Architecture Reference: OpenEV Data API¶

1. Introduction & Architectural Style¶

The OpenEV Data API is engineered using Hexagonal Architecture (Ports and Adapters), implemented via a Rust Cargo Workspace. This architectural choice strictly enforces the Dependency Rule, ensuring that the business logic (Core) remains independent of frameworks, databases and external interfaces.

1.1. System Objective¶

The system serves two primary objectives:

1. Data Processing Pipeline (ETL): Transform the layered JSON dataset into multiple ready-to-consume formats:
2. Complete canonical JSON (all vehicles expanded and validated)
3. SQLite database (embedded, ready-to-query)
4. PostgreSQL schema + data (production-ready)
5. CSV export (analysis and spreadsheet integration)

6. XML export (legacy system compatibility)

7. API Server: Provide a high-performance REST API to query vehicle data, deployable in multiple environments (containers, serverless, edge).

The system prioritizes correctness (via strict typing), reproducibility (deterministic builds), and portability (multiple output formats and deployment targets).

1.2. Build-Time vs Runtime Strategy¶

Build-Time (CI/CD Pipeline): - Dataset compilation happens during the release process - Validation and merge logic executed once - Multiple output artifacts generated and attached to releases - Ensures data quality before distribution

Runtime (API Server): - Serves pre-compiled, validated data - No runtime validation overhead - Optimized for read performance - Stateless and horizontally scalable

1.3. Component Roles (Crates)¶

The workspace is divided into three distinct crates, each representing a specific architectural layer:

• crates/ev-core (The Core / Inner Hexagon):

  • Role: Pure Domain Library
  • Responsibility: Defines domain types (Vehicle, Battery, Charging, etc.), validation rules, and business logic. Contains no I/O, HTTP, or Database code. Shared dependency for all other crates.
  • Principle: "Make invalid states unrepresentable"
  • Key Features:
    • Rust structs mirroring the JSON schema
    • Serde serialization/deserialization
    • Type-safe enums for classifications
    • Validation logic for data integrity
    • Schema generation support

• crates/ev-etl (Data Processing Pipeline):

  • Role: CLI Tool for Batch Processing
  • Responsibility: Executes during CI/CD to transform source data into distributable artifacts
  • Key Features:
    • Reads layered JSON files from dataset repository
    • Implements deep merge strategy (base.json → year → variant)
    • Validates against JSON schema and domain types
    • Generates multiple output formats:
      • vehicles.json - Complete canonical dataset
      • vehicles.db - SQLite database with full schema
      • vehicles.sql - PostgreSQL DDL + INSERT statements
      • vehicles.csv - Flattened tabular export
      • vehicles.xml - XML representation
    • Provides validation reports and statistics

• crates/ev-server (API Server):

  • Role: HTTP REST API Server
  • Responsibility: Exposes vehicle data through HTTP endpoints
  • Key Features:
    • RESTful API design
    • Query by make, model, year, variant
    • Search and filter capabilities
    • OpenAPI/Swagger documentation
    • Multiple deployment targets:
      • Standalone binary (Linux/Windows/macOS)
      • Docker container
      • Kubernetes deployment
      • Serverless functions (future)

1.4. Architectural Principles¶

1. Dependency Inversion: High-level modules (Adapters) depend on low-level modules (Core). The Core depends on nothing.
2. Single Source of Truth: The dataset repository is the canonical source; all artifacts are derived.
3. Zero-Cost Abstractions: Usage of Rust Generics and Traits over runtime polymorphism for compile-time optimization.
4. Type-Driven Development: Leverage Rust's type system to prevent invalid states.
5. Deterministic Builds: Same input always produces identical output.
6. Format Agnostic Core: Core domain logic is independent of serialization formats.

2. File Directory Tree¶

This structure represents the physical layout of the Monorepo/Workspace.

