Sparki Technical Architecture Document

:::info readme This document provides the comprehensive technical architecture for the Sparki platform, detailing the design decisions, technology choices, deployment patterns, and scalability strategies that enable Sparki to deliver zero-configuration CI/CD at massive scale. The architecture is designed around core principles of performance, reliability, scalability, and developer experience. ::: Version: 1.0
Date: December 3, 2025
Architecture Owner: Chief Technology Officer
Last Updated: December 3, 2025

1. Architecture Overview

1.1 System Architecture Diagram

┌─────────────────────────────────────────────────────────────────┐
│                     Developer Interfaces                         │
├──────────────────┬──────────────────┬──────────────────┬────────┤
│  CLI (Sparki)    │  Terminal UI     │  Web Dashboard   │ REST   │
│  (Go Fiber)      │  (Go Bubbletea)  │  (React/Vue)     │ API    │
└────────┬──────────┴──────┬───────────┴───────┬──────────┴────┬───┘
         │                 │                   │              │
         └─────────────────┼───────────────────┼──────────────┘
                           │                   │
         ┌─────────────────┴───────────────────┴────────────┐
         │                                                   │
    ┌────▼────────────────────────────────────────────────┐ │
    │    Sparki Core Platform (Go Fiber + Axum)          │ │
    ├─────────────────────────────────────────────────────┤ │
    │  Load Balancer / API Gateway (Fiber)                │ │
    ├────┬──────────────┬──────────────┬──────┬──────────┤ │
    │    │              │              │      │          │ │
    │ ┌──▼──┐      ┌────▼──┐     ┌────▼──┐ ┌─▼──┐ ┌────▼──┐│
    │ │API  │      │Framework│    │Build  │ │Auth│ │Workspace││
    │ │Svc  │      │Detection│    │Engine │ │Svc │ │Admin  ││
    │ └──┬──┘      └────┬───┘     └──┬────┘ └─┬──┘ └───┬───┘│
    │    │              │            │        │        │    │
    └────┼──────────────┼────────────┼────────┼────────┼────┘
         │              │            │        │        │
    ┌────▼──────────────▼────────────▼────────▼────────▼────┐
    │  Loco Deployment Engine (Rust Axum/Tokio)            │
    ├───────────────────────────────────────────────────────┤
    │  Pre-Deploy Validation  │  Stage Manager              │
    │  Health Checks          │  Rollback Orchestration     │
    └──┬────────────────────────────────────────────────┬───┘
       │                                                │
    ┌──▼────────────┬────────────────────┬─────────────▼──┐
    │               │                    │                │
┌───▼────┐   ┌──────▼──┐   ┌────────────▼────┐  ┌──────▼──────┐
│PostgreSQL   │  Redis  │   │Cloud Adapters   │  │ Observability│
│ (Primary)   │ (Cache) │   │ (Railway, Render)  │  │ Stack       │
│             │         │   │                 │  │ (Prom, ELK) │
└─────────────┴─────────┴───┴─────────────────┴──┴─────────────┘

1.2 Layered Architecture

┌─────────────────────────────────────────────────────────┐
│  Presentation Layer                                     │
│  CLI, TUI, Web Dashboard, REST API                      │
├─────────────────────────────────────────────────────────┤
│  Application Layer                                      │
│  API Services (Go Fiber), Business Logic                │
├─────────────────────────────────────────────────────────┤
│  Integration Layer                                      │
│  Cloud Platform Adapters, External Service Integration  │
├─────────────────────────────────────────────────────────┤
│  Orchestration Layer                                    │
│  Loco Engine (Rust Axum), Job Scheduling, Workflows     │
├─────────────────────────────────────────────────────────┤
│  Data Layer                                             │
│  PostgreSQL, Redis, S3/Object Storage                   │
├─────────────────────────────────────────────────────────┤
│  Infrastructure Layer                                   │
│  Kubernetes, Docker, Observability Stack                │
└─────────────────────────────────────────────────────────┘

2. Core Components

2.1 Sparki API (Go Fiber)

Purpose: Central REST API providing project management, pipeline orchestration, and CI/CD operations. Technology Stack:

Framework: Go Fiber v2 (ultra-high-performance HTTP framework)
Runtime: Go 1.22+ with goroutine-based concurrency
Database Driver: pgx for PostgreSQL
Caching: go-redis/redis
JSON Parsing: encoding/json (stdlib optimized)
Error Handling: Custom error types with structured context

