creating a distributed cluster and processing geospatial data:

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Initialize cluster
    let cluster = ClusterBuilder::new()
        .with_scheduler_config(SchedulerConfig::default())
        .build();

    cluster.start().await?;

    // Process data...

    cluster.stop().await?;
    Ok(())
}

See the OxiGDAL examples directory for more practical examples.

Testing

Run the test suite with:

cargo test --lib --all-features

Run with all features:

cargo test --all-features

Note: Raft-based leader election is implemented in oxigdal-ha. See oxigdal-ha for full HA failover integration.

Documentation

Full API documentation is available at docs.rs/oxigdal-cluster.

Additional resources:

• OxiGDAL Project
• COOLJAPAN Ecosystem
• Geospatial Data Abstraction Library

Related Projects

OxiGDAL Cluster is part of the OxiGDAL ecosystem:

• oxigdal-core: Core geospatial data structures and operations
• oxigdal-distributed: Distributed geospatial processing
• oxigdal-server: HTTP server for geospatial services
• oxigdal-cloud: Cloud integration (AWS, Azure, GCP)
• oxigdal-analytics: Geospatial analytics and aggregations
• oxigdal-ml: Machine learning for geospatial data
• oxigdal-security: Security and access control

COOLJAPAN Policy Compliance

This crate follows all COOLJAPAN ecosystem standards:

• Pure Rust: No C/Fortran dependencies
• No Unwrap Policy: All fallible operations return Result<T, E> with descriptive errors
• Workspace Management: Uses workspace dependencies with version pinning
• Comprehensive Testing: Full test coverage with property-based testing
• Performance: Optimized for production workloads with extensive benchmarking

Error Handling

OxiGDAL Cluster uses a descriptive ClusterError type for all fallible operations:

use oxigdal_cluster::ClusterError;

pub enum ClusterError {
    SchedulingError(String),
    WorkerFailure(String),
    DataLocalityError(String),
    ReplicationError(String),
    CoordinationError(String),
    // ... more variants
}

pub type Result<T> = std::result::Result<T, ClusterError>;

All functions return Result<T> instead of using unwrap().

Contributing

Contributions are welcome! Please ensure:

1. All tests pass: cargo test
2. No clippy warnings: cargo clippy --all-targets
3. Code follows Rust conventions
4. Documentation is updated for public APIs
5. Follows COOLJAPAN policies (no unwrap, pure Rust, etc.)

License

Licensed under Apache-2.0 license.

Copyright (c) 2024-2025 COOLJAPAN OU (Team Kitasan)

Acknowledgments

OxiGDAL Cluster is built as part of the COOLJAPAN project, bringing Pure Rust distributed computing to geospatial data processing.

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Part of the COOLJAPAN ecosystem - Pure Rust libraries for geospatial and scientific computing.