open-ev-data-api/
├── .github/
│   ├── workflows/
│   │   ├── ci.yml                # Quality Gates (Clippy, Fmt, Test)
│   │   ├── etl-artifacts.yml     # ETL Pipeline: Build and Attach Artifacts
│   │   └── release.yml           # Semantic Release + Docker Build
│   └── CODEOWNERS                # Governance
├── .cargo/
│   └── config.toml               # Global build flags
├── crates/                       # [WORKSPACE MEMBERS]
│   │
│   ├── ev-core/                  # [LAYER: DOMAIN]
│   │   ├── Cargo.toml
│   │   └── src/
│   │       ├── lib.rs            # Library entry point
│   │       ├── domain/           # Domain Entities
│   │       │   ├── mod.rs
│   │       │   ├── vehicle.rs    # Main aggregate root
│   │       │   ├── battery.rs    # Battery specifications
│   │       │   ├── charging.rs   # Charging capabilities
│   │       │   ├── powertrain.rs # Motor and drivetrain
│   │       │   ├── range.rs      # Range and efficiency
│   │       │   └── types.rs      # Common types and enums
│   │       └── validation/       # Validation rules
│   │           └── mod.rs
│   │
│   ├── ev-etl/                   # [LAYER: DATA PROCESSING]
│   │   ├── Cargo.toml
│   │   └── src/
│   │       ├── main.rs           # CLI entry point
│   │       ├── ingest/           # Data ingestion
│   │       │   ├── mod.rs
│   │       │   ├── reader.rs     # File system reader
│   │       │   └── parser.rs     # JSON parsing
│   │       ├── merge/            # Deep merge logic
│   │       │   ├── mod.rs
│   │       │   └── strategy.rs   # Merge precedence rules
│   │       ├── validate/         # Validation pipeline
│   │       │   └── mod.rs
│   │       └── output/           # Output generators
│   │           ├── mod.rs
│   │           ├── json.rs       # Canonical JSON output
│   │           ├── sqlite.rs     # SQLite database generator
│   │           ├── postgresql.rs # PostgreSQL schema + data
│   │           ├── csv.rs        # CSV export
│   │           └── xml.rs        # XML export
│   │
│   └── ev-server/                # [LAYER: API SERVER]
│       ├── Cargo.toml
│       └── src/
│           ├── main.rs           # Binary entry point
│           ├── config.rs         # Configuration management
│           ├── api/              # HTTP Handlers
│           │   ├── mod.rs
│           │   ├── routes.rs     # Route definitions
│           │   ├── vehicles.rs   # Vehicle endpoints
│           │   ├── search.rs     # Search endpoints
│           │   └── health.rs     # Health check
│           ├── db/               # Database layer
│           │   ├── mod.rs
│           │   ├── sqlite.rs     # SQLite connection
│           │   └── postgresql.rs # PostgreSQL connection (optional)
│           └── models/           # API response models
│               └── mod.rs
│
├── tests/                        # [INTEGRATION TESTS]
│   ├── etl_pipeline_test.rs      # ETL processing tests
│   ├── merge_logic_test.rs       # Merge strategy tests
│   ├── output_format_test.rs     # Output validation tests
│   └── api_integration_test.rs   # API endpoint tests
├── fixtures/                     # [TEST DATA]
│   └── sample_vehicles/          # Sample vehicle data for testing
├── schemas/                      # [DATABASE SCHEMAS]
│   ├── sqlite/
│   │   └── schema.sql            # SQLite schema definition
│   └── postgresql/
│       └── schema.sql            # PostgreSQL schema definition
├── docker/
│   ├── Dockerfile                # API server container
│   └── docker-compose.yml        # Local development setup
├── Cargo.toml                    # Workspace Root Config
├── .releaserc.json               # Semantic Release Config
├── README.md                     # Project Overview
└── docs/
    ├── ARCHITECTURE.md           # This Document
    └── RUST_GUIDELINES.md        # Rust Development Standards

3. System Relationship Diagram¶

This diagram visualizes the dependencies and data flow boundaries. All dependencies point inward toward ev-core.

graph TD
    subgraph "Data Sources"
        Dataset[Dataset Repository<br/>Layered JSON Files]
    end

    subgraph "Build-Time (CI/CD)"
        ETL[ev-etl CLI]

        subgraph "Output Artifacts"
            JSON[vehicles.json]
            SQLite[vehicles.db]
            PostgreSQL[vehicles.sql]
            CSV[vehicles.csv]
            XML[vehicles.xml]
        end
    end

    subgraph "Runtime (Production)"
        Server[ev-server<br/>REST API]
        DB[(Database<br/>SQLite/PostgreSQL)]
        Client[HTTP Clients]
    end

    subgraph "Core Domain"
        Core[ev-core<br/>Domain Types]
    end

    Dataset -->|Read| ETL
    Core -->|Used by| ETL
    Core -->|Used by| Server

    ETL -->|Generate| JSON
    ETL -->|Generate| SQLite
    ETL -->|Generate| PostgreSQL
    ETL -->|Generate| CSV
    ETL -->|Generate| XML