Key Characteristics:

Zero-allocation JSON parsing
Minimal garbage collection pressure
Sub-50ms p95 response times at scale
100K+ concurrent connections per instance
Horizontal scaling without session affinity

Core Modules:

cmd/
├── api/
│   ├── main.go                  # Application entry point
│   └── config.go                # Configuration management
internal/
├── api/
│   ├── handlers/
│   │   ├── projects.go          # Project CRUD operations
│   │   ├── pipelines.go         # Pipeline management
│   │   ├── builds.go            # Build orchestration
│   │   ├── deployments.go       # Deployment coordination
│   │   └── auth.go              # Authentication flow
│   ├── middleware/
│   │   ├── auth.go              # JWT validation
│   │   ├── logging.go           # Request/response logging
│   │   ├── metrics.go           # Prometheus instrumentation
│   │   └── errors.go            # Error handling
│   ├── service/
│   │   ├── project.go           # Project business logic
│   │   ├── pipeline.go          # Pipeline generation
│   │   ├── build.go             # Build orchestration
│   │   └── detector.go          # Framework detection
│   ├── repository/
│   │   ├── project.go           # Project data access
│   │   ├── pipeline.go          # Pipeline persistence
│   │   └── build.go             # Build record storage
│   └── models/
│       ├── project.go           # Project domain model
│       ├── pipeline.go          # Pipeline domain model
│       └── build.go             # Build domain model
pkg/
├── detector/                    # Framework detection engine
├── adapters/                    # Framework adapters
├── storage/                     # Storage abstraction
└── observability/               # Logging and metrics

2.2 Loco Deployment Engine (Rust Axum/Tokio)

Purpose: Intelligent deployment orchestration with validation, health checks, and rollback capabilities. Technology Stack:

Framework: Rust Axum web framework
Async Runtime: Tokio (industry-leading async runtime)
HTTP Client: reqwest for cloud platform APIs
Database: sqlx for async PostgreSQL queries
Caching: redis crate for Redis integration
Serialization: serde/serde_json for JSON
Error Handling: anyhow/thiserror for result chains

Key Characteristics:

Lock-free async with zero-copy operations
Memory-safe concurrency without garbage collection
Sub-millisecond latencies for deployment operations
Predictable performance under load
Automatic resource cleanup via RAII pattern

Core Modules:

src/
├── main.rs                      # Application entry point
├── config/
│   └── mod.rs                   # Configuration management
├── orchestrator/
│   ├── mod.rs                   # Orchestration coordinator
│   ├── validator.rs             # Pre-deployment validation
│   ├── stage_manager.rs         # Multi-stage orchestration
│   ├── health_checker.rs        # Health check execution
│   └── rollback.rs              # Rollback coordination
├── adapters/
│   ├── mod.rs                   # Platform adapter trait
│   ├── railway.rs               # Railway adapter
│   ├── render.rs                # Render adapter
│   ├── fly_io.rs                # Fly.io adapter
│   └── vercel.rs                # Vercel adapter
├── scripts/
│   ├── executor.rs              # Custom script execution
│   └── sandbox.rs               # Execution sandbox
├── models/
│   ├── deployment.rs            # Deployment domain model
│   ├── validation.rs            # Validation result model
│   └── health.rs                # Health check model
├── repository/
│   └── deployment.rs            # Async data access
└── observability/
    ├── logging.rs               # Structured logging
    ├── metrics.rs               # Prometheus metrics
    └── tracing.rs               # Distributed tracing

2.3 Terminal UI (Go Bubbletea)

Purpose: Delightful, keyboard-centric terminal interface for CI/CD management. Technology Stack:

Framework: Charmbracelet Bubbletea
Terminal Rendering: tcell for cross-platform terminal support
Colors: charmbracelet/lipgloss for styling
Input Handling: Custom keybind mapping
WebSocket Client: gorilla/websocket for real-time updates
Graphics: ASCII art and Unicode support

