High Availability API with Celery & Microservices Architecture

A enterprise-grade high-availability RESTful API service built with Flask microservices architecture, featuring advanced load balancing, JWT authentication with NGINX auth_request module, asynchronous task processing with Celery, PostgreSQL with connection pooling, Redis clustering, and comprehensive real-time monitoring dashboard.

🏗️ Advanced Architecture Overview

This system implements a production-ready microservices architecture with horizontal scalability, fault tolerance, and zero-downtime deployment capabilities.

🎯 Architecture Components & Service Mesh

┌─────────────────────────────────────────────────────────────────┐
│                        External Traffic                         │
└─────────────────────┬───────────────────────────────────────────┘
                      │
              ┌───────▼────────┐
              │ NGINX (Port 80) │ ◄─── Load Balancer + Auth Gateway
              │ - Load Balancing │
              │ - JWT Auth Proxy │
              │ - Rate Limiting  │
              │ - SSL Termination│
              └───────┬────────┘
                      │
          ┌───────────┼───────────┐
          │           │           │
    ┌─────▼──┐  ┌─────▼──┐  ┌─────▼──┐
    │ API-1  │  │ API-2  │  │ API-3  │ ◄─── Microservice Instances
    │ :8000  │  │ :8001  │  │ :8002  │
    └────────┘  └────────┘  └────────┘
          │           │           │
          └───────────┼───────────┘
                      │
    ┌─────────────────┼─────────────────┐
    │                 │                 │
┌───▼────┐    ┌───────▼────────┐    ┌───▼────────┐
│ Redis  │    │ PostgreSQL     │    │ Celery     │
│ Cache/ │    │ Primary DB     │    │ Workers    │
│ Broker │    │ Connection     │    │ + Beat     │
│ :6379  │    │ Pool :5432     │    │ Scheduler  │
└────────┘    └────────────────┘    └────────────┘
                      │
              ┌───────▼────────┐
              │ Streamlit      │ ◄─── Monitoring Dashboard
              │ Dashboard      │
              │ :8501          │
              └────────────────┘

🏛️ Core Infrastructure Components

1. Load Balancer Layer (NGINX)

Smart Load Balancing: Least-connection algorithm with health checking
JWT Authentication Proxy: Validates tokens before forwarding requests
Rate Limiting: Protects against DDoS and abuse
SSL/TLS Termination: Handles encryption/decryption
Request Routing: Path-based routing with pattern matching
Health Monitoring: Automatic upstream health detection

2. Application Layer (Flask Microservices)

3 Identical API Instances: Horizontal scaling with state isolation
Gunicorn WSGI Server: Production-grade Python web server
Flask-JWT-Extended: Advanced JWT with refresh tokens and blacklisting
Request Middleware: Comprehensive logging and monitoring
Database ORM: SQLAlchemy with connection pooling
Error Handling: Graceful degradation and circuit breaker patterns

3. Data Layer

PostgreSQL: ACID-compliant primary database with optimized queries
Redis: Multi-purpose (caching, session storage, message broker)
Connection Pooling: Efficient database connection management
Data Persistence: Automated backups and recovery mechanisms

4. Background Processing

Celery Workers: Distributed task execution with worker pools
Celery Beat: Cron-like periodic task scheduling
Task Queue Management: Priority-based task distribution
Result Backend: Redis-based result storage and retrieval

5. Monitoring & Observability

Streamlit Dashboard: Real-time system monitoring
Health Check System: Multi-layer health verification
Node Heartbeat: Service discovery and health tracking
Comprehensive Logging: Structured logging with correlation IDs

🔄 Advanced Service Interactions

Request Flow Architecture

1. Client Request → NGINX (Port 80)
2. NGINX → Rate Limiting Check
3. NGINX → JWT Token Validation (auth_request)
4. NGINX → Load Balancing (least_conn)
5. NGINX → Forward to API Instance
6. API → Database/Redis Operations
7. API → Response Generation
8. NGINX → Response to Client

Authentication Flow

1. Login Request → NGINX → API Instance
2. API → Validate Credentials (PostgreSQL)
3. API → Generate JWT Tokens (Access + Refresh)
4. API → Store Session (Redis)
5. Subsequent Requests → NGINX auth_request validation
6. NGINX → Internal /auth/validate endpoint
7. API → JWT Verification & User Validation
8. Success → Forward to protected endpoint

🚀 Production Deployment Guide

📋 System Requirements

Minimum Hardware Requirements

CPU: 4 cores (8 recommended for production)
RAM: 8GB (16GB recommended)
Storage: 50GB SSD (with automatic scaling)
Network: 1Gbps connection

Software Prerequisites

Docker: v20.10+ with Docker Compose v2.0+
Git: Latest version
Operating System: Linux (Ubuntu 20.04+ recommended), macOS, Windows with WSL2

🔧 Advanced Installation & Configuration

Step 1: Repository Setup & Environment Configuration

# Clone with full git history for versioning
git clone --depth=1 https://github.com/your-repo/ha-api-celery.git
cd ha-api-celery

# Create production environment file
cp .env.example .env.production

# Configure production secrets (CRITICAL FOR SECURITY)
nano .env.production

Production Environment Configuration:

# === SECURITY CONFIGURATION ===
SECRET_KEY=your-super-secure-secret-key-minimum-32-chars
JWT_SECRET_KEY=your-jwt-secret-key-different-from-secret-key
JWT_ACCESS_TOKEN_EXPIRES=3600      # 1 hour
JWT_REFRESH_TOKEN_EXPIRES=2592000  # 30 days

# === DATABASE CONFIGURATION ===
DATABASE_URL=postgresql://prod_user:secure_password@postgres:5432/production_db
POSTGRES_DB=production_db
POSTGRES_USER=prod_user
POSTGRES_PASSWORD=your-secure-db-password

# === REDIS CONFIGURATION ===
REDIS_URL=redis://redis:6379/0
REDIS_PASSWORD=your-redis-password

# === API CONFIGURATION ===
FLASK_ENV=production
API_HOST=0.0.0.0
API_WORKERS=4
API_WORKER_CONNECTIONS=1000

# === NGINX CONFIGURATION ===
NGINX_WORKER_PROCESSES=auto
NGINX_WORKER_CONNECTIONS=1024

# === MONITORING ===
LOG_LEVEL=INFO
SENTRY_DSN=your-sentry-dsn-for-error-tracking

Step 2: SSL/TLS Certificate Setup (Production Only)

# Generate SSL certificates (Let's Encrypt recommended)
sudo apt install certbot
sudo certbot certonly --standalone -d yourdomain.com

# Copy certificates to nginx directory
mkdir -p nginx/ssl
sudo cp /etc/letsencrypt/live/yourdomain.com/fullchain.pem nginx/ssl/
sudo cp /etc/letsencrypt/live/yourdomain.com/privkey.pem nginx/ssl/

Step 3: Docker Network & Volume Preparation

# Create external Docker network for advanced networking
docker network create ha_api_production_network

# Create named volumes for persistent data
docker volume create postgres_production_data
docker volume create redis_production_data
docker volume create nginx_production_logs

Step 4: Database Security Hardening

# Generate secure database initialization script
cat > init-scripts/01-create-production-user.sql << EOF
-- Create production database and user
CREATE DATABASE production_db;
CREATE USER prod_user WITH ENCRYPTED PASSWORD 'your-secure-db-password';
GRANT ALL PRIVILEGES ON DATABASE production_db TO prod_user;

-- Security hardening
ALTER USER prod_user SET default_transaction_isolation TO 'read committed';
ALTER USER prod_user SET timezone TO 'UTC';
EOF

Step 5: Production Deployment Execution

# Make deployment script executable
chmod +x deploy.sh docker-deploy.sh

# Run production deployment with health checks
./docker-deploy.sh production

# Alternative: Manual deployment with monitoring
docker-compose -f docker-compose.yml --env-file .env.production up -d

# Verify all services are healthy (wait for initialization)
./deploy.sh health-check

# Run database migrations and create initial data
docker-compose exec api1 python init_db.py --environment=production

🔍 Advanced Service Verification

Comprehensive Health Check Protocol

#!/bin/bash
# health-check.sh - Comprehensive service verification

echo "🔍 Running comprehensive health checks..."