    SQLite -.->|Deploy| DB
    PostgreSQL -.->|Deploy| DB

    Server -->|Query| DB
    Client -->|HTTP Request| Server
    Server -->|JSON Response| Client

Dependency Flow¶

1. ev-core is the foundation - no dependencies
2. ev-etl depends on ev-core for types and validation
3. ev-server depends on ev-core for types and serialization
4. Output artifacts are standalone - no runtime dependencies on Rust code

4. Operational Data Flow¶

This sequence diagram illustrates both the Build-Time Pipeline (artifact generation) and the Runtime Path (API consumption).

sequenceDiagram
    autonumber
    participant Contributor
    participant DatasetRepo as Dataset Repository
    participant CI as GitHub Actions
    participant ETL as ev-etl CLI
    participant Artifacts as Release Artifacts
    participant Deploy as Deployment
    participant API as ev-server
    participant Client as API Consumer

    note over Contributor, Artifacts: BUILD-TIME PIPELINE

    Contributor->>DatasetRepo: Commit JSON changes
    DatasetRepo->>CI: Trigger workflow
    CI->>ETL: Run cargo build --release -p ev-etl
    ETL->>ETL: Load layered JSON files
    ETL->>ETL: Deep merge (base → year → variant)
    ETL->>ETL: Validate against schema
    ETL->>Artifacts: Generate vehicles.json
    ETL->>Artifacts: Generate vehicles.db (SQLite)
    ETL->>Artifacts: Generate vehicles.sql (PostgreSQL)
    ETL->>Artifacts: Generate vehicles.csv
    ETL->>Artifacts: Generate vehicles.xml
    CI->>DatasetRepo: Attach artifacts to release

    note over Deploy, Client: RUNTIME PATH

    Deploy->>API: Deploy ev-server + vehicles.db
    API->>API: Load database into memory
    Client->>API: GET /api/v1/vehicles/list?make=tesla
    API->>API: Query local database
    API-->>Client: Return JSON response

    Client->>API: GET /api/v1/vehicles/code/tesla:model_3:2024:model_3
    API->>API: Query by unique code
    API-->>Client: Return vehicle details

Pipeline Stages¶

Build-Time (CI/CD)¶

1. Trigger: Dataset repository receives commits
2. Compilation: ETL reads and merges layered JSON
3. Validation: Schema validation + business rules
4. Generation: Multiple output formats created
5. Distribution: Artifacts attached to GitHub releases

Runtime (API Server)¶

1. Initialization: Server loads pre-built database
2. Request Handling: REST endpoints process queries
3. Query Execution: Fast lookups in local database
4. Response: JSON serialization and delivery

5. Technology Stack & Versioning¶

The project utilizes the Rust 2024 Edition. Below is the technology stack for the ecosystem.

5.1. Language Environment¶

• Language: Rust
• Edition: 2024 (Latest Stable Edition)
• Toolchain: stable (Version 1.85+)
• MSRV: 1.92.0 (Minimum Supported Rust Version)

5.2. Core Libraries¶

5.3. ETL-Specific Libraries¶

5.4. Server-Specific Libraries¶

5.5. Development & Testing¶

5.6. Infrastructure & Deployment¶

• Container Runtime: Docker 27.0+
• Container Orchestration: Kubernetes 1.30+ (optional)
• CI/CD: GitHub Actions
• Release Automation: semantic-release
• Database (Production):
• SQLite 3.45+ (embedded mode)
• PostgreSQL 16+ (server mode)

5.7. API Standards¶

• OpenAPI Specification: 3.1.0
• REST API Versioning: URL-based (/api/v1/)
• Response Format: JSON (RFC 8259)
• Date/Time Format: ISO 8601
• Character Encoding: UTF-8

6. ETL Pipeline Specification¶

6.1. Input Processing¶

Discovery Phase¶

• Recursively scan the dataset repository src/ directory
• Identify all manufacturer directories (first level)
• Identify all model directories (second level)
• Collect all JSON files: base.json, year directories, and variant files

File Classification¶

• Base Files: src/<make>/<model>/base.json
• Year Base Files: src/<make>/<model>/<year>/<vehicle_slug>.json
• Variant Files: src/<make>/<model>/<year>/<vehicle_slug>_<variant_slug>.json

6.2. Merge Strategy¶

The ETL implements a deterministic deep merge with the following precedence (lowest to highest):

1. Model Base (base.json) - Shared attributes across all years
2. Year Base (<vehicle_slug>.json) - Specific year configuration
3. Variant (<vehicle_slug>_<variant_slug>.json) - Delta from year base