Core Modules:

cmd/tui/
├── main.go                      # TUI entry point
└── config.go                    # Configuration loading
internal/tui/
├── app/
│   ├── app.go                   # Main Bubbletea model
│   ├── state.go                 # Application state
│   └── theme.go                 # Theme configuration
├── views/
│   ├── overview.go              # Overview dashboard
│   ├── pipelines.go             # Pipeline list view
│   ├── builds.go                # Build monitor view
│   ├── deployments.go           # Deployment view
│   └── settings.go              # Settings view
├── components/
│   ├── pipeline_list.go         # Pipeline list widget
│   ├── build_progress.go        # Build progress bar
│   ├── log_viewer.go            # Log streaming viewer
│   └── status_bar.go            # Status indicator
├── keybinds/
│   └── keymap.go                # Keyboard shortcuts
└── client/
    ├── api.go                   # API client
    └── websocket.go             # WebSocket client

2.4 Shield Authentication Service (Django)

Purpose: Identity and access management for users, workspaces, and RBAC. Technology Stack:

Framework: Django 4.2+
Database: PostgreSQL via psycopg2
Caching: Redis via django-redis
Password Hashing: bcrypt
JWT: djangorestframework-simplejwt
OAuth/SAML: django-allauth, python3-saml
Async Tasks: Celery for async operations

Core Modules:

manage.py
config/
├── settings.py                  # Django settings
├── urls.py                      # URL routing
└── wsgi.py                      # WSGI entry point
apps/
├── authentication/
│   ├── models.py                # User, tokens, refresh models
│   ├── views.py                 # Auth endpoints
│   ├── serializers.py           # Request/response serialization
│   └── permissions.py           # Custom permissions
├── workspaces/
│   ├── models.py                # Workspace, team models
│   ├── views.py                 # Workspace management endpoints
│   └── admin.py                 # Admin dashboard
├── rbac/
│   ├── models.py                # Role, permission models
│   ├── services.py              # Permission checking logic
│   └── cache.py                 # Permission cache management
└── integrations/
    ├── oauth.py                 # OAuth 2.0 handlers
    └── saml.py                  # SAML 2.0 handlers

3. Data Architecture

3.1 PostgreSQL Schema

Core Tables:

-- Users and authentication
CREATE TABLE users (
    id UUID PRIMARY KEY,
    email VARCHAR(255) NOT NULL UNIQUE,
    name VARCHAR(255),
    password_hash VARCHAR(255) NOT NULL,
    email_verified BOOLEAN DEFAULT FALSE,
    created_at TIMESTAMP DEFAULT NOW(),
    updated_at TIMESTAMP DEFAULT NOW()
);

-- Workspaces
CREATE TABLE workspaces (
    id UUID PRIMARY KEY,
    name VARCHAR(255) NOT NULL,
    slug VARCHAR(255) NOT NULL UNIQUE,
    owner_id UUID REFERENCES users(id),
    created_at TIMESTAMP DEFAULT NOW(),
    updated_at TIMESTAMP DEFAULT NOW()
);

-- Projects
CREATE TABLE projects (
    id UUID PRIMARY KEY,
    workspace_id UUID REFERENCES workspaces(id),
    name VARCHAR(255) NOT NULL,
    git_url VARCHAR(255) NOT NULL,
    framework VARCHAR(50),
    language VARCHAR(50),
    created_at TIMESTAMP DEFAULT NOW(),
    updated_at TIMESTAMP DEFAULT NOW()
);

-- Pipelines
CREATE TABLE pipelines (
    id UUID PRIMARY KEY,
    project_id UUID REFERENCES projects(id),
    name VARCHAR(255) NOT NULL,
    configuration JSONB NOT NULL,
    version INT DEFAULT 1,
    created_at TIMESTAMP DEFAULT NOW(),
    updated_at TIMESTAMP DEFAULT NOW()
);

-- Builds
CREATE TABLE builds (
    id UUID PRIMARY KEY,
    pipeline_id UUID REFERENCES pipelines(id),
    commit_sha VARCHAR(40),
    branch VARCHAR(255),
    status VARCHAR(50),
    started_at TIMESTAMP,
    completed_at TIMESTAMP,
    created_at TIMESTAMP DEFAULT NOW()
);

-- Deployments
CREATE TABLE deployments (
    id UUID PRIMARY KEY,
    build_id UUID REFERENCES builds(id),
    stage VARCHAR(50),
    status VARCHAR(50),
    platform VARCHAR(50),
    strategy VARCHAR(50),
    started_at TIMESTAMP,
    completed_at TIMESTAMP,
    created_at TIMESTAMP DEFAULT NOW()
);

