Comprehensive System Architecture Guide for NetPulse
Real-time Network Operations Center (NOC) Monitoring Platform
- Overview
- System Architecture
- Component Architecture
- Data Flow
- Technology Stack
- Database Design
- Security Architecture
- Deployment Architecture
- Scalability Considerations
- Monitoring & Observability
NetPulse follows a modern microservices architecture with clear separation of concerns, designed for high availability, scalability, and maintainability. The system consists of 6 independent microservices that communicate through well-defined APIs, message queues, and an API Gateway that handles routing and authentication.
- Microservices: 6 independently deployable services with single responsibilities
- API Gateway Pattern: Centralized routing, authentication, and cross-cutting concerns
- Event-Driven: Asynchronous communication using RabbitMQ and Celery
- Domain-Driven Design: Services organized around business capabilities
- Security-First: JWT-based authentication with service-to-service verification
- Observable: Comprehensive logging, metrics, and distributed tracing
- Cloud-Native: Container-first design optimized for Kubernetes deployment
| Service | Port | Responsibility | Database |
|---|---|---|---|
| API Gateway | 8000 | Request routing, authentication | None |
| Auth Service | 8001 | User authentication, JWT tokens | PostgreSQL |
| Device Service | 8002 | Device CRUD operations | PostgreSQL |
| Monitoring Service | 8003 | Real-time monitoring, WebSocket | TimescaleDB |
| Alert Service | 8004 | Alert processing, correlation | PostgreSQL |
| Notification Service | 8005 | Multi-channel notifications | None |
| Reporting Service | 8006 | Analytics, insights generation | PostgreSQL |
graph TB
subgraph "Client Layer"
WEB[Web Dashboard<br/>React + TypeScript]
MOBILE[Mobile App<br/>React Native]
API_CLIENT[API Clients<br/>External Systems]
end
subgraph "Load Balancer"
LB[nginx/CloudFlare<br/>SSL Termination]
end
subgraph "API Gateway Layer"
GATEWAY[API Gateway Service<br/>Port 8000<br/>FastAPI + Request Routing]
end
subgraph "Microservices"
AUTH[Auth Service<br/>Port 8001<br/>JWT Authentication]
DEVICE[Device Service<br/>Port 8002<br/>Device Management]
MONITOR[Monitoring Service<br/>Port 8003<br/>Real-time Monitoring]
ALERT[Alert Service<br/>Port 8004<br/>Alert Processing]
NOTIFY[Notification Service<br/>Port 8005<br/>Multi-channel Notifications]
REPORT[Reporting Service<br/>Port 8006<br/>Analytics & Reports]
end
subgraph "Data Layer"
POSTGRES[(PostgreSQL 15<br/>Primary Database)]
TIMESCALE[(TimescaleDB<br/>Time-series Metrics)]
REDIS[(Redis 7<br/>Cache & Sessions)]
end
subgraph "Message Queue"
RABBIT[RabbitMQ<br/>Message Broker]
CELERY[Celery Workers<br/>Background Tasks]
end
subgraph "External Services"
SENDGRID[SendGrid<br/>Email Service]
AFRICAS[Africa's Talking<br/>SMS Service]
WEBHOOKS[Webhook Endpoints<br/>External Integrations]
end
subgraph "Monitoring Stack"
PROMETHEUS[Prometheus<br/>Metrics Collection]
GRAFANA[Grafana<br/>Visualization]
JAEGER[Jaeger<br/>Distributed Tracing]
end
%% Client connections
WEB --> LB
MOBILE --> LB
API_CLIENT --> LB
%% Load balancer to gateway
LB --> GATEWAY
%% Gateway to services
GATEWAY --> AUTH
GATEWAY --> DEVICE
GATEWAY --> MONITOR
GATEWAY --> ALERT
GATEWAY --> NOTIFY
GATEWAY --> REPORT
%% Service to database connections
AUTH --> POSTGRES
DEVICE --> POSTGRES
MONITOR --> TIMESCALE
ALERT --> POSTGRES
REPORT --> POSTGRES
%% Cache connections
AUTH --> REDIS
GATEWAY --> REDIS
MONITOR --> REDIS
%% Message queue connections
MONITOR --> RABBIT
ALERT --> RABBIT
NOTIFY --> RABBIT
RABBIT --> CELERY
%% External service connections
NOTIFY --> SENDGRID
NOTIFY --> AFRICAS
NOTIFY --> WEBHOOKS
%% Monitoring connections
GATEWAY -.-> PROMETHEUS
AUTH -.-> PROMETHEUS
DEVICE -.-> PROMETHEUS
MONITOR -.-> PROMETHEUS
ALERT -.-> PROMETHEUS
NOTIFY -.-> PROMETHEUS
REPORT -.-> PROMETHEUS
PROMETHEUS --> GRAFANA
style GATEWAY fill:#e1f5fe
style AUTH fill:#f3e5f5
style DEVICE fill:#e8f5e8
style MONITOR fill:#fff3e0
style ALERT fill:#ffebee
style NOTIFY fill:#f1f8e9
style REPORT fill:#e3f2fd
graph TB
subgraph "API Gateway"
MAIN[FastAPI Application]
ROUTER[Request Router]
AUTH_MID[Authentication Middleware]
CORS[CORS Middleware]
RATE_LIMIT[Rate Limiting]
end
subgraph "Routing Logic"
ROUTE_MAP[Route Mapping]
SERVICE_DISCO[Service Discovery]
LOAD_BAL[Load Balancing]
end
subgraph "Security"
JWT_VERIFY[JWT Verification]
TOKEN_REFRESH[Token Refresh]
RBAC[Role-based Access]
end
MAIN --> ROUTER
MAIN --> AUTH_MID
MAIN --> CORS
MAIN --> RATE_LIMIT
ROUTER --> ROUTE_MAP
ROUTER --> SERVICE_DISCO
ROUTER --> LOAD_BAL
AUTH_MID --> JWT_VERIFY
AUTH_MID --> TOKEN_REFRESH
AUTH_MID --> RBAC
Responsibilities:
- Route requests to appropriate microservices
- JWT token validation and refresh
- Rate limiting and CORS handling
- Service discovery and load balancing
- Request/response transformation
graph TB
subgraph "Auth Service"
AUTH_API[FastAPI Endpoints]
USER_MGR[User Management]
ORG_MGR[Organization Management]
TOKEN_MGR[Token Management]
end
subgraph "Security Layer"
PWD_HASH[Password Hashing]
JWT_GEN[JWT Generation]
REFRESH[Token Refresh]
VALIDATION[Token Validation]
end
subgraph "Database"
USER_MODEL[User Model]
ORG_MODEL[Organization Model]
