Solvice Maps: Technical Architecture

Architecture Overview

Solvice Maps implements a sophisticated, cloud-native microservices architecture designed for high performance, scalability, and reliability. The system combines multiple routing engines with intelligent request processing, caching, and optimization to deliver sub-50ms routing calculations at enterprise scale.

System Architecture Diagram

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Client Apps   │────│   Load Balancer  │────│   API Gateway   │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                                                        │
                        ┌───────────────────────────────┼───────────────────────────────┐
                        │                               │                               │
                ┌───────▼────────┐              ┌──────▼──────┐              ┌────────▼────────┐
                │  MapR Gateway  │              │ OSM Service │             │  External APIs  │
                │   (Quarkus)    │              │  (Node.js)   │             │ (TomTom/Google) │
                └───────┬────────┘              └──────┬──────┘              └─────────────────┘
                        │                              │
        ┌───────────────┼──────────────────────────────┼───────────────┐
        │               │                              │               │
┌───────▼──────┐ ┌──────▼──────┐              ┌───────▼───────┐ ┌─────▼─────┐
│ PostgreSQL   │ │   Pub/Sub   │              │    Storage    │ │Monitoring │
│   Database   │ │  (Events)   │              │   (Results)   │ │& Metrics  │
└──────────────┘ └─────────────┘              └───────────────┘ └───────────┘

Core Components

1. MapR Gateway (Primary API Service)

Technology Stack:

Framework: Quarkus (Java/Kotlin)
Language: Kotlin 1.9 with JVM 17
Database: PostgreSQL with Hibernate ORM
Authentication: JWT (HS256) tokens
Messaging: Google Cloud Pub/Sub

Responsibilities:

Primary API endpoint for all routing requests
Request validation, authentication, and rate limiting
Intelligent request splitting for large matrices
Content-based caching and deduplication
Multi-engine routing coordination
Result aggregation and response formatting

Key Architectural Patterns:

Layered Architecture: Controllers → Services → Repositories
Event-Driven Processing: Pub/Sub for asynchronous operations
Proxy Pattern: Dynamic client creation for external APIs
Circuit Breaker: Fault tolerance for external services

2. OSRM Integration Service

Technology Stack:

Framework: NestJS (Node.js/TypeScript)
Runtime: Node.js 22 with clustering
OSRM: Native C++ bindings (Project-OSRM)
Load Balancing: Weighted round-robin
Deployment: Google Kubernetes Engine

Responsibilities:

Direct integration with OSRM routing engines
Traffic slice management (time-dependent routing)
Interpolation for smooth traffic transitions
High-performance routing calculations
Pub/Sub message processing for batch operations

Advanced Features:

Multi-Instance Management: Load multiple OSRM instances per region
Traffic Slice Interpolation: Decimal slice support (e.g., 2.3, 4.7)
Memory Optimization: Memory-mapped files for OSRM data
Batch Processing: Configurable concurrency limits

3. Infrastructure Layer

Cloud Platform: Google Cloud Platform (GCP)

Compute: Google Compute Engine with Container-Optimized OS
Container Orchestration: Google Kubernetes Engine (GKE)
Load Balancing: Global HTTP(S) Load Balancer
Storage: Cloud Storage for large results
Messaging: Cloud Pub/Sub for event processing
Monitoring: Cloud Monitoring with custom metrics

Data Flow Architecture

1. Synchronous Request Flow (Routes, Small Tables)

1. Client Request → API Gateway (Auth/Validation)
2. Gateway → MapR Gateway (Request Processing)
3. MapR Gateway → Routing Engine Selection
4. Engine Processing → Response Generation
5. Response → Gateway → Client

Timeline: 10-50ms end-to-end

2. Asynchronous Request Flow (Large Tables, Cubes)

1. Client Request → MapR Gateway (Validation)
2. Gateway → Request Splitting (if needed)
3. Child Requests → Pub/Sub Publishing
4. Pub/Sub → OSRM Service Processing
5. Results → Cloud Storage
6. Completion Event → Response Aggregation
7. Client Polling/Webhook → Final Results