-- Audit logs
CREATE TABLE audit_logs (
    id UUID PRIMARY KEY,
    user_id UUID REFERENCES users(id),
    workspace_id UUID REFERENCES workspaces(id),
    event_type VARCHAR(50),
    resource_type VARCHAR(50),
    resource_id UUID,
    action VARCHAR(50),
    details JSONB,
    created_at TIMESTAMP DEFAULT NOW()
);

Indexes:

-- Query performance indexes
CREATE INDEX idx_projects_workspace ON projects(workspace_id);
CREATE INDEX idx_pipelines_project ON pipelines(project_id);
CREATE INDEX idx_builds_pipeline ON builds(pipeline_id);
CREATE INDEX idx_deployments_build ON deployments(build_id);
CREATE INDEX idx_audit_logs_workspace ON audit_logs(workspace_id);
CREATE INDEX idx_audit_logs_timestamp ON audit_logs(created_at);

3.2 Redis Cache Strategy

Cache Keys Pattern:

# User/auth cache
user:{user_id}                          → User object
user:{user_id}:permissions:{workspace}  → Permission set
user:{user_id}:workspaces               → Workspace list
token:refresh:{token}                   → Refresh token metadata

# Project/pipeline cache
project:{project_id}                    → Project metadata
pipeline:{pipeline_id}                  → Pipeline configuration
build:{build_id}:logs                   → Build log stream
deployment:{deployment_id}:status       → Deployment status

# Session cache
session:{session_id}                    → Session data

Cache TTLs:

User profiles: 1 hour
Permissions: 15 minutes
Pipelines: 5 minutes
Build logs: Streaming (no TTL)
Deployments: 10 minutes
Sessions: 24 hours

3.3 Object Storage (S3/MinIO)

Bucket Structure:

sparki-artifacts/
├── projects/{project_id}/
│   ├── builds/{build_id}/
│   │   ├── artifacts/
│   │   ├── logs/
│   │   └── test-reports/
│   └── deployments/{deployment_id}/
│       └── logs/

sparki-backups/
├── database-backups/
├── audit-logs/
└── deployment-snapshots/

4. Storage Integration Strategy

4.1 Fiber Storage Adapter Selection

Primary Choices for Sparki:

Adapter	Use Case	Why Selected
PostgreSQL	Primary database	ACID compliance, scalability, proven reliability
Redis	Caching layer	Sub-millisecond performance, native data structures
S3	Build artifacts	Scalability, durability, cost efficiency
Badger	Embedded state (optional)	Fast embedded key-value for local caching
SurrealDB	Future real-time DB	Multi-platform, time-series for metrics

4.2 Storage Adapter Implementation

// Fiber storage adapter interface
type StorageAdapter interface {
    // Key-value operations
    Get(ctx context.Context, key string) (string, error)
    Set(ctx context.Context, key string, value string, ttl time.Duration) error
    Delete(ctx context.Context, key string) error

    // List operations
    List(ctx context.Context, prefix string) ([]string, error)

    // Cleanup
    Close() error
}

// PostgreSQL adapter (primary)
type PostgresAdapter struct {
    db *pgx.Conn
}

// Redis adapter (caching)
type RedisAdapter struct {
    client *redis.Client
}

// S3 adapter (artifacts)
type S3Adapter struct {
    client *s3.Client
}

5. Observability Architecture

5.1 Logging Stack

Three-Tier Logging Architecture:

Application Logs (JSON)
        ↓
Structured Logging Layer (Fiber middleware)
        ↓
Log Aggregation (Filebeat/Logstash)
        ↓
Elasticsearch (Centralized storage)
        ↓
Kibana (Log visualization & analysis)

Log Format:

{
    "timestamp": "2025-12-03T10:30:45.123Z",
    "level": "INFO",
    "service": "sparki-api",
    "component": "build-executor",
    "correlation_id": "req_xyz789",
    "user_id": "user_123",
    "workspace_id": "ws_456",
    "message": "Build started successfully",
    "metadata": {
        "build_id": "build_789",
        "pipeline_id": "pipe_123",
        "duration_ms": 1234
    }
}

5.2 Metrics Stack

Prometheus Metrics:

# API Performance
sparki_api_request_duration_seconds{method, endpoint, status} - Histogram
sparki_api_request_errors_total{method, endpoint, error_type} - Counter
sparki_api_active_connections{} - Gauge

# Build Metrics
sparki_build_duration_seconds{project, framework} - Histogram
sparki_build_success_total{project, framework} - Counter
sparki_build_cache_hits_total{} - Counter

# Deployment Metrics
sparki_deployment_duration_seconds{stage, platform} - Histogram
sparki_deployment_success_total{stage, platform} - Counter
sparki_deployment_rollback_total{reason} - Counter

# System Metrics
sparki_system_memory_bytes - Gauge
sparki_system_cpu_usage{core} - Gauge
process_goroutines_count - Gauge

Visualization:

Grafana dashboards for real-time monitoring
Custom dashboards for pipeline/deployment metrics
Alert rules for performance/reliability thresholds

5.3 Distributed Tracing

Jaeger Integration:

Application Traces
        ↓
Jaeger Agent (local collector)
        ↓
Jaeger Collector (centralized)
        ↓
Elasticsearch (trace storage)
        ↓
Jaeger UI (trace visualization)

Trace Propagation:

Correlation IDs across all requests
Span tracking through service calls
Performance analysis per service

6. Deployment Architecture

6.1 Kubernetes Deployment

Service Topology:

Ingress (NGINX/Traefik)
    ↓
┌─────────────────────────────┐
│ Sparki API (3+ replicas)    │
│ (Go Fiber, Auto-scaling)    │
└──────────┬────────────────┬─┘
           │                │
    ┌──────▼──────┐    ┌────▼──────┐
    │PostgreSQL   │    │Redis       │
    │(HA mode)    │    │(Cluster)   │
    └─────────────┘    └────────────┘

    ┌─────────────────────────────┐
    │ Loco Engine (2+ replicas)   │
    │ (Rust Axum, Job queue)      │
    └─────────────────────────────┘

    ┌─────────────────────────────┐
    │ Shield Auth (2+ replicas)   │
    │ (Django, Session store)     │
    └─────────────────────────────┘

Resource Requests:

Service	CPU	Memory	Replicas	Notes
Sparki API	2000m	2Gi	3+	Auto-scaling enabled
Loco Engine	1000m	1Gi	2+	Job queue backed
Shield Auth	1000m	1Gi	2+	Session stateful
PostgreSQL	2000m	4Gi	1 master	HA via replication
Redis	1000m	2Gi	3 nodes	Cluster mode

6.2 High Availability Strategy

Service-Level Agreements:

API: 99.95% uptime SLA
Loco: 99.95% uptime SLA
Auth: 99.95% uptime SLA

HA Components:

Multi-replica deployments with load balancing
Database replication and automatic failover
Redis cluster with sentinel monitoring
Cross-region deployment option

7. Security Architecture

7.1 Authentication Flow

Developer Login
        ↓
OAuth 2.0 / SAML 2.0 / Email+Password
        ↓
Shield Authentication Service
        ↓
JWT Token Generation
        ↓
Return Access Token + Refresh Token
        ↓
Store in secure HttpOnly cookie (Web)
        ↓
Use in Authorization header (CLI/API)

7.2 Permission Validation Flow

Request with JWT
        ↓
API Middleware: Validate JWT signature
        ↓
Authorization Service: Check permissions
        ↓
Cache lookup (Redis) - &lt;1ms hit
        ↓
Database lookup if cache miss
        ↓
Cache result for 15 minutes
        ↓
Allow/Deny request

7.3 Encryption Strategy

In Transit: TLS 1.3 for all external traffic
At Rest: AES-256 for sensitive data in database
Credentials: Bcrypt for password hashing, encrypted vault for API keys

8. Scalability Patterns

8.1 Horizontal Scaling

Stateless Services:

Sparki API: Scale horizontally without session affinity
Loco Engine: Scale via job queue (no local state)
Shield Auth: Scale via shared token store (Redis)

Stateful Services:

PostgreSQL: Vertical scaling + read replicas
Redis: Cluster mode for horizontal scaling
Object Storage: Inherently scalable (S3)

8.2 Performance Optimization

Caching Strategy:

Framework detection results: 5 minutes
Pipeline configurations: 5 minutes
User permissions: 15 minutes
Project metadata: 1 hour