SESSION_MODEL[Session Model]
end
AUTH_API --> USER_MGR
AUTH_API --> ORG_MGR
AUTH_API --> TOKEN_MGR
USER_MGR --> PWD_HASH
TOKEN_MGR --> JWT_GEN
TOKEN_MGR --> REFRESH
TOKEN_MGR --> VALIDATION
USER_MGR --> USER_MODEL
ORG_MGR --> ORG_MODEL
TOKEN_MGR --> SESSION_MODEL
Responsibilities:
- User registration and authentication
- JWT token generation and validation
- Organization management
- Role-based access control
- Session management
graph TB
subgraph "Device Service"
DEVICE_API[FastAPI Endpoints]
DEVICE_CRUD[Device CRUD]
STATUS_MGR[Status Management]
CONFIG_MGR[Configuration Management]
end
subgraph "Business Logic"
VALIDATION[Device Validation]
INVENTORY[Inventory Management]
GROUPING[Device Grouping]
METADATA[Metadata Management]
end
subgraph "Database"
DEVICE_MODEL[Device Model]
CONFIG_MODEL[Configuration Model]
GROUP_MODEL[Device Group Model]
end
DEVICE_API --> DEVICE_CRUD
DEVICE_API --> STATUS_MGR
DEVICE_API --> CONFIG_MGR
DEVICE_CRUD --> VALIDATION
STATUS_MGR --> INVENTORY
CONFIG_MGR --> GROUPING
CONFIG_MGR --> METADATA
DEVICE_CRUD --> DEVICE_MODEL
CONFIG_MGR --> CONFIG_MODEL
GROUPING --> GROUP_MODEL
Responsibilities:
- Device registration and management
- Device status tracking
- Configuration management
- Device grouping and tagging
- Inventory management
graph TB
subgraph "Monitoring Service"
MONITOR_API[FastAPI + WebSocket]
HEALTH_CHECK[Health Checking]
METRIC_COL[Metrics Collection]
REALTIME[Real-time Streaming]
end
subgraph "Data Processing"
PING_SERVICE[Ping Monitoring]
SNMP_SERVICE[SNMP Collection]
METRIC_PROC[Metrics Processing]
THRESHOLD[Threshold Checking]
end
subgraph "Background Tasks"
CELERY_TASKS[Celery Tasks]
SCHEDULER[Task Scheduler]
WORKER_POOL[Worker Pool]
end
subgraph "Data Storage"
TIMESERIES[TimescaleDB]
METRIC_MODEL[Metrics Model]
REDIS_CACHE[Redis Cache]
end
MONITOR_API --> HEALTH_CHECK
MONITOR_API --> METRIC_COL
MONITOR_API --> REALTIME
HEALTH_CHECK --> PING_SERVICE
METRIC_COL --> SNMP_SERVICE
METRIC_COL --> METRIC_PROC
METRIC_PROC --> THRESHOLD
HEALTH_CHECK --> CELERY_TASKS
METRIC_COL --> SCHEDULER
SCHEDULER --> WORKER_POOL
METRIC_PROC --> TIMESERIES
METRIC_COL --> METRIC_MODEL
REALTIME --> REDIS_CACHE
Responsibilities:
- Real-time device monitoring
- Metrics collection and processing
- WebSocket streaming for live updates
- Background health checking
- Time-series data storage
graph TB
subgraph "Alert Service"
ALERT_API[FastAPI Endpoints]
ALERT_PROC[Alert Processing]
CORRELATION[Alert Correlation]
ESCALATION[Escalation Logic]
end
subgraph "Alert Logic"
THRESHOLD[Threshold Engine]
ANOMALY[Anomaly Detection]
SUPPRESSION[Alert Suppression]
GROUPING[Alert Grouping]
end
subgraph "Workflow"
ACK_MGR[Acknowledgment]
RESOLUTION[Resolution Tracking]
SLA_TRACK[SLA Tracking]
HISTORY[Alert History]
end
subgraph "Database"
ALERT_MODEL[Alert Model]
RULE_MODEL[Alert Rules Model]
HISTORY_MODEL[History Model]
end
ALERT_API --> ALERT_PROC
ALERT_API --> CORRELATION
ALERT_API --> ESCALATION
ALERT_PROC --> THRESHOLD
ALERT_PROC --> ANOMALY
CORRELATION --> SUPPRESSION
CORRELATION --> GROUPING
ESCALATION --> ACK_MGR
ESCALATION --> RESOLUTION
ESCALATION --> SLA_TRACK
ESCALATION --> HISTORY
ALERT_PROC --> ALERT_MODEL
THRESHOLD --> RULE_MODEL
HISTORY --> HISTORY_MODEL
Responsibilities:
- Alert processing and correlation
- Threshold-based alerting
- Alert acknowledgment and resolution
- Escalation workflows
- Alert history and reporting
graph TB
subgraph "Notification Service"
NOTIFY_API[FastAPI Endpoints]
CHANNEL_MGR[Channel Manager]
TEMPLATE_MGR[Template Manager]
DELIVERY[Delivery Engine]
end
subgraph "Notification Channels"
EMAIL[Email Service]
SMS[SMS Service]
WEBHOOK[Webhook Service]
SLACK[Slack Integration]
end
subgraph "External APIs"
SENDGRID[SendGrid API]
AFRICAS[Africa's Talking]
SLACK_API[Slack API]
CUSTOM_HOOKS[Custom Webhooks]
end
subgraph "Background Processing"
QUEUE[Message Queue]
RETRY[Retry Logic]
BATCH[Batch Processing]
TRACKING[Delivery Tracking]
end
NOTIFY_API --> CHANNEL_MGR
NOTIFY_API --> TEMPLATE_MGR
NOTIFY_API --> DELIVERY
CHANNEL_MGR --> EMAIL
CHANNEL_MGR --> SMS
CHANNEL_MGR --> WEBHOOK
CHANNEL_MGR --> SLACK
EMAIL --> SENDGRID
SMS --> AFRICAS
SLACK --> SLACK_API
WEBHOOK --> CUSTOM_HOOKS
DELIVERY --> QUEUE
DELIVERY --> RETRY
DELIVERY --> BATCH
DELIVERY --> TRACKING
Responsibilities:
- Multi-channel notification delivery
- Email, SMS, and webhook notifications
- Template management and personalization
- Delivery tracking and retry logic
- Integration with external services
graph TB
subgraph "Reporting Service"
REPORT_API[FastAPI Endpoints]
ANALYTICS[Analytics Engine]
EXPORT[Export Engine]
SCHEDULER[Report Scheduler]
end
subgraph "Report Types"
UPTIME[Uptime Reports]
PERFORMANCE[Performance Reports]
ALERT_RPT[Alert Reports]
CUSTOM[Custom Reports]
end
subgraph "Data Sources"
DEVICE_DATA[Device Data]
METRIC_DATA[Metrics Data]
ALERT_DATA[Alert Data]
USER_DATA[User Data]
end
subgraph "Output Formats"
PDF[PDF Export]
CSV[CSV Export]
JSON[JSON API]
DASHBOARD[Dashboard Views]
end
REPORT_API --> ANALYTICS
REPORT_API --> EXPORT
REPORT_API --> SCHEDULER
ANALYTICS --> UPTIME
ANALYTICS --> PERFORMANCE
ANALYTICS --> ALERT_RPT
ANALYTICS --> CUSTOM
UPTIME --> DEVICE_DATA
PERFORMANCE --> METRIC_DATA
ALERT_RPT --> ALERT_DATA
CUSTOM --> USER_DATA