Merge Rules¶

• Objects: Deep merge by key (recursive)
• Scalars (string, number, boolean): Replace (higher precedence wins)
• Arrays: Complete replacement (no concatenation)
• Null values: Not allowed (use explicit empty states instead)
• Unknown keys: Validation failure

Output Cardinality¶

• Each year base file produces one canonical vehicle
• Each variant file produces one additional canonical vehicle
• Example: model_3.json + model_3_long_range.json = 2 canonical vehicles

6.3. Validation Pipeline¶

Each canonical vehicle must pass:

1. JSON Schema Validation: Against schema.json from dataset repository
2. Required Fields Check: All mandatory fields present
3. Type Validation: Correct data types for all fields
4. Business Rules:
5. At least one battery capacity (gross or net)
6. At least one charge port
7. At least one rated range entry
8. At least one source
9. Valid slug patterns (lowercase, alphanumeric + underscore)
10. Valid ISO codes (country, currency)
11. Referential Integrity: Variant files reference valid base vehicles

6.4. Output Formats¶

6.4.1. Canonical JSON (vehicles.json)¶

Format: Single JSON file with array of all canonical vehicles

Structure:

{
  "schema_version": "1.0.0",
  "generated_at": "2025-12-25T12:00:00Z",
  "vehicle_count": 1234,
  "vehicles": [
    { /* canonical vehicle 1 */ },
    { /* canonical vehicle 2 */ },
    ...
  ],
  "metadata": {
    "etl_version": "1.0.0",
    "dataset_commit": "abc123def",
    "processing_time_ms": 5432
  }
}

Use Cases: - Direct JSON consumption by applications - Import into other systems - Data analysis and exploration

6.4.2. SQLite Database (vehicles.db)¶

Schema Design: Normalized relational structure

Tables: - vehicles - Core vehicle information - battery_specs - Battery specifications - charging_specs - Charging capabilities - charge_ports - Physical charge ports - motors - Electric motor details - range_ratings - Range by test cycle - sources - Data sources - variants - Variant metadata

Indexes: - Primary keys on all tables - Composite index on (make_slug, model_slug, year, trim_slug) - Index on make_slug, model_slug separately - Full-text search index on model names

Use Cases: - Embedded applications - Desktop applications - Quick queries without server setup - API server data source (embedded mode)

6.4.3. PostgreSQL Schema (vehicles.sql)¶

Contents: - Complete DDL (CREATE TABLE, CREATE INDEX) - INSERT statements for all data - Views for common queries - Functions for search operations

Features: - JSONB columns for complex nested data - GiST indexes for JSONB queries - Full-text search with tsvector - Materialized views for aggregations

Use Cases: - Production API deployments - Advanced analytics - Multi-tenant scenarios - High-concurrency environments

6.4.4. CSV Export (vehicles.csv)¶

Format: Flattened denormalized structure

Columns: - Vehicle identification (make, model, year, trim, variant) - Key specifications (battery, range, charging) - Performance metrics - Pricing information - Source URLs (concatenated)

Handling Complex Fields: - Arrays: Pipe-separated values (value1|value2|value3) - Objects: Dot notation (battery.pack_capacity_kwh_net) - Nested structures: Flattened to top level

Use Cases: - Spreadsheet analysis (Excel, Google Sheets) - Data science workflows (pandas, R) - Business intelligence tools - Legacy system integration

6.4.5. XML Export (vehicles.xml)¶

Format: Hierarchical XML structure

Root Element: <vehicles>

Vehicle Structure:

<vehicle id="oed:tesla:model_3:2024:base">
  <make slug="tesla">Tesla</make>
  <model slug="model_3">Model 3</model>
  <year>2024</year>
  <battery>
    <pack_capacity_kwh_net>60.0</pack_capacity_kwh_net>
    <!-- ... -->
  </battery>
  <!-- ... -->
</vehicle>

Features: - XML Schema (XSD) generation - Namespace support - XSLT transformation support

Use Cases: - Enterprise system integration - SOAP-based services - Government/regulatory systems - Legacy XML pipelines

6.5. Error Handling and Reporting¶

Validation Errors¶

• Collect all errors (don't fail fast)
• Categorize by severity: ERROR, WARNING, INFO
• Generate detailed error report with file paths and line numbers

Statistics Generation¶

• Total vehicles processed
• Variants generated
• Files scanned
• Validation failures
• Processing time per stage