Database Optimization:

Indexed queries for frequent operations
Connection pooling to reduce overhead
Query optimization and monitoring
Read replicas for scaling read-heavy workloads

9. Disaster Recovery

9.1 Backup Strategy

Database Backups:

Continuous replication to standby
Daily snapshots to S3
Weekly full backups for archives
30-day retention policy

Configuration Backups:

Version control for all pipeline configurations
Immutable audit logs
Configuration snapshots before changes

9.2 Recovery Procedures

RTO (Recovery Time Objective): <15 minutes RPO (Recovery Point Objective): <5 minutes Recovery Steps:

Automated failover to replica
Restore from latest snapshot if needed
Verify data integrity
Resume operations

10. Compliance & Security

10.1 Data Privacy

GDPR Compliance: Data subject rights, right to be forgotten
HIPAA Compliance: Encryption, audit logging, access controls
SOC 2 Type II: Annual audit, continuous controls

10.2 Audit Logging

All authentication, authorization, and data access events logged:

User: Who performed action
Resource: What was affected
Action: What happened
Timestamp: When it occurred
Result: Success/failure
IP Address: Where from

Conclusion

Sparki’s technical architecture is designed around core principles of performance, reliability, and developer experience. The combination of Go Fiber for ultra-fast HTTP performance and Rust Axum for deterministic deployment orchestration positions Sparki as the highest-performance CI/CD platform available. The architecture supports millions of concurrent developers, delivers sub-50ms API response times, and provides 99.95% availability required for mission-critical production workloads.

Document History:

Version	Date	Author	Changes
1.0	2025-12-03	Sparki Engineering	Initial technical architecture

Getting Started

User Guides

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DevOps

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Specifications

Monetization

Reports & Status

​Sparki Technical Architecture Document

​1. Architecture Overview

​1.1 System Architecture Diagram

​1.2 Layered Architecture

​2. Core Components

​2.1 Sparki API (Go Fiber)

​2.2 Loco Deployment Engine (Rust Axum/Tokio)

​2.3 Terminal UI (Go Bubbletea)

​2.4 Shield Authentication Service (Django)

​3. Data Architecture

​3.1 PostgreSQL Schema

​3.2 Redis Cache Strategy

​3.3 Object Storage (S3/MinIO)

​4. Storage Integration Strategy

​4.1 Fiber Storage Adapter Selection

​4.2 Storage Adapter Implementation

​5. Observability Architecture

​5.1 Logging Stack

​5.2 Metrics Stack

​5.3 Distributed Tracing

​6. Deployment Architecture

​6.1 Kubernetes Deployment

​6.2 High Availability Strategy

​7. Security Architecture

​7.1 Authentication Flow

​7.2 Permission Validation Flow

​7.3 Encryption Strategy

​8. Scalability Patterns

​8.1 Horizontal Scaling

​8.2 Performance Optimization

​9. Disaster Recovery

​9.1 Backup Strategy

​9.2 Recovery Procedures

​10. Compliance & Security

​10.1 Data Privacy

​10.2 Audit Logging

​Conclusion

Sparki Technical Architecture Document

1. Architecture Overview

1.1 System Architecture Diagram

1.2 Layered Architecture

2. Core Components

2.1 Sparki API (Go Fiber)

2.2 Loco Deployment Engine (Rust Axum/Tokio)

2.3 Terminal UI (Go Bubbletea)

2.4 Shield Authentication Service (Django)

3. Data Architecture

3.1 PostgreSQL Schema

3.2 Redis Cache Strategy

3.3 Object Storage (S3/MinIO)

4. Storage Integration Strategy

4.1 Fiber Storage Adapter Selection

4.2 Storage Adapter Implementation

5. Observability Architecture

5.1 Logging Stack

5.2 Metrics Stack

5.3 Distributed Tracing

6. Deployment Architecture

6.1 Kubernetes Deployment

6.2 High Availability Strategy

7. Security Architecture

7.1 Authentication Flow

7.2 Permission Validation Flow

7.3 Encryption Strategy

8. Scalability Patterns

8.1 Horizontal Scaling

8.2 Performance Optimization

9. Disaster Recovery

9.1 Backup Strategy

9.2 Recovery Procedures

10. Compliance & Security

10.1 Data Privacy

10.2 Audit Logging

Conclusion