EXPORT --> PDF
EXPORT --> CSV
EXPORT --> JSON
EXPORT --> DASHBOARD
Responsibilities:
- Uptime and performance reporting
- Alert analytics and insights
- Scheduled report generation
- Multi-format export capabilities
- Custom dashboard creation
sequenceDiagram
participant Device as Network Device
participant Monitor as Monitoring Service
participant Queue as RabbitMQ
participant Worker as Celery Worker
participant DB as TimescaleDB
participant WS as WebSocket
participant Client as Frontend
participant Alert as Alert Service
Device->>Monitor: PING/SNMP Health Check
Monitor->>Queue: Queue Metric Collection
Queue->>Worker: Process Metrics
Worker->>DB: Store Time-series Data
Worker->>WS: Broadcast Real-time Updates
WS->>Client: Live Dashboard Updates
Note over Worker,DB: Threshold Analysis
Worker->>Worker: Analyze Thresholds
Worker->>Alert: Send Alert if Threshold Exceeded
Alert->>Queue: Queue Alert for Processing
Queue->>Worker: Process Alert
Worker->>Client: Alert Notification
flowchart TD
START([Metric Received]) --> VALIDATE{Validate Data}
VALIDATE -->|Valid| THRESHOLD{Check Thresholds}
VALIDATE -->|Invalid| LOG[Log Error]
THRESHOLD -->|Normal| STORE[Store Metric]
THRESHOLD -->|Anomaly| CREATE_ALERT[Create Alert]
CREATE_ALERT --> SEVERITY{Determine Severity}
SEVERITY -->|Critical| IMMEDIATE[Immediate Notification]
SEVERITY -->|Warning| QUEUE_ALERT[Queue for Batch]
SEVERITY -->|Info| LOG_ONLY[Log Only]
IMMEDIATE --> MULTI_CHANNEL[Multi-channel Notification]
QUEUE_ALERT --> BATCH[Batch Processing]
MULTI_CHANNEL --> SMS[SMS Alert]
MULTI_CHANNEL --> EMAIL[Email Alert]
MULTI_CHANNEL --> WEBHOOK[Webhook Alert]
SMS --> TRACK[Track Delivery]
EMAIL --> TRACK
WEBHOOK --> TRACK
STORE --> END([End])
LOG --> END
LOG_ONLY --> END
TRACK --> END
BATCH --> END
sequenceDiagram
participant Client as Frontend
participant Gateway as API Gateway
participant Auth as Auth Service
participant Device as Device Service
participant Redis as Redis Cache
Client->>Gateway: POST /auth/login
Gateway->>Auth: Forward Login Request
Auth->>Auth: Validate Credentials
Auth->>Auth: Generate JWT Token
Auth->>Redis: Store Session Data
Auth->>Gateway: Return JWT Token
Gateway->>Client: Login Response with Token
Note over Client,Gateway: Subsequent API Calls
Client->>Gateway: API Request with JWT
Gateway->>Auth: Verify Token
Auth->>Redis: Check Session
Auth->>Gateway: Token Valid Response
Gateway->>Device: Forward to Service
Device->>Gateway: Service Response
Gateway->>Client: Final Response
| Service | Framework | Database | Key Libraries |
|---|---|---|---|
| API Gateway | FastAPI 0.104 | Redis (cache) | httpx, python-jose, uvicorn |
| Auth Service | FastAPI 0.104 | PostgreSQL | passlib, python-jose, sqlalchemy |
| Device Service | FastAPI 0.104 | PostgreSQL | sqlalchemy, pydantic, httpx |
| Monitoring Service | FastAPI 0.104 | TimescaleDB | celery, redis, websockets |
| Alert Service | FastAPI 0.104 | PostgreSQL | celery, sqlalchemy, redis |
| Notification Service | FastAPI 0.104 | None | requests, celery, sendgrid |
| Reporting Service | FastAPI 0.104 | PostgreSQL | pandas, matplotlib, sqlalchemy |
| Component | Technology | Version | Purpose |
|---|---|---|---|
| Container Runtime | Docker | 24.x | Application containerization |
| Orchestration | Docker Compose | 2.x | Local development orchestration |
| Load Balancer | nginx | 1.25 | Request distribution and SSL termination |
| Message Queue | RabbitMQ | 3.12 | Asynchronous task processing |
| Cache | Redis | 7.x | Session storage and caching |
| Database | PostgreSQL | 15.x | Primary data storage |
| Time-series DB | TimescaleDB | 2.x | Metrics and time-series data |
| Monitoring | Prometheus | 2.x | Metrics collection |
| Visualization | Grafana | 10.x | Dashboards and alerting |
| Tracing | Jaeger | 1.x | Distributed tracing |
| Category | Tool | Purpose |
|---|---|---|
| Code Quality | Black, Flake8 | Python code formatting and linting |
| Testing | pytest, pytest-cov | Unit and integration testing |
| API Documentation | FastAPI/OpenAPI | Automatic API documentation |
| Type Checking | mypy | Static type checking |
| Dependency Management | pip-tools | Python dependency management |
| CI/CD | GitHub Actions | Continuous integration and deployment |
-- Organizations table (used by auth-service)
CREATE TABLE organizations (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
name VARCHAR(255) NOT NULL,
slug VARCHAR(100) UNIQUE NOT NULL,
created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);
-- Users table (auth-service)
CREATE TABLE users (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
organization_id UUID REFERENCES organizations(id),
email VARCHAR(255) UNIQUE NOT NULL,
hashed_password VARCHAR(255) NOT NULL,
full_name VARCHAR(255),
is_active BOOLEAN DEFAULT true,
is_superuser BOOLEAN DEFAULT false,
created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);
-- Devices table (device-service)
CREATE TABLE devices (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
organization_id UUID NOT NULL,
name VARCHAR(255) NOT NULL,
ip_address INET NOT NULL,
device_type VARCHAR(50) NOT NULL,
location VARCHAR(255),
status VARCHAR(20) DEFAULT 'unknown',
last_seen TIMESTAMP WITH TIME ZONE,
created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);