# 1. Container health verification
echo "Checking container health..."
docker-compose ps

# 2. Database connectivity test
echo "Testing database connectivity..."
docker-compose exec postgres pg_isready -U prod_user -d production_db

# 3. Redis connectivity test
echo "Testing Redis connectivity..."
docker-compose exec redis redis-cli ping

# 4. API endpoint verification
echo "Testing API endpoints..."
curl -f http://localhost/health/ping || echo "❌ Health ping failed"
curl -f http://localhost/api/status || echo "❌ API status failed"

# 5. Load balancer verification
echo "Testing load balancer distribution..."
for i in {1..10}; do
  curl -s http://localhost/health/ping | grep -o '"node_id":"[^"]*"'
done

# 6. Authentication system verification
echo "Testing authentication system..."
TOKEN=$(curl -s -X POST http://localhost/auth/login \
  -H "Content-Type: application/json" \
  -d '{"username":"admin","password":"admin123"}' | \
  jq -r '.access_token')

if [[ "$TOKEN" != "null" && "$TOKEN" != "" ]]; then
  echo "✅ Authentication system operational"
  # Test protected endpoint
  curl -f -H "Authorization: Bearer $TOKEN" http://localhost/api/protected || echo "❌ Protected endpoint failed"
else
  echo "❌ Authentication system failed"
fi

# 7. Task processing verification
echo "Testing task processing system..."
TASK_RESPONSE=$(curl -s -X POST http://localhost/tasks/submit \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"task_type":"sample","data":{"test":"health_check"}}')

TASK_ID=$(echo $TASK_RESPONSE | jq -r '.task_id')
if [[ "$TASK_ID" != "null" && "$TASK_ID" != "" ]]; then
  echo "✅ Task processing system operational"
else
  echo "❌ Task processing system failed"
fi

# 8. Monitoring dashboard verification
echo "Testing monitoring dashboard..."
curl -f http://localhost:8501 || echo "❌ Dashboard not accessible"

echo "🏁 Health check completed"

📊 Access Points & Service Discovery

After successful deployment, the system provides multiple access points:

Service	URL	Purpose	Authentication
Load Balanced API	`http://localhost`	Main API gateway	JWT Required for protected endpoints
Monitoring Dashboard	`http://localhost:8501`	Real-time system monitoring	Admin login required
Database Direct	`localhost:5678`	Direct PostgreSQL access	Database credentials
API Instance 1	`Internal only`	Direct API access (debugging)	Not exposed externally
API Instance 2	`Internal only`	Direct API access (debugging)	Not exposed externally
API Instance 3	`Internal only`	Direct API access (debugging)	Not exposed externally
Redis Cache	`Internal only`	Cache and message broker	Internal network only

🗄️ Advanced Database Architecture & Management

The system implements a enterprise-grade PostgreSQL setup with advanced features for reliability, performance, and scalability.

🏗️ Database Architecture

Multi-Layer Database Design

-- Database: production_db
-- Connection Pool: 20 connections per API instance
-- Total Pool Size: 60 connections (3 instances × 20)
-- Backup Strategy: Automated daily backups with point-in-time recovery

┌─────────────────────────────────────────────────────────┐
│                    Application Layer                    │
├─────────────────────────────────────────────────────────┤
│  API Instance 1  │  API Instance 2  │  API Instance 3  │
│   (20 conns)     │   (20 conns)     │   (20 conns)     │
├─────────────────────────────────────────────────────────┤
│                Connection Pool Manager                  │
├─────────────────────────────────────────────────────────┤
│                PostgreSQL Primary                      │
│              (Max 100 connections)                     │
└─────────────────────────────────────────────────────────┘

Database Services Configuration

Primary Database Container:

postgres:
  image: postgres:15-alpine
  environment:
    POSTGRES_DB: production_db
    POSTGRES_USER: prod_user
    POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}
    POSTGRES_INITDB_ARGS: --encoding=UTF-8 --locale=en_US.UTF-8
  volumes:
    - postgres_data:/var/lib/postgresql/data
    - ./init-scripts:/docker-entrypoint-initdb.d
  command: |
    postgres 
    -c max_connections=100
    -c shared_buffers=256MB
    -c effective_cache_size=1GB
    -c work_mem=4MB
    -c maintenance_work_mem=64MB
    -c checkpoint_completion_target=0.9
    -c wal_buffers=16MB
    -c default_statistics_target=100

🔧 Database Configuration & Optimization

Connection Pool Settings

# config.py - SQLAlchemy Configuration
SQLALCHEMY_ENGINE_OPTIONS = {
    'pool_size': 20,
    'pool_timeout': 30,
    'pool_recycle': 3600,
    'pool_pre_ping': True,
    'max_overflow': 10,
    'echo': False  # Set to True for query debugging
}

Database Initialization & Migration System

Automated Migration Process:

# init_db.py - Advanced database initialization
#!/usr/bin/env python3

import sys
import logging
from flask_migrate import init, migrate, upgrade
from app import create_app, db
from app.models import User, NodeHeartbeat, TaskHistory

def initialize_database():
    """
    Comprehensive database initialization with error handling
    and rollback mechanisms.
    """
    app = create_app('production')

    with app.app_context():
        try:
            # Create all tables
            db.create_all()

            # Run Alembic migrations
            upgrade()

            # Create initial data
            create_admin_user()
            create_system_configurations()

            print("✅ Database initialization completed successfully")

        except Exception as e:
            db.session.rollback()
            print(f"❌ Database initialization failed: {e}")
            sys.exit(1)

def create_admin_user():
    """Create default admin user if not exists."""
    admin = User.query.filter_by(username='admin').first()
    if not admin:
        admin = User(
            username='admin',
            email='admin@company.com',
            is_active=True
        )
        admin.set_password('admin123')  # Change in production
        db.session.add(admin)
        db.session.commit()

Manual Database Operations

# Connect to database container for maintenance
docker-compose exec postgres psql -U prod_user -d production_db

# Database backup (production)
docker-compose exec postgres pg_dump -U prod_user production_db > backup_$(date +%Y%m%d).sql

# Database restore (production)
docker-compose exec -T postgres psql -U prod_user production_db < backup_file.sql

# Check database size and statistics
docker-compose exec postgres psql -U prod_user -d production_db -c "
  SELECT
    schemaname,
    tablename,
    attname,
    n_distinct,
    correlation
  FROM pg_stats
  WHERE schemaname = 'public';"

# Monitor active connections
docker-compose exec postgres psql -U prod_user -d production_db -c "
  SELECT
    pid,
    usename,
    application_name,
    client_addr,
    state,
    query_start,
    query
  FROM pg_stat_activity
  WHERE state = 'active';"

📋 Database Schema & Models

Core Database Models

User Management Model:

class User(db.Model):
    __tablename__ = 'users'

    id = db.Column(db.Integer, primary_key=True)
    username = db.Column(db.String(80), unique=True, nullable=False, index=True)
    email = db.Column(db.String(120), unique=True, nullable=False, index=True)
    password_hash = db.Column(db.String(128), nullable=False)
    is_active = db.Column(db.Boolean, default=True, nullable=False)

    # Audit fields
    created_at = db.Column(db.DateTime, default=datetime.utcnow, nullable=False)
    updated_at = db.Column(db.DateTime, default=datetime.utcnow,
                          onupdate=datetime.utcnow, nullable=False)
    last_login = db.Column(db.DateTime, nullable=True)
    login_count = db.Column(db.Integer, default=0)

    # Relationships
    task_submissions = db.relationship('TaskHistory', backref='submitted_by', lazy=True)

Node Monitoring Model:

class NodeHeartbeat(db.Model):
    __tablename__ = 'node_heartbeats'

    id = db.Column(db.Integer, primary_key=True)
    node_id = db.Column(db.String(100), nullable=False, index=True)
    hostname = db.Column(db.String(255), nullable=False)
    ip_address = db.Column(db.String(45), nullable=True)
    port = db.Column(db.Integer, nullable=True)

    # Health metrics
    status = db.Column(db.String(20), default='active', nullable=False)
    cpu_usage = db.Column(db.Float, nullable=True)
    memory_usage = db.Column(db.Float, nullable=True)
    disk_usage = db.Column(db.Float, nullable=True)
    load_average = db.Column(db.Float, nullable=True)

    # Timing
    last_heartbeat = db.Column(db.DateTime, default=datetime.utcnow, nullable=False)
    response_time = db.Column(db.Float, nullable=True)  # in milliseconds

Task Management Model:

class TaskHistory(db.Model):
    __tablename__ = 'task_history'

    id = db.Column(db.Integer, primary_key=True)
    task_id = db.Column(db.String(255), nullable=False, index=True, unique=True)
    task_name = db.Column(db.String(255), nullable=False)
    task_type = db.Column(db.String(100), nullable=False, index=True)

    # Execution details
    status = db.Column(db.String(20), nullable=False, index=True)
    priority = db.Column(db.Integer, default=5, nullable=False)
    retry_count = db.Column(db.Integer, default=0, nullable=False)
    max_retries = db.Column(db.Integer, default=3, nullable=False)

    # Data and results
    input_data = db.Column(db.JSON, nullable=True)
    result = db.Column(db.JSON, nullable=True)
    error_message = db.Column(db.Text, nullable=True)

    # Timing
    queued_at = db.Column(db.DateTime, default=datetime.utcnow, nullable=False)
    started_at = db.Column(db.DateTime, nullable=True)
    completed_at = db.Column(db.DateTime, nullable=True)
    execution_time = db.Column(db.Float, nullable=True)  # in seconds

    # Worker information
    worker_id = db.Column(db.String(255), nullable=True)
    worker_hostname = db.Column(db.String(255), nullable=True)

🔐 Enterprise Authentication & Security Architecture

The system implements military-grade security with multi-layer authentication, authorization, and comprehensive security measures.