Timeline: 30 seconds to 10+ minutes depending on size

3. Caching and Optimization Flow

1. Request → Hash Generation (Content-based)
2. Cache Lookup → PostgreSQL
3. Cache Hit → Direct Response (sub-10ms)
4. Cache Miss → Engine Processing → Cache Store
5. Future Identical Requests → Cache Hit

Cache Hit Rate: 60-80% for typical workloads

Routing Engine Integration

Engine Architecture

Multi-Engine Support:

interface RoutingEngine {
    fun calculateRoute(request: RouteRequest): RouteResponse
    fun calculateTable(request: TableRequest): TableResponse
    fun isAvailable(): Boolean
    fun getRegionSupport(): List<Region>
}

Implemented Engines:

OSM/OSRM: Self-hosted, high-performance, free
TomTom: Commercial API with real-time traffic
AnyMap: European-focused routing service
Google Maps: Global coverage with comprehensive data

Engine Selection Logic

Automatic Engine Selection:

fun selectEngine(request: RoutingRequest): RoutingEngine {
    val region = detectRegion(request.coordinates)
    val engines = getAvailableEngines(region)
    return engines.filter { it.isAvailable() }
                 .sortedBy { it.getPriority() }
                 .first()
}

Selection Criteria:

Geographic Coverage: Engine support for request region
Request Size: Engine limits and capabilities
Performance Requirements: Response time vs. accuracy
Cost Optimization: Usage-based routing decisions

Traffic Slice Management

Time-Dependent Routing:

Integer Slices (0-12): Direct OSRM instance calls
Decimal Slices (2.3, 4.7): Linear interpolation between adjacent slices
Traffic Patterns: Different profiles for weekdays vs. weekends
Real-time Selection: Current time-based slice selection

Interpolation Algorithm:

function interpolateSlice(slice: number, lowerResult: Result, upperResult: Result): Result {
    const weight = slice % 1; // Decimal portion
    return {
        duration: lowerResult.duration * (1 - weight) + upperResult.duration * weight,
        distance: lowerResult.distance * (1 - weight) + upperResult.distance * weight,
        geometry: lowerResult.geometry // Use lower slice geometry
    };
}

Database Architecture

Schema Design

Core Entities:

-- Main request tracking
CREATE TABLE table_requests (
    id BIGSERIAL PRIMARY KEY,
    hash VARCHAR(64) UNIQUE, -- Content-based caching
    parent_id BIGINT REFERENCES table_requests(id), -- Request splitting
    status VARCHAR(20), -- IN_PROGRESS, SUCCEEDED, FAILED
    created_at TIMESTAMP,
    updated_at TIMESTAMP
);

-- Cube (time-dependent) requests
CREATE TABLE cubes (
    id BIGSERIAL PRIMARY KEY,
    hash VARCHAR(64) UNIQUE,
    nr_time_slices INTEGER,
    status VARCHAR(20),
    created_at TIMESTAMP
);

-- External storage references
CREATE TABLE table_responses (
    id BIGSERIAL PRIMARY KEY,
    table_id BIGINT REFERENCES table_requests(id),
    storage_path VARCHAR(255), -- Cloud Storage path
    size_bytes BIGINT
);

Indexing Strategy:

Hash-based lookups: B-tree index on content hash
Status queries: Index on (status, created_at)
Hierarchical queries: Index on parent_id for request splitting

Data Storage Strategy

Hot Data (PostgreSQL):

Request metadata and status
Small responses (< 1MB)
User authentication and rate limiting data

Cold Data (Cloud Storage):

Large matrix results (> 1MB)
Binary OSRM data files
Historical analytics data

Performance Architecture

Performance Targets

Response Time SLAs:

Simple Routes: < 50ms P95
Small Tables (< 100 coords): < 100ms P95
Large Tables: Asynchronous processing
API Overhead: < 10ms for cached responses

Optimization Strategies

1. Request Splitting:

fun splitLargeRequest(request: TableRequest): List<TableRequest> {
    val engineLimit = getEngineLimit(request.engine)
    val sourceBatches = request.sources.chunked(engineLimit.sources)
    val destBatches = request.destinations.chunked(engineLimit.destinations)
    
    return sourceBatches.flatMap { sources →
        destBatches.map { destinations →
            request.copy(sources = sources, destinations = destinations)
        }
    }
}

2. Content-Based Caching:

fun generateCacheKey(request: TableRequest): String {
    val content = listOf(
        request.sources.sorted(),
        request.destinations.sorted(),
        request.engine,
        request.profile
    ).joinToString("|")
    return SHA256.hash(content)
}

3. Parallel Processing:

suspend fun processTableRequests(requests: List<TableRequest>): List<TableResponse> {
    return requests.map { request →
        async { processRequest(request) }
    }.awaitAll()
}

Scalability Architecture

Horizontal Scaling

Stateless Services:

All services designed for horizontal scaling
No local state storage
Session data in external stores (PostgreSQL, Redis)

Auto-Scaling Triggers:

CPU utilization > 70%
Request queue depth > 100
Response time P95 > SLA threshold

Load Distribution:

# Kubernetes HPA Configuration
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: mapr-gateway-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: mapr-gateway
  minReplicas: 2
  maxReplicas: 20
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70

Event-Driven Scaling

Pub/Sub Message Processing:

Dynamic subscription scaling based on queue depth
Weighted round-robin message distribution
Dead letter queues for failed processing

Asynchronous Processing Benefits:

Decouples API response time from computation time
Natural backpressure handling
Enables batch optimization strategies

Security Architecture

Authentication & Authorization

Multi-Layer Security:

1. API Gateway → API Key Validation
2. JWT Token → Claims Validation
3. Rate Limiting → Per-key limits
4. IP Whitelisting → Enterprise customers

JWT Token Structure:

{
  "iss": "solvice-maps",
  "sub": "user-id",
  "aud": "maps-api",
  "exp": 1640995200,
  "iat": 1640991600,
  "scope": ["routing:read", "tables:write"]
}

Data Protection

Encryption:

In Transit: TLS 1.3 for all API communication
At Rest: Google Cloud Storage encryption
Database: PostgreSQL transparent data encryption

Access Controls:

Service Accounts: GCP IAM with minimal permissions
Network Segmentation: VPC isolation
Secrets Management: Google Secret Manager

Monitoring and Observability

Metrics Collection

Custom Metrics:

// OSRM service metrics
@Counter('osrm_requests_total', ['method', 'status', 'team'])
osrmRequestsTotal: Counter;

@Histogram('osrm_request_duration_seconds', ['method', 'team'])
osrmRequestDuration: Histogram;

@Gauge('osrm_active_connections', ['engine'])
osrmActiveConnections: Gauge;

Infrastructure Metrics:

Request throughput and latency
Database connection pool utilization
Memory and CPU usage per service
External API response times and error rates

Distributed Tracing

OpenTelemetry Integration:

@WithSpan("process-table-request")
suspend fun processTableRequest(request: TableRequest): TableResponse {
    val span = Span.current()
    span.setAttribute("table.size", request.sources.size * request.destinations.size)
    span.setAttribute("routing.engine", request.engine.toString())
    
    return try {
        val result = routingEngine.calculateTable(request)
        span.setStatus(StatusCode.OK)
        result
    } catch (e: Exception) {
        span.recordException(e)
        span.setStatus(StatusCode.ERROR)
        throw e
    }
}

Health Monitoring

Health Check Endpoints:

/health/live: Basic service liveness
/health/ready: Service readiness (dependencies available)
/health/engines: Routing engine status
/health/database: Database connectivity

Error Handling and Resilience

Circuit Breaker Pattern

@Component
class RoutingEngineCircuitBreaker {
    private val circuitBreaker = CircuitBreaker.ofDefaults("routing-engine")
    
    fun callEngine(request: RoutingRequest): RoutingResponse {
        return circuitBreaker.executeSupplier {
            externalRoutingEngine.process(request)
        }
    }
}

Retry Strategies

Exponential Backoff:

val retryConfig = RetryConfig.custom<RoutingResponse>()
    .maxAttempts(3)
    .waitDuration(Duration.ofSeconds(1))
    .exponentialBackoffMultiplier(2.0)
    .retryOnException { it is TemporaryRoutingException }
    .build()

Graceful Degradation

Fallback Mechanisms:

Engine Fallback: Switch to alternative routing engine
Cached Response: Return stale cached data with warnings
Simplified Response: Return basic distance calculations
Error Response: Structured error with retry guidance

Data Consistency and Reliability

Event Sourcing for Request Tracking

Event Log:

sealed class TableEvent {
    data class TableRequestCreated(val tableId: Long, val request: TableRequest)
    data class TableProcessingStarted(val tableId: Long, val engine: RoutingEngine)
    data class TableProcessingCompleted(val tableId: Long, val response: TableResponse)
    data class TableProcessingFailed(val tableId: Long, val error: String)
}

Idempotency

Idempotent Request Processing:

@Transactional
fun processTableRequest(request: TableRequest): TableResponse {
    val existingResponse = findByHash(request.hash)
    if (existingResponse != null && existingResponse.isComplete()) {
        return existingResponse
    }
    
    return executeNewRequest(request)
}

Development and Deployment Architecture

CI/CD Pipeline

Automated Deployment:

# GitLab CI/CD Pipeline
stages:
  - test
  - build
  - deploy-staging
  - integration-test
  - deploy-production

test:
  script:
    - ./gradlew test
    - npm test

build:
  script:
    - docker build -t $IMAGE_TAG .
    - docker push $REGISTRY/$IMAGE_TAG

deploy-production:
  script:
    - kubectl set image deployment/mapr-gateway app=$IMAGE_TAG
    - kubectl rollout status deployment/mapr-gateway

Blue-Green Deployment

Zero-Downtime Deployments:

Deploy new version to blue environment
Run health checks and integration tests
Switch load balancer to blue environment
Monitor for issues, rollback if necessary
Decommission green environment

This technical architecture provides a robust foundation for Solvice Maps, enabling high performance, scalability, and reliability while maintaining developer productivity and operational simplicity.

Map Tiles

Maplibre

​Solvice Maps: Technical Architecture

​Architecture Overview

​System Architecture Diagram

​Core Components

​1. MapR Gateway (Primary API Service)

​2. OSRM Integration Service

​3. Infrastructure Layer

​Data Flow Architecture

​1. Synchronous Request Flow (Routes, Small Tables)

​2. Asynchronous Request Flow (Large Tables, Cubes)

​3. Caching and Optimization Flow

​Routing Engine Integration

​Engine Architecture

​Engine Selection Logic

​Traffic Slice Management

​Database Architecture

​Schema Design

​Data Storage Strategy

​Performance Architecture

​Performance Targets

​Optimization Strategies

​Scalability Architecture

​Horizontal Scaling

​Event-Driven Scaling

​Security Architecture

​Authentication & Authorization

​Data Protection

​Monitoring and Observability

​Metrics Collection

​Distributed Tracing

​Health Monitoring

​Error Handling and Resilience

​Circuit Breaker Pattern

​Retry Strategies

​Graceful Degradation

​Data Consistency and Reliability

​Event Sourcing for Request Tracking

​Idempotency

​Development and Deployment Architecture

​CI/CD Pipeline

​Blue-Green Deployment