-- Alerts table (alert-service)
CREATE TABLE alerts (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
device_id UUID,
organization_id UUID NOT NULL,
alert_type VARCHAR(50) NOT NULL,
severity VARCHAR(20) NOT NULL CHECK (severity IN ('info', 'warning', 'critical')),
message TEXT NOT NULL,
acknowledged BOOLEAN DEFAULT false,
acknowledged_by UUID,
acknowledged_at TIMESTAMP WITH TIME ZONE,
resolved BOOLEAN DEFAULT false,
resolved_at TIMESTAMP WITH TIME ZONE,
created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);
-- Device metrics table (monitoring-service with TimescaleDB)
CREATE TABLE device_metrics (
time TIMESTAMPTZ NOT NULL,
device_id UUID NOT NULL,
metric_type VARCHAR(50) NOT NULL,
value DOUBLE PRECISION NOT NULL,
unit VARCHAR(20),
PRIMARY KEY (time, device_id, metric_type)
);
-- Convert to TimescaleDB hypertable for time-series optimization
SELECT create_hypertable('device_metrics', 'time');
-- Performance indexes
CREATE INDEX idx_users_organization ON users(organization_id);
CREATE INDEX idx_users_email ON users(email);
CREATE INDEX idx_devices_organization ON devices(organization_id);
CREATE INDEX idx_devices_status ON devices(status);
CREATE INDEX idx_alerts_organization_severity ON alerts(organization_id, severity);
CREATE INDEX idx_alerts_device_time ON alerts(device_id, created_at DESC);
CREATE INDEX idx_device_metrics_device_time ON device_metrics(device_id, time DESC);
CREATE INDEX idx_device_metrics_type_time ON device_metrics(metric_type, time DESC);
-- Retention policies for time-series data
SELECT add_retention_policy('device_metrics', INTERVAL '90 days');# Session Management (auth-service)
session:{session_id}:
user_id: UUID
organization_id: UUID
expires_at: timestamp
permissions: array
# JWT Token Blacklist (auth-service)
blacklist:token:{jti}: expiration_timestamp
# Device Status Cache (monitoring-service)
device:status:{device_id}:
status: online|offline|unknown
last_seen: timestamp
response_time: float
# Real-time Metrics Cache (monitoring-service)
metrics:{device_id}:latest:
cpu: float
memory: float
network: float
disk: float
timestamp: timestamp
# Alert Counters (alert-service)
alerts:count:{organization_id}: integer
alerts:critical:{organization_id}: integer
alerts:unresolved:{organization_id}: integer
# Rate Limiting (api-gateway)
rate_limit:{user_id}:{endpoint}: request_count
rate_limit:{ip}:{endpoint}: request_count
# Background Task Results (celery)
celery-task-meta-{task_id}: task_result_jsonflowchart TD
CLIENT[Client Request] --> GATEWAY[API Gateway]
GATEWAY --> JWT_CHECK{JWT Valid?}
JWT_CHECK -->|No| AUTH_SERVICE[Auth Service]
AUTH_SERVICE --> LOGIN[User Login]
LOGIN --> JWT_GENERATE[Generate JWT]
JWT_GENERATE --> REDIS_SESSION[Store Session]
JWT_CHECK -->|Yes| EXTRACT_CLAIMS[Extract Claims]
EXTRACT_CLAIMS --> RBAC{Check Permissions}
RBAC -->|Authorized| FORWARD[Forward to Service]
RBAC -->|Denied| FORBIDDEN[403 Forbidden]
FORWARD --> MICROSERVICE[Target Microservice]
MICROSERVICE --> VALIDATE_ORG[Validate Organization]
VALIDATE_ORG --> BUSINESS_LOGIC[Execute Business Logic]
REDIS_SESSION --> RESPONSE[Return Response]
BUSINESS_LOGIC --> RESPONSE
FORBIDDEN --> RESPONSE
| Layer | Component | Security Measures |
|---|---|---|
| Network | nginx/Load Balancer | TLS 1.3, Rate limiting, DDoS protection |
| API Gateway | FastAPI Gateway | JWT validation, Request sanitization, CORS |
| Service-to-Service | Internal APIs | mTLS, Service mesh authentication |
| Data | PostgreSQL/Redis | Encryption at rest, Connection encryption |
| Application | Microservices | Input validation, SQL injection prevention |
| Container | Docker | Non-root users, Minimal base images |
| Secrets | Environment Variables | Encrypted environment variables, Vault integration |
# JWT Token Structure
{
"sub": "user_id",
"org": "organization_id",
"exp": 1234567890,
"iat": 1234567890,
"jti": "token_unique_id",
"permissions": ["device:read", "alert:write"],
"scopes": ["monitoring", "alerts"]
}
# Role-Based Access Control
PERMISSIONS = {
"admin": ["*"],
"operator": ["device:read", "device:write", "alert:read", "alert:write"],
"viewer": ["device:read", "alert:read", "report:read"],
"api_user": ["device:read", "metrics:read"]
}
# Rate Limiting Rules
RATE_LIMITS = {
"auth_endpoints": "5/minute",
"api_endpoints": "100/minute",
"monitoring_endpoints": "1000/minute",
"bulk_operations": "10/hour"
}| Service | Expected Load | Response Time | Throughput |
|---|---|---|---|
| API Gateway | 10k req/min | < 10ms | 1000 RPS |
| Auth Service | 1k req/min | < 100ms | 100 RPS |
| Device Service | 5k req/min | < 50ms | 500 RPS |
| Monitoring Service | 50k metrics/min | < 200ms | 2000 RPS |
| Alert Service | 1k alerts/min | < 100ms | 200 RPS |
| Notification Service | 500 notifications/min | < 500ms | 100 RPS |
| Reporting Service | 100 reports/hour | < 2s | 10 RPS |
flowchart TD
CLIENT[Client Request] --> NGINX[nginx Cache]
NGINX -->|Cache Miss| GATEWAY[API Gateway]
NGINX -->|Cache Hit| RETURN_CACHED[Return Cached Response]
GATEWAY --> REDIS[Redis Cache]
REDIS -->|Cache Hit| RETURN_REDIS[Return from Redis]
REDIS -->|Cache Miss| SERVICE[Microservice]
SERVICE --> DATABASE[Database Query]
DATABASE --> CACHE_STORE[Store in Cache]
CACHE_STORE --> RETURN_FRESH[Return Fresh Data]