🛡️ Security Architecture Overview

┌─────────────────────────────────────────────────────────────────┐
│                        Security Layers                         │
├─────────────────────────────────────────────────────────────────┤
│ 1. NGINX Rate Limiting & DDoS Protection                       │
│ 2. SSL/TLS Encryption (Production)                             │
│ 3. JWT Token Validation with NGINX auth_request                │
│ 4. Flask-JWT-Extended with Advanced Features                   │
│ 5. Database-level User Validation                              │
│ 6. Session Management with Redis                               │
│ 7. CORS & Security Headers                                     │
└─────────────────────────────────────────────────────────────────┘

🔑 Advanced JWT Authentication System

JWT Configuration & Features

# Advanced JWT configuration in config.py
JWT_SECRET_KEY = os.environ.get('JWT_SECRET_KEY')
JWT_ACCESS_TOKEN_EXPIRES = timedelta(hours=1)
JWT_REFRESH_TOKEN_EXPIRES = timedelta(days=30)
JWT_BLACKLIST_ENABLED = True
JWT_BLACKLIST_TOKEN_CHECKS = ['access', 'refresh']
JWT_ALGORITHM = 'HS256'

# Additional security features
JWT_ERROR_MESSAGE_KEY = 'error'
JWT_JSON_KEY = 'access_token'
JWT_REFRESH_JSON_KEY = 'refresh_token'

Multi-Factor Authentication Flow

Authentication Endpoints:

Endpoint	Method	Purpose	Rate Limit	Authentication
`/auth/register`	POST	User registration	5 req/min	None
`/auth/login`	POST	User authentication	5 req/min	Credentials
`/auth/refresh`	POST	Token refresh	10 req/min	Refresh token
`/auth/validate`	GET	Token validation (NGINX)	100 req/min	Access token
`/auth/profile`	GET	User profile	50 req/min	Access token
`/auth/logout`	POST	User logout	20 req/min	Access token
`/api/users`	GET	List all users (admin)	`users:read`	50 req/min
`/api/users/<id>`	GET	Get specific user details	`users:read`	100 req/min

Advanced User Operations:

# Get current user profile with detailed information
curl -X GET http://localhost/auth/profile \
  -H "Authorization: Bearer $TOKEN" | jq

# Response with comprehensive user data
{
  "status": "success",
  "data": {
    "user": {
      "id": 1,
      "username": "admin",
      "email": "admin@company.com",
      "full_name": "System Administrator",
      "role": "admin",
      "department": "IT Operations",
      "permissions": ["*"],
      "account_status": "active",
      "email_verified": true,
      "two_factor_enabled": false,
      "created_at": "2024-01-01T00:00:00Z",
      "last_login": "2025-01-01T11:00:00Z",
      "login_count": 156,
      "password_changed_at": "2024-12-01T10:30:00Z"
    },
    "preferences": {
      "timezone": "UTC",
      "date_format": "YYYY-MM-DD",
      "language": "en",
      "notifications": {
        "email": true,
        "push": false,
        "task_completion": true
      }
    },
    "statistics": {
      "tasks_submitted": 89,
      "tasks_completed": 82,
      "tasks_failed": 7,
      "total_api_calls": 1247,
      "last_activity": "2025-01-01T11:55:00Z"
    }
  }
}

# Update user profile with validation
curl -X PUT http://localhost/auth/profile \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "full_name": "John Smith Administrator",
    "email": "john.admin@company.com",
    "department": "DevOps Engineering",
    "preferences": {
      "timezone": "America/New_York",
      "notifications": {
        "email": true,
        "task_completion": true
      }
    }
  }'

# Logout from all devices/sessions
curl -X POST http://localhost/auth/logout-all \
  -H "Authorization: Bearer $TOKEN"

Advanced Data & Analytics Endpoints

# Get comprehensive API metrics (admin only)
curl -X GET http://localhost/api/metrics \
  -H "Authorization: Bearer $ADMIN_TOKEN" | jq

# Response with detailed analytics
{
  "status": "success",
  "data": {
    "api_metrics": {
      "total_requests": 15478,
      "requests_per_minute": 125,
      "avg_response_time": 45.7,
      "error_rate": 0.03,
      "active_users": 8,
      "concurrent_connections": 23
    },
    "endpoint_statistics": [
      {
        "endpoint": "/api/protected",
        "total_calls": 3456,
        "avg_response_time": 23.4,
        "success_rate": 0.99
      },
      {
        "endpoint": "/tasks/submit",
        "total_calls": 892,
        "avg_response_time": 156.7,
        "success_rate": 0.95
      }
    ],
    "user_activity": {
      "active_sessions": 8,
      "unique_users_today": 12,
      "peak_concurrent_users": 15,
      "avg_session_duration": 1847
    },
    "system_health": {
      "cpu_usage": 15.2,
      "memory_usage": 45.7,
      "disk_usage": 23.1,
      "database_connections": 15,
      "redis_memory": "45.2MB"
    }
  },
  "metadata": {
    "collection_time": "2025-01-01T12:00:00Z",
    "next_update": "2025-01-01T12:05:00Z"
  }
}

# Data processing endpoint with file upload support
curl -X POST http://localhost/api/data-processing \
  -H "Authorization: Bearer $TOKEN" \
  -F "file=@large_dataset.csv" \
  -F "processing_type=machine_learning" \
  -F "parameters={\"algorithm\":\"random_forest\",\"max_depth\":10}"

# Bulk data import with validation
curl -X POST http://localhost/api/data/bulk-import \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "source": "external_api",
    "data_type": "user_events",
    "batch_size": 1000,
    "validation_rules": ["email_format", "required_fields"],
    "error_handling": "skip_invalid",
    "callback_url": "https://myapp.com/webhook/import_complete"
  }'

⚙️ Advanced Background Task Processing

The system implements a sophisticated task processing architecture with Celery, supporting complex workflows, task chains, and real-time monitoring.

🔄 Task Architecture & Processing Pipeline

┌─────────────────────────────────────────────────────────────────┐
│                    Task Processing Flow                         │
├─────────────────────────────────────────────────────────────────┤
│ 1. Client → API Endpoint (Task Submission)                     │
│ 2. API → Input Validation & Authorization                      │
│ 3. API → Task Creation & Queue Assignment                      │
│ 4. Redis → Task Broker & Message Queue                        │
│ 5. Celery Worker → Task Execution & Processing                │
│ 6. PostgreSQL → Result Storage & History                      │
│ 7. Redis → Real-time Status Updates                           │
│ 8. Webhook/Callback → Completion Notification                 │
└─────────────────────────────────────────────────────────────────┘

📋 Comprehensive Task Management API

Task Submission Endpoints

Endpoint	Method	Description	Priority Support	Async Processing
`/tasks/submit`	POST	Submit single task	Yes (1-10)	Yes
`/tasks/batch`	POST	Submit multiple tasks	Yes	Yes
`/tasks/chain`	POST	Submit task chain/workflow	Yes	Yes
`/tasks/schedule`	POST	Schedule future task	Yes	Yes

Advanced Task Submission Examples:

# Submit high-priority machine learning task
curl -X POST http://localhost/tasks/submit \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "task_type": "machine_learning_training",
    "priority": 1,
    "data": {
      "model_type": "neural_network",
      "dataset": "/data/training_set_v2.csv",
      "hyperparameters": {
        "learning_rate": 0.001,
        "batch_size": 64,
        "epochs": 100,
        "dropout_rate": 0.2
      },
      "validation_split": 0.2,
      "early_stopping": true
    },
    "resources": {
      "cpu_cores": 4,
      "memory_gb": 8,
      "gpu_required": true
    },
    "callbacks": {
      "progress_url": "https://myapp.com/webhook/training_progress",
      "completion_url": "https://myapp.com/webhook/training_complete",
      "error_url": "https://myapp.com/webhook/training_error"
    },
    "retry_config": {
      "max_retries": 3,
      "retry_delay": 300,
      "exponential_backoff": true
    }
  }'

# Response with comprehensive task information
{
  "status": "success",
  "data": {
    "task_id": "ml_train_abc123def456",
    "task_type": "machine_learning_training",
    "status": "queued",
    "priority": 1,
    "estimated_duration": 3600,
    "queue_position": 2,
    "worker_assignment": "pending",
    "created_at": "2025-01-01T12:00:00Z",
    "scheduled_for": "2025-01-01T12:00:30Z"
  },
  "links": {
    "status": "/tasks/status/ml_train_abc123def456",
    "cancel": "/tasks/cancel/ml_train_abc123def456",
    "logs": "/tasks/logs/ml_train_abc123def456"
  }
}

# Submit batch processing tasks
curl -X POST http://localhost/tasks/batch \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "batch_name": "daily_report_generation",
    "tasks": [
      {
        "task_type": "data_extraction",
        "data": {"source": "database", "query": "SELECT * FROM sales WHERE date >= ?", "params": ["2025-01-01"]}
      },
      {
        "task_type": "data_transformation",
        "data": {"input_format": "csv", "output_format": "json", "transformations": ["aggregate", "normalize"]}
      },
      {
        "task_type": "report_generation",
        "data": {"template": "daily_sales", "format": "pdf", "recipients": ["manager@company.com"]}
      }
    ],
    "execution_mode": "sequential",
    "failure_strategy": "stop_on_error"
  }'

# Schedule recurring task with cron expression
curl -X POST http://localhost/tasks/schedule \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "task_type": "database_cleanup",
    "schedule": {
      "type": "cron",
      "expression": "0 2 * * *",
      "timezone": "UTC"
    },
    "data": {
      "cleanup_older_than_days": 30,
      "tables": ["task_history", "user_sessions", "audit_logs"],
      "backup_before_cleanup": true
    },
    "active": true,
    "max_iterations": null
  }'

Task Monitoring & Management

# Get detailed task status with progress information
curl -X GET http://localhost/tasks/status/ml_train_abc123def456 \
  -H "Authorization: Bearer $TOKEN" | jq

# Comprehensive task status response
{
  "status": "success",
  "data": {
    "task_id": "ml_train_abc123def456",
    "task_type": "machine_learning_training",
    "status": "running",
    "progress": {
      "percentage": 45.7,
      "current_epoch": 46,
      "total_epochs": 100,
      "current_loss": 0.234,
      "validation_accuracy": 0.89,
      "estimated_remaining": 1980
    },
    "execution": {
      "worker_id": "celery_worker_001",
      "worker_hostname": "worker-node-1",
      "started_at": "2025-01-01T12:01:15Z",
      "running_time": 2745,
      "memory_usage": "2.4GB",
      "cpu_usage": 85.2
    },
    "history": [
      {
        "timestamp": "2025-01-01T12:00:00Z",
        "status": "queued",
        "message": "Task submitted to queue"
      },
      {
        "timestamp": "2025-01-01T12:01:15Z",
        "status": "running",
        "message": "Task started by worker celery_worker_001"
      },
      {
        "timestamp": "2025-01-01T12:15:30Z",
        "status": "running",
        "message": "Training progress: Epoch 25/100, Loss: 0.456"
      }
    ],
    "resources": {
      "allocated_cpu": 4,
      "allocated_memory": "8GB",
      "gpu_allocated": true,
      "gpu_utilization": 92.1
    }
  }
}

# Get task execution logs in real-time
curl -X GET http://localhost/tasks/logs/ml_train_abc123def456 \
  -H "Authorization: Bearer $TOKEN" \
  -H "Accept: text/plain"

# Cancel running task
curl -X POST http://localhost/tasks/cancel/ml_train_abc123def456 \
  -H "Authorization: Bearer $TOKEN" \
  -d '{"reason": "User requested cancellation", "force": false}'

# Get comprehensive task statistics
curl -X GET http://localhost/tasks/stats \
  -H "Authorization: Bearer $TOKEN" | jq

{
  "status": "success",
  "data": {
    "overview": {
      "total_tasks": 15678,
      "completed_tasks": 14892,
      "failed_tasks": 123,
      "running_tasks": 8,
      "queued_tasks": 655,
      "success_rate": 0.949
    },
    "performance": {
      "avg_execution_time": 245.7,
      "median_execution_time": 89.2,
      "tasks_per_hour": 156,
      "peak_concurrent_tasks": 25
    },
    "by_type": [
      {
        "task_type": "data_processing",
        "count": 5678,
        "avg_duration": 123.4,
        "success_rate": 0.95
      },
      {
        "task_type": "machine_learning_training",
        "count": 89,
        "avg_duration": 3456.7,
        "success_rate": 0.92
      }
    ],
    "workers": {
      "active_workers": 4,
      "total_capacity": 16,
      "current_load": 0.62,
      "worker_health": [
        {
          "worker_id": "celery_worker_001",
          "status": "active",
          "current_tasks": 2,
          "total_processed": 1247
        }
      ]
    }
  }
}

📈 Monitoring & Observability

The system provides enterprise-grade monitoring with comprehensive metrics, alerting, and observability features.

🎛️ Monitoring Architecture

┌─────────────────────────────────────────────────────────────────┐
│                    Monitoring Stack                             │
├─────────────────────────────────────────────────────────────────┤
│ Application Metrics → Prometheus → Grafana                     │
│ System Metrics → Node Exporter → AlertManager                  │
│ Log Aggregation → ELK Stack → Kibana                          │
│ Custom Dashboards → Streamlit → Real-time Updates             │
│ Health Checks → Service Discovery → Auto-scaling              │
└─────────────────────────────────────────────────────────────────┘

📊 Real-time Monitoring Dashboard Features

Streamlit Dashboard Capabilities:

# Access monitoring dashboard
open http://localhost:8501

# Dashboard login (same credentials as API)
Username: admin
Password: admin123

Dashboard Sections:

System Overview
- Real-time CPU, Memory, Disk usage
- Network I/O and bandwidth utilization
- Database connection pool status
- Redis memory usage and hit rates
API Performance Metrics
- Request rate and response times
- Error rates by endpoint
- Geographic request distribution
- Load balancer performance
Task Processing Analytics
- Task queue depths and processing rates
- Worker pool utilization
- Task success/failure rates
- Resource usage by task type
Security Monitoring
- Authentication success/failure rates
- Rate limiting activations
- Suspicious activity detection
- Token usage patterns

Advanced Health Check System:

# Comprehensive health endpoint
curl -X GET http://localhost/health/status | jq

{
  "status": "healthy",
  "timestamp": "2025-01-01T12:00:00Z",
  "checks": {
    "database": {
      "status": "up",
      "response_time": 2.3,
      "connection_pool": {
        "active": 15,
        "idle": 5,
        "total": 20
      },
      "slow_queries": 0
    },
    "redis": {
      "status": "up",
      "response_time": 0.8,
      "memory_usage": "45.2MB",
      "connected_clients": 8,
      "hit_rate": 0.94
    },
    "celery": {
      "status": "up",
      "active_workers": 4,
      "pending_tasks": 12,
      "failed_tasks_last_hour": 1,
      "average_task_time": 67.3
    },
    "external_dependencies": {
      "email_service": {"status": "up", "response_time": 156.7},
      "notification_service": {"status": "up", "response_time": 89.2}
    }
  },
  "system": {
    "uptime": 86400,
    "cpu_usage": 15.2,
    "memory_usage": 45.7,
    "disk_usage": 23.1,
    "load_average": [0.85, 0.92, 1.15],
    "open_file_descriptors": 156
  },
  "alerts": []
}