Exit Codes¶

• 0: Success - all vehicles valid
• 1: Validation failures - some vehicles invalid
• 2: Schema errors - malformed JSON
• 3: File system errors - cannot read files

7. API Server Specification¶

7.1. Architecture¶

Pattern: Three-layer architecture - Presentation Layer: HTTP handlers and routing - Service Layer: Business logic and queries - Data Layer: Database abstraction

7.2. Endpoints¶

GET /api/v1/health¶

Health check endpoint

Response:

{
  "status": "healthy",
  "version": "1.0.0",
  "database": "connected",
  "vehicle_count": 1234
}

GET /api/v1/vehicles/list¶

List all vehicles with pagination and filtering

Query Parameters: - make: Filter by manufacturer (slug) - model: Filter by model (slug) - year: Filter by year - vehicle_type: Filter by type (suv, sedan, etc.) - min_range_km: Minimum range - max_range_km: Maximum range - page: Page number (default: 1) - per_page: Items per page (default: 20, max: 100)

Response:

{
  "vehicles": [ /* vehicle summaries */ ],
  "pagination": {
    "page": 1,
    "per_page": 20,
    "total": 1234,
    "total_pages": 62
  }
}

GET /api/v1/vehicles/code/{code}¶

Get specific vehicle by unique code

Path Parameters: - code: Vehicle unique code (format: make:model:year:filename)

Response: Full canonical vehicle object

GET /api/v1/vehicles/search¶

Full-text search across vehicles

Query Parameters: - q: Search query (minimum 2 characters) - page, per_page: Pagination

Response: Ranked search results with pagination

GET /api/v1/makes/list¶

List all manufacturers with model information

Response:

{
  "makes": [
    { "slug": "tesla", "name": "Tesla", "vehicle_count": 42, "models": ["Model 3", "Model S", "Model X", "Model Y"] },
    { "slug": "byd", "name": "BYD", "vehicle_count": 38, "models": ["Dolphin", "Seal", "Atto 3"] }
  ]
}

7.3. Configuration¶

Configuration via environment variables:

• DATABASE_URL: SQLite or PostgreSQL connection string
• PORT: Server port (default: 3000)
• HOST: Bind address (default: 0.0.0.0)
• LOG_LEVEL: Logging level (debug, info, warn, error)
• CORS_ORIGINS: Allowed CORS origins
• MAX_PAGE_SIZE: Maximum items per page
• ENABLE_COMPRESSION: Enable gzip compression
• ENABLE_OPENAPI: Enable OpenAPI endpoint

7.4. Performance Characteristics¶

Targets: - Cold start: < 100ms (with SQLite embedded) - Response time (p50): < 10ms - Response time (p99): < 50ms - Throughput: > 10,000 req/s (single instance, cached) - Memory footprint: < 100MB (with in-memory SQLite)

8. CI/CD Pipeline¶

8.1. Dataset Repository Pipeline¶

Triggered by: Push to main or release creation

Workflow: ETL Artifacts Generation

name: Generate Data Artifacts

on:
  push:
    branches: [main]
  release:
    types: [created]

jobs:
  build-etl:
    - Checkout API repository
    - Install Rust toolchain
    - Build ev-etl in release mode

  generate-artifacts:
    - Run ev-etl against dataset source
    - Generate all output formats
    - Validate all artifacts
    - Calculate checksums

  publish-artifacts:
    - Upload to GitHub Release
    - Tag with semantic version
    - Generate release notes

Artifacts Produced: - vehicles.json + vehicles.json.sha256 - vehicles.db + vehicles.db.sha256 - vehicles.sql + vehicles.sql.sha256 - vehicles.csv + vehicles.csv.sha256 - vehicles.xml + vehicles.xml.sha256 - validation-report.txt - statistics.json

8.2. API Repository Pipeline¶

Triggered by: Push, PR, or release

Workflow 1: Continuous Integration

name: CI

on: [push, pull_request]

jobs:
  test:
    - cargo fmt --check
    - cargo clippy -- -D warnings
    - cargo test --all-features
    - cargo test --doc

  build:
    - cargo build --release -p ev-etl
    - cargo build --release -p ev-server

Workflow 2: Release

name: Release

on:
  push:
    branches: [main]

jobs:
  semantic-release:
    - Run semantic-release
    - Generate changelog
    - Create GitHub release

  build-binaries:
    - Cross-compile for multiple platforms
    - Upload binaries to release

  build-docker:
    - Build Docker image
    - Push to container registry
    - Tag with semantic version