RETURN_CACHED --> CLIENT
RETURN_REDIS --> CLIENT
RETURN_FRESH --> CLIENT
-- TimescaleDB Optimizations
-- Continuous aggregates for real-time dashboards
CREATE MATERIALIZED VIEW device_metrics_hourly
WITH (timescaledb.continuous) AS
SELECT device_id,
time_bucket('1 hour', time) as bucket,
AVG(value) as avg_value,
MAX(value) as max_value,
MIN(value) as min_value,
COUNT(*) as data_points
FROM device_metrics
WHERE metric_type = 'cpu_usage'
GROUP BY device_id, bucket;
-- Retention policies
SELECT add_retention_policy('device_metrics', INTERVAL '90 days');
SELECT add_retention_policy('device_metrics_hourly', INTERVAL '1 year');
-- Compression policies
SELECT add_compression_policy('device_metrics', INTERVAL '7 days');
-- PostgreSQL optimizations
-- Partitioning for large tables
CREATE TABLE alerts_y2024 PARTITION OF alerts
FOR VALUES FROM ('2024-01-01') TO ('2025-01-01');
-- Indexes for common queries
CREATE INDEX CONCURRENTLY idx_alerts_org_created
ON alerts(organization_id, created_at DESC)
WHERE resolved = false;
CREATE INDEX CONCURRENTLY idx_devices_status_location
ON devices(status, location)
WHERE is_active = true;# docker-compose.prod.yml scaling
version: '3.8'
services:
api-gateway:
deploy:
replicas: 3
resources:
limits:
cpus: '1.0'
memory: 512M
auth-service:
deploy:
replicas: 2
resources:
limits:
cpus: '0.5'
memory: 256M
device-service:
deploy:
replicas: 3
resources:
limits:
cpus: '0.5'
memory: 512M
monitoring-service:
deploy:
replicas: 5
resources:
limits:
cpus: '1.0'
memory: 1G
# Load balancer configuration
nginx:
image: nginx:alpine
ports:
- "80:80"
- "443:443"
volumes:
- ./nginx.conf:/etc/nginx/nginx.conf
depends_on:
- api-gatewayflowchart TD
SERVICES[Microservices] --> PROMETHEUS[Prometheus]
SERVICES --> JAEGER[Jaeger Tracing]
SERVICES --> LOGS[Centralized Logging]
PROMETHEUS --> GRAFANA[Grafana Dashboards]
PROMETHEUS --> ALERTMANAGER[Alert Manager]
JAEGER --> TRACE_UI[Tracing UI]
LOGS --> ELK[ELK Stack]
GRAFANA --> ALERTS[Visual Alerts]
ALERTMANAGER --> NOTIFICATION[Notification Channels]
NOTIFICATION --> SLACK[Slack]
NOTIFICATION --> EMAIL[Email]
NOTIFICATION --> PAGERDUTY[PagerDuty]
| Category | Metric | Target | Alert Threshold |
|---|---|---|---|
| Availability | Service Uptime | 99.9% | < 99.5% |
| Performance | API Response Time | < 100ms | > 500ms |
| Throughput | Requests/second | 1000 RPS | < 500 RPS |
| Errors | Error Rate | < 0.1% | > 1% |
| Resources | CPU Utilization | < 70% | > 85% |
| Resources | Memory Usage | < 80% | > 90% |
| Database | Query Performance | < 50ms | > 200ms |
| Queue | Message Lag | < 1 minute | > 5 minutes |
# Custom metrics in microservices
from prometheus_client import Counter, Histogram, Gauge
# Request counters
REQUEST_COUNT = Counter(
'http_requests_total',
'Total HTTP requests',
['method', 'endpoint', 'status']
)
# Response time histogram
REQUEST_DURATION = Histogram(
'http_request_duration_seconds',
'HTTP request duration',
['method', 'endpoint']
)
# Active connections gauge
ACTIVE_CONNECTIONS = Gauge(
'active_database_connections',
'Active database connections'
)
# Device-specific metrics
DEVICE_STATUS = Gauge(
'device_online_status',
'Device online status (1=online, 0=offline)',
['device_id', 'device_type']
)
ALERT_COUNT = Counter(
'alerts_generated_total',
'Total alerts generated',
['severity', 'alert_type']
)# Health check implementation
@app.get("/health")
async def health_check():
checks = {
"database": await check_database_connection(),
"redis": await check_redis_connection(),
"message_queue": await check_rabbitmq_connection(),
"external_apis": await check_external_dependencies()
}
all_healthy = all(checks.values())
status_code = 200 if all_healthy else 503
return JSONResponse(
status_code=status_code,
content={
"status": "healthy" if all_healthy else "unhealthy",
"timestamp": datetime.utcnow().isoformat(),
"checks": checks,
"version": os.getenv("APP_VERSION", "unknown")
}
)
# Readiness probe
@app.get("/ready")
async def readiness_check():
"""Check if service is ready to handle requests"""
return {"status": "ready", "timestamp": datetime.utcnow().isoformat()}
# Liveness probe
@app.get("/alive")
async def liveness_check():
"""Check if service is alive"""
return {"status": "alive", "timestamp": datetime.utcnow().isoformat()}| Resource | HTTP Method | Endpoint | Description |
|---|---|---|---|
| Devices | GET | /api/v1/devices |
List all devices |
| Devices | POST | /api/v1/devices |
Create new device |
| Devices | GET | /api/v1/devices/{id} |
Get device by ID |
| Devices | PUT | /api/v1/devices/{id} |
Update device |
| Devices | DELETE | /api/v1/devices/{id} |
Delete device |
| Alerts | GET | /api/v1/alerts |
List alerts |
| Alerts | POST | /api/v1/alerts/{id}/acknowledge |
Acknowledge alert |
| Metrics | GET | /api/v1/devices/{id}/metrics |
Get device metrics |
| Reports | GET | /api/v1/reports |
List reports |
| Reports | POST | /api/v1/reports/generate |
Generate report |
// Success Response
{
"success": true,
"data": {
"id": "123e4567-e89b-12d3-a456-426614174000",
"name": "Server-01",
"status": "online"
},
"meta": {
"timestamp": "2024-01-15T10:30:00Z",
"version": "v1",
"request_id": "req_123456789"
}
}
// Error Response
{
"success": false,
"error": {
"code": "DEVICE_NOT_FOUND",