# Node heartbeat system
curl -X GET http://localhost/health/nodes | jq

{
  "status": "success",
  "data": {
    "total_nodes": 3,
    "active_nodes": 3,
    "inactive_nodes": 0,
    "nodes": [
      {
        "node_id": "api-node-1",
        "hostname": "ha_api_service_1",
        "ip_address": "172.20.0.5",
        "port": 8000,
        "status": "active",
        "last_heartbeat": "2025-01-01T11:59:45Z",
        "response_time": 23.4,
        "health_score": 98.5,
        "metrics": {
          "cpu_usage": 14.2,
          "memory_usage": 42.1,
          "requests_per_minute": 45,
          "error_rate": 0.01
        }
      }
    ]
  }
}

🔧 Performance Optimization & Tuning

⚡ Application Performance Optimization

Database Query Optimization

# Optimized database queries with indexing
class User(db.Model):
    __tablename__ = 'users'

    # Optimized indexes for common queries
    __table_args__ = (
        db.Index('idx_username_active', 'username', 'is_active'),
        db.Index('idx_email_domain', 'email'),
        db.Index('idx_created_at', 'created_at'),
        db.Index('idx_last_login', 'last_login'),
    )

# Database connection pool optimization
SQLALCHEMY_ENGINE_OPTIONS = {
    'pool_size': 20,              # Base connection pool size
    'pool_timeout': 30,           # Timeout for getting connection
    'pool_recycle': 3600,         # Recycle connections every hour
    'pool_pre_ping': True,        # Validate connections before use
    'max_overflow': 10,           # Additional connections beyond pool_size
    'pool_reset_on_return': 'commit'  # Reset connection state
}

Redis Caching Strategy

# Advanced Redis caching implementation
class CacheManager:
    def __init__(self, redis_client):
        self.redis = redis_client
        self.default_ttl = 3600  # 1 hour

    def cache_with_tags(self, key, value, ttl=None, tags=None):
        """Cache with tag-based invalidation"""
        ttl = ttl or self.default_ttl

        # Store main cache entry
        self.redis.setex(key, ttl, json.dumps(value))

        # Store tags for batch invalidation
        if tags:
            for tag in tags:
                self.redis.sadd(f"tag:{tag}", key)
                self.redis.expire(f"tag:{tag}", ttl)

    def invalidate_by_tag(self, tag):
        """Invalidate all cache entries with specific tag"""
        keys = self.redis.smembers(f"tag:{tag}")
        if keys:
            self.redis.delete(*keys)
            self.redis.delete(f"tag:{tag}")

# Usage example
cache_manager = CacheManager(redis_client)

# Cache user data with tags
cache_manager.cache_with_tags(
    f"user:{user_id}",
    user_data,
    ttl=3600,
    tags=['users', f'user_{user_id}', 'profiles']
)

API Response Optimization

# Response compression and optimization
from flask import request, jsonify, make_response
import gzip

@app.after_request
def compress_response(response):
    """Compress API responses for better performance"""
    accept_encoding = request.headers.get('Accept-Encoding', '')

    if ('gzip' in accept_encoding.lower() and
        response.status_code < 300 and
        len(response.data) > 1000):

        gzipped_data = gzip.compress(response.data)

        response.data = gzipped_data
        response.headers['Content-Encoding'] = 'gzip'
        response.headers['Content-Length'] = len(gzipped_data)

    return response

# Database query optimization with eager loading
def get_user_with_tasks(user_id):
    """Optimized query with eager loading"""
    return User.query.options(
        db.joinedload(User.task_submissions),
        db.joinedload(User.node_heartbeats)
    ).filter_by(id=user_id).first()

🚀 Infrastructure Performance Tuning

NGINX Configuration Optimization

# High-performance NGINX configuration
worker_processes auto;
worker_cpu_affinity auto;
worker_rlimit_nofile 65535;

events {
    worker_connections 4096;
    use epoll;
    multi_accept on;
}

http {
    # Performance optimizations
    sendfile on;
    tcp_nopush on;
    tcp_nodelay on;
    keepalive_timeout 30;
    keepalive_requests 1000;

    # Buffer optimizations
    client_body_buffer_size 128k;
    client_max_body_size 50m;
    client_header_buffer_size 1k;
    large_client_header_buffers 4 4k;
    output_buffers 1 32k;
    postpone_output 1460;

    # Compression
    gzip on;
    gzip_vary on;
    gzip_min_length 1000;
    gzip_comp_level 6;
    gzip_types
        text/plain
        text/css
        application/json
        application/javascript
        text/xml
        application/xml
        application/xml+rss
        text/javascript;

    # Connection pooling to upstream
    upstream api_backend {
        least_conn;
        server api1:8000 weight=1 max_fails=3 fail_timeout=30s;
        server api2:8001 weight=1 max_fails=3 fail_timeout=30s;
        server api3:8002 weight=1 max_fails=3 fail_timeout=30s;
        keepalive 32;
    }
}

PostgreSQL Performance Tuning

-- PostgreSQL optimization settings
-- postgresql.conf optimizations

# Memory settings
shared_buffers = 256MB                 # 25% of system RAM
effective_cache_size = 1GB             # 75% of system RAM
work_mem = 4MB                         # Per-operation memory
maintenance_work_mem = 64MB            # Maintenance operations

# Checkpoint settings
checkpoint_completion_target = 0.9
wal_buffers = 16MB
wal_compression = on

# Query planner
default_statistics_target = 100
random_page_cost = 1.1                 # For SSD storage

# Connection settings
max_connections = 100
shared_preload_libraries = 'pg_stat_statements'

# Monitoring queries
SELECT
    query,
    calls,
    total_time,
    mean_time,
    rows
FROM pg_stat_statements
ORDER BY total_time DESC
LIMIT 10;

🐳 Advanced Docker Configuration

Production Docker Compose Setup

# docker-compose.production.yml
version: "3.8"

services:
  # API services with resource limits
  api1: &api_service
    build: .
    deploy:
      resources:
        limits:
          cpus: "1.0"
          memory: 1G
        reservations:
          cpus: "0.5"
          memory: 512M
      restart_policy:
        condition: on-failure
        delay: 5s
        max_attempts: 3
    environment:
      - FLASK_ENV=production
      - API_WORKERS=4
      - GUNICORN_TIMEOUT=120
      - GUNICORN_KEEPALIVE=5
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health/ping"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 40s
    logging:
      driver: "json-file"
      options:
        max-size: "100m"
        max-file: "3"
    security_opt:
      - no-new-privileges:true

  api2:
    <<: *api_service
    environment:
      - FLASK_ENV=production
      - API_PORT=8001
      - API_WORKERS=4
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8001/health/ping"]

  api3:
    <<: *api_service
    environment:
      - FLASK_ENV=production
      - API_PORT=8002
      - API_WORKERS=4
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8002/health/ping"]

  # Production PostgreSQL with backup
  postgres:
    image: postgres:15-alpine
    deploy:
      resources:
        limits:
          cpus: "2.0"
          memory: 2G
    environment:
      - POSTGRES_DB=${POSTGRES_DB}
      - POSTGRES_USER=${POSTGRES_USER}
      - POSTGRES_PASSWORD=${POSTGRES_PASSWORD}
    volumes:
      - postgres_data:/var/lib/postgresql/data
      - ./backups:/backups
      - ./init-scripts:/docker-entrypoint-initdb.d
    command: |
      postgres 
      -c max_connections=100
      -c shared_buffers=256MB
      -c effective_cache_size=1GB
      -c work_mem=4MB
      -c maintenance_work_mem=64MB
      -c checkpoint_completion_target=0.9
      -c wal_buffers=16MB
      -c default_statistics_target=100
      -c log_statement=all
      -c log_duration=on
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER} -d ${POSTGRES_DB}"]
      interval: 10s
      timeout: 5s
      retries: 10
      start_period: 30s

Kubernetes Deployment Configuration

# k8s-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ha-api-deployment
  labels:
    app: ha-api
spec:
  replicas: 3
  selector:
    matchLabels:
      app: ha-api
  template:
    metadata:
      labels:
        app: ha-api
    spec:
      containers:
        - name: ha-api
          image: ha-api:latest
          ports:
            - containerPort: 8000
          env:
            - name: DATABASE_URL
              valueFrom:
                secretKeyRef:
                  name: ha-api-secrets
                  key: database-url
            - name: REDIS_URL
              valueFrom:
                configMapKeyRef:
                  name: ha-api-config
                  key: redis-url
          resources:
            requests:
              memory: "512Mi"
              cpu: "500m"
            limits:
              memory: "1Gi"
              cpu: "1000m"
          livenessProbe:
            httpGet:
              path: /health/ping
              port: 8000
            initialDelaySeconds: 30
            periodSeconds: 30
          readinessProbe:
            httpGet:
              path: /health/status
              port: 8000
            initialDelaySeconds: 5
            periodSeconds: 10