Release Artifacts: - Binaries: ev-etl-linux-x64, ev-etl-windows-x64, ev-etl-macos-arm64 - Binaries: ev-server-linux-x64, ev-server-windows-x64, ev-server-macos-arm64 - Docker image: ghcr.io/open-ev-data/ev-server:latest - Docker image: ghcr.io/open-ev-data/ev-server:v1.2.3

9. Deployment Scenarios¶

9.1. Embedded Mode (SQLite)¶

Use Case: Single-server deployments, edge locations, development

Setup:

# Download artifacts
wget https://github.com/.../vehicles.db
wget https://github.com/.../ev-server-linux-x64

# Run server
DATABASE_URL=vehicles.db ./ev-server-linux-x64

Characteristics: - Zero external dependencies - < 100MB total footprint - Single binary deployment - Fast startup time

9.2. Container Deployment (Docker)¶

Use Case: Cloud deployments, Kubernetes, scalability

Docker Compose:

version: '3.8'
services:
  api:
    image: ghcr.io/open-ev-data/ev-server:latest
    environment:
      - DATABASE_URL=vehicles.db
    ports:
      - "3000:3000"
    volumes:
      - ./vehicles.db:/app/vehicles.db:ro

Kubernetes Deployment: - Deployment with multiple replicas - ConfigMap for database file - HorizontalPodAutoscaler - Ingress for external access

9.3. PostgreSQL Mode¶

Use Case: High-concurrency production, multi-tenant

Setup:

# Import schema
psql -d openev -f vehicles.sql

# Run server
DATABASE_URL=postgresql://user:pass@localhost/openev ./ev-server

Characteristics: - Advanced query capabilities - Connection pooling - Read replicas support - Full ACID compliance

10. Development Workflow¶

Important: For detailed Rust coding standards, best practices, and implementation guidelines, see RUST_GUIDELINES.md.

10.1. Initial Setup¶

# Clone API repository
git clone https://github.com/open-ev-data/open-ev-data-api.git
cd open-ev-data-api

# Install Rust toolchain
rustup install stable
rustup default stable

# Build all crates
cargo build --all

# Run tests
cargo test --all

10.2. ETL Development Cycle¶

# Point to local dataset
export DATASET_PATH=../open-ev-data-dataset/src

# Build and run ETL
cargo run -p ev-etl -- \
  --input $DATASET_PATH \
  --output ./output \
  --formats json,sqlite,csv

# Validate output
cargo run -p ev-etl -- \
  --validate ./output/vehicles.json

10.3. Server Development Cycle¶

# Use test database
cargo run -p ev-server -- \
  --database ./output/vehicles.db \
  --port 3000

# Run with hot reload (cargo-watch)
cargo watch -x 'run -p ev-server'

# Run integration tests
cargo test -p ev-server --test integration

10.4. Release Process¶

1. Dataset Release: Triggers ETL artifact generation
2. API Development: Happens independently
3. API Release: semantic-release handles versioning
4. Docker Build: Automatic on API release
5. Deployment: Manual or automatic depending on environment

11. Future Enhancements¶

Phase 2 - API Improvements¶

• GraphQL endpoint alongside REST
• WebSocket support for real-time updates
• Advanced search with Elasticsearch
• Caching layer with Redis
• Rate limiting and API keys

Phase 3 - Analytics¶

• Usage analytics and telemetry
• Popular vehicle tracking
• Search query analysis
• Performance monitoring dashboard

Phase 4 - Data Quality¶

• Automated data quality scoring
• Community contribution workflow
• Diff visualization for updates
• Historical data tracking (versioned snapshots)

12. Summary¶

The OpenEV Data API provides a comprehensive solution for transforming the layered dataset into multiple consumption formats:

Core Strengths: 1. Build-Time Compilation: Data validation happens once, not on every request 2. Multiple Formats: Single source, five output formats 3. Type Safety: Rust's type system prevents invalid states 4. Performance: Optimized for read-heavy workloads 5. Portability: Works embedded (SQLite) or client-server (PostgreSQL) 6. Automation: Fully automated CI/CD pipeline

Architecture Benefits: - Clean separation of concerns (Hexagonal Architecture) - Testable and maintainable codebase - Independent deployability of components - Multiple deployment strategies supported

Integration Points: - Dataset updates trigger artifact regeneration - API releases follow semantic versioning - Multiple consumption patterns supported (files, API, embedded)