"message": "Device with ID 123 not found",
"details": {
"device_id": "123",
"organization_id": "org_456"
}
},
"meta": {
"timestamp": "2024-01-15T10:30:00Z",
"version": "v1",
"request_id": "req_123456789"
}
}
// Paginated Response
{
"success": true,
"data": [...],
"pagination": {
"page": 1,
"per_page": 50,
"total": 150,
"total_pages": 3,
"has_next": true,
"has_prev": false
},
"meta": {
"timestamp": "2024-01-15T10:30:00Z",
"version": "v1",
"request_id": "req_123456789"
}
}# Standard Error Codes
class ErrorCodes:
# Authentication & Authorization
INVALID_CREDENTIALS = "INVALID_CREDENTIALS"
TOKEN_EXPIRED = "TOKEN_EXPIRED"
INSUFFICIENT_PERMISSIONS = "INSUFFICIENT_PERMISSIONS"
# Resource Errors
DEVICE_NOT_FOUND = "DEVICE_NOT_FOUND"
DEVICE_ALREADY_EXISTS = "DEVICE_ALREADY_EXISTS"
INVALID_DEVICE_TYPE = "INVALID_DEVICE_TYPE"
# Validation Errors
INVALID_INPUT = "INVALID_INPUT"
MISSING_REQUIRED_FIELD = "MISSING_REQUIRED_FIELD"
INVALID_IP_ADDRESS = "INVALID_IP_ADDRESS"
# System Errors
DATABASE_ERROR = "DATABASE_ERROR"
EXTERNAL_SERVICE_ERROR = "EXTERNAL_SERVICE_ERROR"
RATE_LIMIT_EXCEEDED = "RATE_LIMIT_EXCEEDED"
# Exception Handler
from fastapi import HTTPException
from fastapi.responses import JSONResponse
class NetPulseException(Exception):
def __init__(self, code: str, message: str, details: dict = None, status_code: int = 400):
self.code = code
self.message = message
self.details = details or {}
self.status_code = status_code
@app.exception_handler(NetPulseException)
async def netpulse_exception_handler(request: Request, exc: NetPulseException):
return JSONResponse(
status_code=exc.status_code,
content={
"success": False,
"error": {
"code": exc.code,
"message": exc.message,
"details": exc.details
},
"meta": {
"timestamp": datetime.utcnow().isoformat(),
"version": "v1",
"request_id": request.headers.get("x-request-id")
}
}
)# Middleware for standardized responses
@app.middleware("http")
async def standardize_response_middleware(request: Request, call_next):
# Add request ID
request_id = str(uuid.uuid4())
request.state.request_id = request_id
# Add correlation ID header
response = await call_next(request)
response.headers["X-Request-ID"] = request_id
response.headers["X-API-Version"] = "v1"
return response
# Request validation middleware
@app.middleware("http")
async def request_validation_middleware(request: Request, call_next):
# Validate content type for POST/PUT requests
if request.method in ["POST", "PUT", "PATCH"]:
content_type = request.headers.get("content-type", "")
if not content_type.startswith("application/json"):
return JSONResponse(
status_code=415,
content={
"success": False,
"error": {
"code": "UNSUPPORTED_MEDIA_TYPE",
"message": "Content-Type must be application/json"
}
}
)
return await call_next(request)# Service Structure Template
from fastapi import FastAPI, Depends, HTTPException
from sqlalchemy.orm import Session
from typing import List, Optional
import uuid
# Service Layer Pattern
class DeviceService:
def __init__(self, db: Session):
self.db = db
async def create_device(self, device_data: DeviceCreate) -> Device:
"""Create a new device with validation"""
# Validate IP address uniqueness
existing = self.db.query(Device).filter(
Device.ip_address == device_data.ip_address
).first()
if existing:
raise NetPulseException(
code="DEVICE_ALREADY_EXISTS",
message=f"Device with IP {device_data.ip_address} already exists",
status_code=409
)
# Create device
device = Device(
id=uuid.uuid4(),
**device_data.dict(),
created_at=datetime.utcnow()
)
self.db.add(device)
self.db.commit()
self.db.refresh(device)
return device
# Repository Pattern for Database Access
class DeviceRepository:
def __init__(self, db: Session):
self.db = db
def get_by_id(self, device_id: str) -> Optional[Device]:
return self.db.query(Device).filter(Device.id == device_id).first()
def get_by_organization(self, org_id: str, skip: int = 0, limit: int = 100) -> List[Device]:
return (
self.db.query(Device)
.filter(Device.organization_id == org_id)
.offset(skip)
.limit(limit)
.all()
)# Unit Testing Template
import pytest
from fastapi.testclient import TestClient
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
# Test Configuration
SQLALCHEMY_DATABASE_URL = "sqlite:///./test.db"
engine = create_engine(SQLALCHEMY_DATABASE_URL, connect_args={"check_same_thread": False})
TestingSessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
@pytest.fixture
def test_db():
"""Create test database session"""
db = TestingSessionLocal()
try:
yield db
finally:
db.close()
@pytest.fixture
def test_client():
"""Create test client"""
return TestClient(app)
# Test Examples
class TestDeviceService:
def test_create_device_success(self, test_db):
service = DeviceService(test_db)
device_data = DeviceCreate(
name="Test Device",
ip_address="192.168.1.100",
device_type="server"
)
device = await service.create_device(device_data)
assert device.name == "Test Device"
assert device.ip_address == "192.168.1.100"
assert device.id is not None
def test_create_device_duplicate_ip_fails(self, test_db):
service = DeviceService(test_db)
device_data = DeviceCreate(
name="Test Device",
ip_address="192.168.1.100",
device_type="server"
)
# Create first device
await service.create_device(device_data)
# Attempt to create duplicate should fail
with pytest.raises(NetPulseException) as exc_info:
await service.create_device(device_data)
assert exc_info.value.code == "DEVICE_ALREADY_EXISTS"
# Integration Testing
class TestDeviceAPI:
def test_create_device_endpoint(self, test_client):
response = test_client.post(
"/api/v1/devices",
json={
"name": "Test Device",
"ip_address": "192.168.1.100",
"device_type": "server"
},
headers={"Authorization": "Bearer test-token"}
)
assert response.status_code == 201
data = response.json()
assert data["success"] is True
assert data["data"]["name"] == "Test Device"-
Code Quality
- All tests passing (unit, integration, e2e)
- Code coverage > 80%
- Linting checks passed (flake8, black)
- Type checking passed (mypy)
- Security scan completed (bandit)
-
Configuration
- Environment variables configured
- Database migrations applied
- Redis cache configured
- RabbitMQ queues created
- SSL certificates installed
-
Monitoring
- Health checks implemented
- Prometheus metrics exposed
- Grafana dashboards configured
- Alert rules configured
- Log aggregation setup
-
Security
- JWT secret keys rotated
- Database passwords updated
- API rate limiting configured
- CORS policies configured
- Security headers implemented
-
Performance
- Database indexes optimized
- Cache warming scripts run
- Load testing completed
- Resource limits configured
- Auto-scaling policies set
-
Enhanced Monitoring
- Real-time alerting improvements
- Custom dashboard builder
- Mobile push notifications
- Alert correlation and noise reduction
-
Performance Optimizations
- Database query optimization
- Caching layer improvements
- Background job optimization
- API response time improvements
-
User Experience
- Single Sign-On (SSO) integration
- Multi-tenant improvements
- Advanced filtering and search
- Bulk operations UI
-
Advanced Analytics
- Machine learning-based anomaly detection
- Predictive maintenance alerts
- Trend analysis and forecasting
- Automated root cause analysis
-
Integration Capabilities
- Third-party monitoring tool integrations
- Webhook customization
- API gateway for external integrations
- Plugin architecture
-
Scalability Improvements
- Kubernetes deployment
- Auto-scaling implementation
- Multi-region deployment
- Event-driven architecture
-
AI-Powered Features
- Intelligent alerting with ML
- Automated remediation
- Natural language query interface
- Predictive capacity planning
-
Enterprise Features
- Advanced RBAC with custom roles
- Audit logging and compliance
- Multi-cloud monitoring
- White-label solution
-
Platform Evolution
- Microservices mesh implementation
- Event sourcing and CQRS
- Real-time collaboration features
- API marketplace
This architecture document serves as the technical blueprint for NetPulse's microservices-based network monitoring platform. For implementation details, refer to the service-specific documentation and code comments.
graph TB
subgraph "Client Authentication"
LOGIN[Login Request]
JWT[JWT Token]
REFRESH[Refresh Token]
end
subgraph "API Gateway Security"
RATE_LIMIT[Rate Limiting]
CORS[CORS Policy]
CSRF[CSRF Protection]
end
subgraph "Service Security"
RBAC[Role-Based Access]
PERMISSIONS[Permission System]
AUDIT[Audit Logging]
end
subgraph "Data Security"
ENCRYPTION[Data Encryption]
HASHING[Password Hashing]
SANITIZATION[Input Sanitization]
end
LOGIN --> JWT
JWT --> REFRESH
JWT --> RATE_LIMIT
RATE_LIMIT --> CORS
CORS --> CSRF
CSRF --> RBAC
RBAC --> PERMISSIONS
PERMISSIONS --> AUDIT
AUDIT --> ENCRYPTION
ENCRYPTION --> HASHING
HASHING --> SANITIZATION
Security Measures:
- JWT Authentication: Stateless authentication with refresh tokens
- RBAC: Role-based access control with granular permissions
- Rate Limiting: API rate limiting to prevent abuse
- Input Validation: Comprehensive input sanitization and validation
- HTTPS Only: All communications encrypted in transit
- Database Encryption: Sensitive data encrypted at rest
- Audit Logging: Complete audit trail of all user actions
graph TB
subgraph "Production Environment"
subgraph "Load Balancer"
LB[nginx/CloudFlare]
end
subgraph "Application Tier"
API1[FastAPI Instance 1]
API2[FastAPI Instance 2]
API3[FastAPI Instance 3]
end
subgraph "Worker Tier"
WORKER1[Celery Worker 1]
WORKER2[Celery Worker 2]
WORKER3[Celery Worker 3]
end
subgraph "Data Tier"
POSTGRES_PRIMARY[(PostgreSQL Primary)]
POSTGRES_REPLICA[(PostgreSQL Replica)]
REDIS_CLUSTER[(Redis Cluster)]
end
subgraph "Monitoring"
PROMETHEUS[Prometheus]
GRAFANA[Grafana]
ALERTS[AlertManager]
end
end
LB --> API1
LB --> API2
LB --> API3
API1 --> POSTGRES_PRIMARY
API2 --> POSTGRES_PRIMARY
API3 --> POSTGRES_PRIMARY
API1 --> REDIS_CLUSTER
API2 --> REDIS_CLUSTER
API3 --> REDIS_CLUSTER
WORKER1 --> POSTGRES_PRIMARY
WORKER2 --> POSTGRES_PRIMARY
WORKER3 --> POSTGRES_PRIMARY
POSTGRES_PRIMARY --> POSTGRES_REPLICA
PROMETHEUS --> API1
PROMETHEUS --> API2
PROMETHEUS --> API3
GRAFANA --> PROMETHEUS
ALERTS --> PROMETHEUS
# docker-compose.prod.yml structure
version: '3.8'
services:
# Frontend
frontend:
image: netpulse/frontend:latest
ports:
- "3000:80"
environment:
- REACT_APP_API_URL=https://api.netpulse.com
# Backend API
backend:
image: netpulse/backend:latest
ports:
- "8000:8000"
environment:
- DATABASE_URL=postgresql://user:pass@postgres:5432/netpulse
- REDIS_URL=redis://redis:6379/0