---
apiVersion: v1
kind: Service
metadata:
  name: ha-api-service
spec:
  selector:
    app: ha-api
  ports:
    - protocol: TCP
      port: 80
      targetPort: 8000
  type: LoadBalancer

🔄 CI/CD Pipeline Configuration

GitHub Actions Workflow

# .github/workflows/deploy.yml
name: Deploy HA API

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  test:
    runs-on: ubuntu-latest
    services:
      postgres:
        image: postgres:15
        env:
          POSTGRES_PASSWORD: postgres
          POSTGRES_DB: test_db
        options: >-
          --health-cmd pg_isready
          --health-interval 10s
          --health-timeout 5s
          --health-retries 5
      redis:
        image: redis:7
        options: >-
          --health-cmd "redis-cli ping"
          --health-interval 10s
          --health-timeout 5s
          --health-retries 5

    steps:
      - uses: actions/checkout@v3

      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: "3.11"

      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install -r requirements.txt

      - name: Run tests
        env:
          DATABASE_URL: postgresql://postgres:postgres@localhost:5432/test_db
          REDIS_URL: redis://localhost:6379/0
        run: |
          python -m pytest tests/ -v --cov=app --cov-report=xml

      - name: Upload coverage to Codecov
        uses: codecov/codecov-action@v3
        with:
          file: ./coverage.xml

  security-scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Run security scan
        uses: pypa/gh-action-pip-audit@v1.0.8
        with:
          inputs: requirements.txt

  build-and-deploy:
    needs: [test, security-scan]
    runs-on: ubuntu-latest
    if: github.ref == 'refs/heads/main'

    steps:
      - uses: actions/checkout@v3

      - name: Build Docker image
        run: |
          docker build -t ha-api:${{ github.sha }} .
          docker tag ha-api:${{ github.sha }} ha-api:latest

      - name: Deploy to staging
        if: github.event_name == 'pull_request'
        run: |
          echo "Deploying to staging environment"
          # Add staging deployment commands

      - name: Deploy to production
        if: github.ref == 'refs/heads/main'
        run: |
          echo "Deploying to production environment"
          # Add production deployment commands

🧪 Comprehensive Testing Strategy

The system implements multi-layer testing with unit tests, integration tests, load testing, and security testing.

Test Architecture & Coverage

┌─────────────────────────────────────────────────────────────────┐
│                        Testing Pyramid                         │
├─────────────────────────────────────────────────────────────────┤
│ E2E Tests → Selenium/Playwright → User Workflows              │
│ Integration Tests → API Testing → Service Interactions        │
│ Unit Tests → pytest → Individual Components                   │
│ Load Tests → Locust → Performance & Scalability              │
│ Security Tests → OWASP ZAP → Vulnerability Assessment        │
└─────────────────────────────────────────────────────────────────┘

Advanced Testing Commands

# Run comprehensive test suite
python -m pytest tests/ -v --cov=app --cov-report=html --cov-report=term

# Run specific test categories
python -m pytest tests/unit/ -v --tb=short
python -m pytest tests/integration/ -v --tb=short
python -m pytest tests/api/ -v --tb=short

# Run tests with different environments
pytest tests/ --env=staging --tb=short
pytest tests/ --env=production --tb=short --no-cov

# Load testing with Locust
locust -f tests/load/locustfile.py --host=http://localhost

Automated API Testing Suite

# tests/api/test_advanced_api.py
import pytest
import requests
import json
from concurrent.futures import ThreadPoolExecutor
import time

@pytest.fixture
def api_client():
    """Advanced API client with authentication"""
    return APITestClient(base_url="http://localhost")

class APITestClient:
    def __init__(self, base_url):
        self.base_url = base_url
        self.session = requests.Session()
        self.access_token = None

    def authenticate(self, username="admin", password="admin123"):
        """Authenticate and store token"""
        response = self.session.post(f"{self.base_url}/auth/login", json={
            "username": username,
            "password": password
        })
        assert response.status_code == 200
        self.access_token = response.json()["data"]["access_token"]
        self.session.headers.update({
            "Authorization": f"Bearer {self.access_token}"
        })
        return self.access_token

def test_load_balancer_distribution(api_client):
    """Test load balancer distributes requests evenly"""
    api_client.authenticate()

    node_counts = {}
    for _ in range(100):
        response = api_client.session.get(f"{api_client.base_url}/health/ping")
        assert response.status_code == 200

        node_id = response.json()["node_id"]
        node_counts[node_id] = node_counts.get(node_id, 0) + 1

    # Verify distribution is reasonably even
    assert len(node_counts) >= 2  # At least 2 nodes receiving requests
    max_requests = max(node_counts.values())
    min_requests = min(node_counts.values())
    assert (max_requests - min_requests) / max_requests < 0.3  # Within 30%

def test_concurrent_task_submission(api_client):
    """Test system handles concurrent task submissions"""
    api_client.authenticate()

    def submit_task(task_id):
        response = api_client.session.post(f"{api_client.base_url}/tasks/submit", json={
            "task_type": "sample",
            "data": {"test_id": task_id}
        })
        return response.status_code, response.json()

    # Submit 50 tasks concurrently
    with ThreadPoolExecutor(max_workers=10) as executor:
        futures = [executor.submit(submit_task, i) for i in range(50)]
        results = [future.result() for future in futures]

    # Verify all tasks were accepted
    success_count = sum(1 for status, _ in results if status == 200)
    assert success_count >= 45  # Allow some failures due to rate limiting

def test_database_transaction_integrity(api_client):
    """Test database transaction integrity under load"""
    api_client.authenticate()

    # Create test user
    user_data = {
        "username": f"test_user_{int(time.time())}",
        "email": f"test_{int(time.time())}@example.com",
        "password": "testpass123"
    }

    response = api_client.session.post(f"{api_client.base_url}/auth/register", json=user_data)
    assert response.status_code == 200
    user_id = response.json()["data"]["user"]["id"]

    # Submit multiple tasks for the same user
    def submit_user_task(task_num):
        return api_client.session.post(f"{api_client.base_url}/tasks/submit", json={
            "task_type": "user_specific",
            "data": {"user_id": user_id, "task_num": task_num}
        })

    with ThreadPoolExecutor(max_workers=5) as executor:
        futures = [executor.submit(submit_user_task, i) for i in range(10)]
        responses = [future.result() for future in futures]

    # Verify all tasks were properly associated with user
    success_responses = [r for r in responses if r.status_code == 200]
    assert len(success_responses) == 10

def test_auth_token_security(api_client):
    """Test JWT token security features"""
    # Test token expiration
    token = api_client.authenticate()

    # Test malformed token
    api_client.session.headers.update({"Authorization": "Bearer invalid_token"})
    response = api_client.session.get(f"{api_client.base_url}/api/protected")
    assert response.status_code == 401

    # Test missing token
    del api_client.session.headers["Authorization"]
    response = api_client.session.get(f"{api_client.base_url}/api/protected")
    assert response.status_code == 401

    # Test valid token
    api_client.session.headers.update({"Authorization": f"Bearer {token}"})
    response = api_client.session.get(f"{api_client.base_url}/api/protected")
    assert response.status_code == 200

@pytest.mark.performance
def test_response_time_sla(api_client):
    """Test API response times meet SLA requirements"""
    api_client.authenticate()

    endpoints = [
        "/health/ping",
        "/health/status",
        "/api/protected",
        "/tasks/stats"
    ]

    for endpoint in endpoints:
        times = []
        for _ in range(10):
            start_time = time.time()
            response = api_client.session.get(f"{api_client.base_url}{endpoint}")
            end_time = time.time()

            assert response.status_code == 200
            times.append(end_time - start_time)

        avg_time = sum(times) / len(times)
        p95_time = sorted(times)[int(0.95 * len(times))]

        # SLA requirements
        if endpoint == "/health/ping":
            assert avg_time < 0.1  # 100ms average
            assert p95_time < 0.2  # 200ms P95
        else:
            assert avg_time < 0.5  # 500ms average
            assert p95_time < 1.0  # 1s P95

Load Testing Configuration

# tests/load/locustfile.py
from locust import HttpUser, task, between
import json
import random

class APIUser(HttpUser):
    wait_time = between(1, 3)

    def on_start(self):
        """Authenticate user on start"""
        self.login()

    def login(self):
        """Login and store token"""
        response = self.client.post("/auth/login", json={
            "username": "admin",
            "password": "admin123"
        })

        if response.status_code == 200:
            token = response.json()["data"]["access_token"]
            self.client.headers.update({
                "Authorization": f"Bearer {token}"
            })

    @task(3)
    def health_check(self):
        """Frequent health checks"""
        self.client.get("/health/ping")

    @task(2)
    def get_status(self):
        """Get API status"""
        self.client.get("/api/status")

    @task(2)
    def protected_endpoint(self):
        """Access protected endpoint"""
        self.client.get("/api/protected")

    @task(1)
    def submit_task(self):
        """Submit background task"""
        self.client.post("/tasks/submit", json={
            "task_type": "sample",
            "data": {"load_test": True, "user_id": random.randint(1, 1000)}
        })

    @task(1)
    def get_task_stats(self):
        """Get task statistics"""
        self.client.get("/tasks/stats")

class AdminUser(HttpUser):
    wait_time = between(2, 5)
    weight = 1  # Fewer admin users

    def on_start(self):
        response = self.client.post("/auth/login", json={
            "username": "admin",
            "password": "admin123"
        })

        if response.status_code == 200:
            token = response.json()["data"]["access_token"]
            self.client.headers.update({
                "Authorization": f"Bearer {token}"
            })

    @task
    def get_metrics(self):
        """Admin metrics endpoint"""
        self.client.get("/api/metrics")

    @task
    def get_users(self):
        """List users"""
        self.client.get("/api/users")

Security Testing

# Security testing with OWASP ZAP
docker run -t owasp/zap2docker-stable zap-baseline.py \
    -t http://localhost \
    -r security_report.html

# SQL injection testing
sqlmap -u "http://localhost/api/users?id=1" \
    --cookie="session=your_session_token" \
    --dbs --batch

# SSL/TLS testing
testssl.sh --full https://yourdomain.com

# Dependency vulnerability scanning
safety check -r requirements.txt
bandit -r app/ -f json -o security_report.json

🔧 Advanced Troubleshooting & Diagnostics

🔍 Comprehensive Diagnostic Tools

System Health Diagnostic Script

#!/bin/bash
# comprehensive-diagnostics.sh

echo "🔍 Starting comprehensive system diagnostics..."
echo "================================================"

# Check Docker containers
echo "📦 Docker Container Status:"
docker-compose ps
echo ""

# Check resource usage
echo "💾 System Resource Usage:"
echo "CPU Usage: $(top -bn1 | grep load | awk '{printf "%.2f\n", $(NF-2)}')"
echo "Memory Usage: $(free | grep Mem | awk '{printf "%.2f%%\n", ($3/$2) * 100.0}')"
echo "Disk Usage: $(df -h / | awk 'NR==2{printf "%s\n", $5}')"
echo ""

# Database diagnostics
echo "🗄️ Database Diagnostics:"
docker-compose exec postgres psql -U postgres -d postgres -c "
SELECT
    'Active Connections' as metric,
    count(*) as value
FROM pg_stat_activity
WHERE state = 'active'
UNION ALL
SELECT
    'Database Size' as metric,
    pg_size_pretty(pg_database_size('postgres')) as value
UNION ALL
SELECT
    'Longest Running Query' as metric,
    COALESCE(max(EXTRACT(EPOCH FROM (now() - query_start)))::text || ' seconds', 'None') as value
FROM pg_stat_activity
WHERE state = 'active' AND query != '<IDLE>';
"

# Redis diagnostics
echo "📊 Redis Diagnostics:"
docker-compose exec redis redis-cli info | grep -E "(used_memory_human|connected_clients|total_commands_processed|keyspace_hits|keyspace_misses)"
echo ""

# API endpoint health
echo "🌐 API Endpoint Health:"
endpoints=("/health/ping" "/health/status" "/api/status")
for endpoint in "${endpoints[@]}"; do
    response_time=$(curl -o /dev/null -s -w "%{time_total}" http://localhost$endpoint)
    status_code=$(curl -o /dev/null -s -w "%{http_code}" http://localhost$endpoint)
    echo "$endpoint: $status_code (${response_time}s)"
done
echo ""

# Celery diagnostics
echo "⚙️ Celery Worker Status:"
docker-compose exec celery_worker celery -A celery_worker.celery inspect active
docker-compose exec celery_worker celery -A celery_worker.celery inspect stats
echo ""

# Log analysis
echo "📝 Recent Error Analysis:"
echo "API Errors (last 100 lines):"
docker-compose logs api1 api2 api3 --tail=100 | grep -i error | tail -5
echo ""

echo "Database Errors (last 100 lines):"
docker-compose logs postgres --tail=100 | grep -i error | tail -5
echo ""

# Network diagnostics
echo "🌐 Network Connectivity:"
echo "Internal DNS resolution:"
docker-compose exec api1 nslookup postgres
docker-compose exec api1 nslookup redis
echo ""

# Performance metrics
echo "📈 Performance Metrics:"
echo "Load Average: $(uptime | awk -F'load average:' '{print $2}')"
echo "Active Processes: $(ps aux | wc -l)"
echo "Open Files: $(lsof | wc -l)"

echo ""
echo "✅ Diagnostics completed"

Database Performance Analysis

-- database-performance-analysis.sql

-- Long running queries
SELECT
    pid,
    now() - pg_stat_activity.query_start AS duration,
    query,
    state,
    usename,
    application_name
FROM pg_stat_activity
WHERE (now() - pg_stat_activity.query_start) > interval '5 minutes'
AND state = 'active'
ORDER BY duration DESC;

-- Database locks
SELECT
    t.schemaname,
    t.tablename,
    l.locktype,
    l.mode,
    l.granted,
    a.usename,
    a.query,
    a.query_start,
    age(now(), a.query_start) AS "age"
FROM pg_stat_activity a
JOIN pg_locks l ON l.pid = a.pid
JOIN pg_stat_user_tables t ON l.relation = t.relid
WHERE a.state = 'active'
ORDER BY a.query_start;

-- Index usage analysis
SELECT
    schemaname,
    tablename,
    attname,
    n_distinct,
    correlation,
    most_common_vals
FROM pg_stats
WHERE schemaname = 'public'
AND n_distinct > 100
ORDER BY n_distinct DESC;

-- Table size analysis
SELECT
    schemaname as table_schema,
    tablename as table_name,
    pg_size_pretty(pg_total_relation_size(schemaname||'.'||tablename)) as size
FROM pg_tables
WHERE schemaname = 'public'
ORDER BY pg_total_relation_size(schemaname||'.'||tablename) DESC;

Advanced Monitoring Queries

# monitoring/advanced_monitoring.py
import psutil
import redis
import psycopg2
from datetime import datetime, timedelta
import json

class AdvancedMonitoringService:
    def __init__(self):
        self.redis_client = redis.from_url("redis://localhost:6379/0")
        self.db_conn = psycopg2.connect("postgresql://postgres:postgres@localhost:5678/postgres")

    def collect_comprehensive_metrics(self):
        """Collect all system metrics"""
        metrics = {
            "timestamp": datetime.utcnow().isoformat(),
            "system": self.get_system_metrics(),
            "database": self.get_database_metrics(),
            "redis": self.get_redis_metrics(),
            "application": self.get_application_metrics()
        }

        # Store metrics in Redis for dashboard
        self.redis_client.setex(
            "system_metrics",
            300,  # 5 minutes TTL
            json.dumps(metrics)
        )

        return metrics

    def get_system_metrics(self):
        """Get system-level metrics"""
        cpu_percent = psutil.cpu_percent(interval=1)
        memory = psutil.virtual_memory()
        disk = psutil.disk_usage('/')
        network = psutil.net_io_counters()

        return {
            "cpu": {
                "usage_percent": cpu_percent,
                "load_average": psutil.getloadavg(),
                "cpu_count": psutil.cpu_count()
            },
            "memory": {
                "total": memory.total,
                "available": memory.available,
                "percent": memory.percent,
                "used": memory.used
            },
            "disk": {
                "total": disk.total,
                "used": disk.used,
                "free": disk.free,
                "percent": disk.percent
            },
            "network": {
                "bytes_sent": network.bytes_sent,
                "bytes_recv": network.bytes_recv,
                "packets_sent": network.packets_sent,
                "packets_recv": network.packets_recv
            }
        }

    def get_database_metrics(self):
        """Get PostgreSQL metrics"""
        with self.db_conn.cursor() as cursor:
            # Connection statistics
            cursor.execute("""
                SELECT count(*) as active_connections
                FROM pg_stat_activity
                WHERE state = 'active'
            """)
            active_connections = cursor.fetchone()[0]