depends_on:
- postgres
- redis
# Celery Workers
worker:
image: netpulse/backend:latest
command: celery -A app.worker worker --loglevel=info
environment:
- DATABASE_URL=postgresql://user:pass@postgres:5432/netpulse
- REDIS_URL=redis://redis:6379/0
depends_on:
- postgres
- redis
- rabbitmq
# Message Queue
rabbitmq:
image: rabbitmq:3-management
ports:
- "5672:5672"
- "15672:15672"
# Databases
postgres:
image: timescale/timescaledb:latest-pg14
environment:
- POSTGRES_DB=netpulse
- POSTGRES_USER=user
- POSTGRES_PASSWORD=pass
volumes:
- postgres_data:/var/lib/postgresql/data
redis:
image: redis:7-alpine
ports:
- "6379:6379"
volumes:
- redis_data:/data
# Monitoring
prometheus:
image: prom/prometheus:latest
ports:
- "9090:9090"
volumes:
- ./monitoring/prometheus.yml:/etc/prometheus/prometheus.yml
grafana:
image: grafana/grafana:latest
ports:
- "3001:3000"
environment:
- GF_SECURITY_ADMIN_PASSWORD=admin
volumes:
- grafana_data:/var/lib/grafana
volumes:
postgres_data:
redis_data:
grafana_data:| Component | Scaling Method | Considerations |
|---|---|---|
| Frontend | CDN + Multiple Replicas | Stateless React app, easy to scale |
| API Gateway | Load Balancer + Auto-scaling | Stateless FastAPI instances |
| Celery Workers | Queue-based Scaling | Auto-scale based on queue length |
| PostgreSQL | Read Replicas + Sharding | Primary-replica setup with read scaling |
| Redis | Cluster Mode | Redis Cluster for high availability |
| Message Queue | RabbitMQ Cluster | Clustered message queue for reliability |
- Database Connection Pooling: Efficient database connections
- Query Optimization: Indexed queries and query optimization
- Caching Strategy: Multi-level caching (Redis, Application, CDN)
- Async Processing: Non-blocking operations using asyncio
- Background Tasks: CPU-intensive tasks offloaded to workers
- WebSocket Optimization: Efficient real-time data streaming
# Prometheus metrics configuration
- Application Metrics:
- Request rate and latency
- Error rates and status codes
- Database query performance
- Cache hit/miss ratios
- Business Metrics:
- Active devices count
- Alert generation rate
- User activity metrics
- SLA compliance metrics
- Infrastructure Metrics:
- CPU and memory usage
- Disk I/O and network traffic
- Container resource usage
- Database performance# Structured logging configuration
- Log Levels:
- ERROR: Application errors and exceptions
- WARN: Performance degradation warnings
- INFO: Business logic events
- DEBUG: Detailed debugging information
- Log Aggregation:
- Centralized logging with ELK stack
- Log correlation with trace IDs
- Real-time log analysis
- Log retention policies# Alert manager configuration
- Critical Alerts:
- Service down (>5 minutes)
- High error rate (>5% for 2 minutes)
- Database connection failure
- Memory usage >90%
- Warning Alerts:
- High response time (>2s for 5 minutes)
- Disk usage >80%
- Queue backlog >1000 items
- Cache miss rate >50%PostgreSQL Backups:
- Full backup: Daily at 2 AM UTC
- Incremental backup: Every 6 hours
- Point-in-time recovery: 30-day retention
- Cross-region replication: Primary + DR site
Redis Backups:
- RDB snapshots: Every hour
- AOF replication: Real-time
- Cluster backup: Coordinated snapshots
Application Backups:
- Configuration backup: Version controlled
- Static assets: CDN with backup regions
- Container images: Multi-registry storageRecovery Time Objectives (RTO):
- Critical services: 15 minutes
- Full system recovery: 2 hours
- Data recovery: 4 hours
Recovery Point Objectives (RPO):
- Real-time data: 5 minutes
- Historical data: 1 hour
- Configuration data: 24 hours
DR Procedures:
- Automated failover: Database and cache
- Manual failover: Application services
- Data validation: Post-recovery checks
- Rollback procedures: Emergency rollbackgraph LR
subgraph "NetPulse Core"
CORE[NetPulse API]
end
subgraph "Notification Services"
SENDGRID[SendGrid Email]
AFRICAS[Africa's Talking SMS]
SLACK[Slack Webhooks]
TEAMS[Microsoft Teams]
end
subgraph "Monitoring Integrations"
PAGER[PagerDuty]
OPSGENIE[OpsGenie]
DATADOG[DataDog]
end
subgraph "Network Equipment"
SNMP[SNMP Devices]
SSH[SSH Monitoring]
API_DEVICES[API-based Devices]
end
CORE --> SENDGRID
CORE --> AFRICAS
CORE --> SLACK
CORE --> TEAMS
CORE --> PAGER
CORE --> OPSGENIE
CORE --> DATADOG
SNMP --> CORE
SSH --> CORE
API_DEVICES --> CORE
- Microservices Migration: Break monolith into smaller services
- Event Sourcing: Implement event sourcing for audit and replay
- CQRS Pattern: Separate read and write models for better performance
- API Gateway: Dedicated API gateway for better routing and security
- Service Mesh: Istio/Linkerd for advanced service communication
- Multi-tenant Architecture: Support for multiple organizations
- Machine Learning Pipeline: Predictive analytics and anomaly detection
- Mobile-first Design: Progressive Web App and native mobile apps
- Container Orchestration: Migration to Kubernetes
- Serverless Functions: AWS Lambda/Azure Functions for event processing
- Edge Computing: Edge nodes for distributed monitoring
- Blockchain Integration: Immutable audit logs using blockchain
- AI/ML Integration: Advanced analytics and predictive monitoring
NetPulse Architecture v1.0
Engineered for Scale, Security, and Performance