            # Database size
            cursor.execute("""
                SELECT pg_database_size(current_database()) as db_size
            """)
            db_size = cursor.fetchone()[0]

            # Query statistics
            cursor.execute("""
                SELECT
                    sum(n_tup_ins) as inserts,
                    sum(n_tup_upd) as updates,
                    sum(n_tup_del) as deletes
                FROM pg_stat_user_tables
            """)
            query_stats = cursor.fetchone()

            return {
                "active_connections": active_connections,
                "database_size": db_size,
                "operations": {
                    "inserts": query_stats[0] or 0,
                    "updates": query_stats[1] or 0,
                    "deletes": query_stats[2] or 0
                }
            }

    def get_redis_metrics(self):
        """Get Redis metrics"""
        info = self.redis_client.info()

        return {
            "memory_usage": info.get('used_memory'),
            "connected_clients": info.get('connected_clients'),
            "total_commands": info.get('total_commands_processed'),
            "keyspace_hits": info.get('keyspace_hits'),
            "keyspace_misses": info.get('keyspace_misses'),
            "hit_rate": info.get('keyspace_hits', 0) / max(1, info.get('keyspace_hits', 0) + info.get('keyspace_misses', 0))
        }

    def get_application_metrics(self):
        """Get application-specific metrics"""
        # API request metrics from Redis
        total_requests = self.redis_client.get('total_requests') or 0

        # Task statistics
        task_stats = {
            "pending_tasks": self.redis_client.llen('celery'),
            "completed_tasks": self.redis_client.get('completed_tasks') or 0,
            "failed_tasks": self.redis_client.get('failed_tasks') or 0
        }

        return {
            "api_requests": int(total_requests),
            "task_statistics": task_stats
        }

    def detect_anomalies(self, metrics):
        """Detect system anomalies"""
        anomalies = []

        # CPU usage anomaly
        if metrics["system"]["cpu"]["usage_percent"] > 90:
            anomalies.append({
                "type": "high_cpu_usage",
                "severity": "critical",
                "value": metrics["system"]["cpu"]["usage_percent"],
                "threshold": 90
            })

        # Memory usage anomaly
        if metrics["system"]["memory"]["percent"] > 85:
            anomalies.append({
                "type": "high_memory_usage",
                "severity": "warning",
                "value": metrics["system"]["memory"]["percent"],
                "threshold": 85
            })

        # Database connection anomaly
        if metrics["database"]["active_connections"] > 50:
            anomalies.append({
                "type": "high_db_connections",
                "severity": "warning",
                "value": metrics["database"]["active_connections"],
                "threshold": 50
            })

        return anomalies

# Usage
if __name__ == "__main__":
    monitor = AdvancedMonitoringService()
    metrics = monitor.collect_comprehensive_metrics()
    anomalies = monitor.detect_anomalies(metrics)

    if anomalies:
        print("🚨 Anomalies detected:")
        for anomaly in anomalies:
            print(f"  - {anomaly['type']}: {anomaly['value']} (threshold: {anomaly['threshold']})")
    else:
        print("✅ All systems operating normally")

🚨 Common Issues & Solutions

Performance Issues

Issue: High Database Connection Usage

# Diagnosis
docker-compose exec postgres psql -U postgres -c "
SELECT count(*), state
FROM pg_stat_activity
GROUP BY state;"

# Solution: Optimize connection pool
# In config.py, adjust:
SQLALCHEMY_ENGINE_OPTIONS = {
    'pool_size': 10,           # Reduce from 20
    'pool_timeout': 60,        # Increase timeout
    'pool_recycle': 1800,      # Recycle connections more frequently
    'pool_pre_ping': True
}

Issue: High Memory Usage

# Diagnosis
docker stats --no-stream

# Solution: Implement memory limits
# In docker-compose.yml:
services:
  api1:
    mem_limit: 1g
    memswap_limit: 1g

Issue: Slow API Response Times

# Diagnosis
curl -w "Total time: %{time_total}s\n" -o /dev/null -s http://localhost/api/protected

# Solutions:
# 1. Enable Redis caching
# 2. Optimize database queries
# 3. Add database indexes
# 4. Implement response caching

Authentication Issues

Issue: JWT Token Validation Failures

# Diagnosis
docker-compose logs nginx | grep "auth_request"

# Check JWT secret consistency
docker-compose exec api1 printenv | grep JWT_SECRET_KEY
docker-compose exec api2 printenv | grep JWT_SECRET_KEY

# Solution: Ensure all services use same JWT secret
# Verify in docker-compose.yml that all API services have:
environment:
  - JWT_SECRET_KEY=your-consistent-jwt-secret-key

Issue: NGINX Authentication Loops

# Diagnosis
docker-compose logs nginx | grep "auth_request"

# Solution: Check NGINX configuration
# Ensure /auth/validate doesn't have auth_request directive
location = /auth/validate {
    internal;
    proxy_pass http://api_backend/auth/validate;
    # No auth_request here!
}

Task Processing Issues

Issue: Celery Workers Not Processing Tasks

# Diagnosis
docker-compose exec celery_worker celery -A celery_worker.celery inspect active
docker-compose exec celery_worker celery -A celery_worker.celery inspect stats

# Check Redis connection
docker-compose exec celery_worker python -c "
import redis
r = redis.from_url('redis://redis:6379/0')
print(r.ping())
"

# Solution: Restart workers and check configuration
docker-compose restart celery_worker celery_beat

Issue: High Task Failure Rate

# Diagnosis
curl -H "Authorization: Bearer $TOKEN" http://localhost/tasks/stats | jq '.data.overview'

# Check worker logs
docker-compose logs celery_worker | grep ERROR

# Solutions:
# 1. Increase worker timeout
# 2. Add retry logic
# 3. Implement task validation
# 4. Monitor resource usage

🎯 Production Optimization Checklist

✅ Pre-Deployment Checklist

Security Configuration:

Changed all default passwords and secrets
Enabled SSL/TLS with valid certificates
Configured firewall rules and security groups
Implemented proper logging and monitoring
Set up intrusion detection system
Configured automated security updates
Implemented backup encryption
Set up access control and user management

Performance Configuration:

Optimized database connection pools
Configured Redis memory limits and eviction policies
Set up NGINX caching and compression
Implemented API rate limiting
Configured Celery worker pools
Set up monitoring and alerting
Optimized Docker resource limits
Implemented health checks for all services

Reliability Configuration:

Set up automated backups
Configured disaster recovery procedures
Implemented health checks and monitoring
Set up log aggregation and analysis
Configured alerting for critical issues
Implemented graceful shutdown procedures
Set up service dependencies and startup order
Configured resource limits and quotas

📊 Performance Benchmarks

Target Performance Metrics:

API Response Time: < 200ms (95th percentile)
Database Query Time: < 50ms (average)
Task Processing Time: < 30s (simple tasks)
System Uptime: > 99.9%
Error Rate: < 0.1%
Memory Usage: < 80% of available
CPU Usage: < 70% average
Disk Usage: < 80% of available

Load Testing Results:

# Expected performance under load
# 1000 concurrent users
# 10,000 requests per minute
# Average response time: 150ms
# 99.9% uptime maintained

🚀 The HA API with Celery system is now fully documented with enterprise-grade architecture, comprehensive deployment guides, advanced monitoring, security best practices, and production-ready configurations. This documentation provides everything needed for development, testing, deployment, and maintenance of a high-availability API system.

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
app		app
init-scripts		init-scripts
migrations		migrations
monitoring		monitoring
nginx		nginx
.env.example		.env.example
.gitignore		.gitignore
Dockerfile		Dockerfile
README.md		README.md
STRUCTURE.md		STRUCTURE.md
celery_worker.py		celery_worker.py
config.py		config.py
deploy.sh		deploy.sh
dev_server.py		dev_server.py
docker-compose.yml		docker-compose.yml
docker-deploy.sh		docker-deploy.sh
docker-manage.sh		docker-manage.sh
gunicorn.conf.py		gunicorn.conf.py
init_db.py		init_db.py
requirements.txt		requirements.txt
reset_database.py		reset_database.py
run.py		run.py
test_api.py		test_api.py
validate_db.py		validate_db.py
verify_config.sh		verify_config.sh

dwickyfp/flask-ha-api-celery-nginx

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