Rename booking service to taskprocessing service

- Renamed services/booking directory to services/taskprocessing
- Updated docker-compose.yml to use taskprocessing service name and correct environment variables
- Updated infrastructure/docker/docker-compose.yml with correct env vars (TASKPROCESSING_SERVER_IPADDRESS/PORT)
- Fixed PROJECT_DESCRIPTION.md to reference task service instead of booking
- Updated scripts/setup.sh and scripts/redis_dry_run.py to use taskprocessing paths
- Fixed redis_dry_run.py test function to properly document BookingRedisHelper legacy class name
- Updated REDIS_SETUP.md documentation paths
- All services tested and running correctly

Author: Kamal Sai Devarapalli

PROJECT_DESCRIPTION.md

@@ -1 +1 @@
-# EventStreamMonitor - Project Description ## What is EventStreamMonitor? **EventStreamMonitor** is a production-ready, real-time microservices monitoring platform that collects, streams, and visualizes application logs and errors across multiple services in real-time. ## Core Purpose The project demonstrates modern DevOps practices by providing: - **Real-time log collection** from multiple microservices - **Event streaming** using Apache Kafka - **Error monitoring and alerting** with automatic filtering - **Live dashboard** for error visualization - **Production-ready architecture** with microservices ## Key Features ### 1. Real-Time Log Streaming - Collects logs from multiple microservices simultaneously - Streams logs to Apache Kafka for processing - Automatic error level detection (ERROR, CRITICAL) - Service identification and metadata tagging ### 2. Error Monitoring & Filtering - Automatically filters ERROR and CRITICAL level logs - Stores errors for analysis and alerting - Provides statistics by service and error type - Real-time error tracking and visualization ### 3. Multi-Service Dashboard - Live dashboard showing error statistics - Real-time error feed with auto-refresh - Service-level error breakdown - Error details with timestamps, stack traces, and context ### 4. Microservices Architecture - Independent, scalable services - Event-driven communication via Kafka - Redis caching for performance - Session management across services - Database per service (isolation) ### 5. Production-Ready Features - Docker containerization - Health checks and monitoring - Grafana integration ready - RESTful APIs for automation - Comprehensive error handling ## Tech Stack - **Backend**: Python, Flask - **Message Broker**: Apache Kafka - **Cache/Sessions**: Redis - **Databases**: PostgreSQL (per service) - **Containerization**: Docker, Docker Compose - **Monitoring**: Custom Dashboard, Grafana-ready - **API**: RESTful APIs ## Architecture Overview ``` Microservices (User, Booking, etc.) - Generate logs and events - Stream to Kafka Kafka Topics Apache Kafka - application-logs - application-logs-errors Consumer filters errors Log Monitor Service - Error filtering - Error storage - API endpoints Dashboard Grafana (Web UI) Integration ``` ## Use Cases ### 1. **DevOps Monitoring** - Monitor multiple microservices from one dashboard - Track errors in real-time - Identify problematic services quickly - Set up alerts for critical errors ### 2. **Development & Debugging** - Real-time error visibility - Service-level error tracking - Error context and stack traces - Historical error analysis ### 3. **Production Monitoring** - Production error monitoring - Service health tracking - Performance insights - Grafana dashboards for operations ### 4. **Learning & Portfolio** - Demonstrates microservices architecture - Shows Kafka event streaming - Redis caching patterns - Docker containerization - Real-time monitoring systems ## What Makes It Stand Out ### For Recruiters/Employers: - **DevOps Skills**: Real-time monitoring, log aggregation - **Microservices**: Scalable, distributed architecture - **Event-Driven**: Kafka for event streaming - **Production Ready**: Docker, health checks, error handling - **Modern Stack**: Kafka, Redis, Python, Flask - **Full-Stack**: Backend APIs + Dashboard UI ### Technical Highlights: - Real-time processing with Kafka - Scalable microservices architecture - Caching and session management - Error filtering and alerting - Dashboard visualization - Grafana integration ready ## Project Structure ``` EventStreamMonitor/ services/ # Microservices usermanagement/ # User service booking/ # Booking service notification/ # Notification service logmonitor/ # Log monitoring service common/ # Shared libraries infrastructure/ # Docker, K8s configs docs/ # Documentation scripts/ # Utility scripts ``` ## Quick Start ```bash # Start all services docker-compose up -d # Access dashboard open http://localhost:5004 # Stream test errors python3 scripts/quick_stream_errors.py ``` ## Perfect For - **First GitHub Project**: Professional, complete, showcases skills - **Portfolio Project**: Demonstrates real-world DevOps practices - **Learning Project**: Covers microservices, Kafka, monitoring - **Interview Project**: Shows system design and architecture skills ## Value Proposition **EventStreamMonitor** demonstrates your ability to: 1. Build production-ready microservices 2. Implement real-time monitoring systems 3. Work with modern DevOps tools (Kafka, Redis, Docker) 4. Create scalable, event-driven architectures 5. Build monitoring and observability tools This is exactly what recruiters look for - practical, production-ready code that solves real problems! 
+# EventStreamMonitor - Project Description ## What is EventStreamMonitor? **EventStreamMonitor** is a production-ready, real-time microservices monitoring platform that collects, streams, and visualizes application logs and errors across multiple services in real-time. ## Core Purpose The project demonstrates modern DevOps practices by providing: - **Real-time log collection** from multiple microservices - **Event streaming** using Apache Kafka - **Error monitoring and alerting** with automatic filtering - **Live dashboard** for error visualization - **Production-ready architecture** with microservices ## Key Features ### 1. Real-Time Log Streaming - Collects logs from multiple microservices simultaneously - Streams logs to Apache Kafka for processing - Automatic error level detection (ERROR, CRITICAL) - Service identification and metadata tagging ### 2. Error Monitoring & Filtering - Automatically filters ERROR and CRITICAL level logs - Stores errors for analysis and alerting - Provides statistics by service and error type - Real-time error tracking and visualization ### 3. Multi-Service Dashboard - Live dashboard showing error statistics - Real-time error feed with auto-refresh - Service-level error breakdown - Error details with timestamps, stack traces, and context ### 4. Microservices Architecture - Independent, scalable services - Event-driven communication via Kafka - Redis caching for performance - Session management across services - Database per service (isolation) ### 5. Production-Ready Features - Docker containerization - Health checks and monitoring - Grafana integration ready - RESTful APIs for automation - Comprehensive error handling ## Tech Stack - **Backend**: Python, Flask - **Message Broker**: Apache Kafka - **Cache/Sessions**: Redis - **Databases**: PostgreSQL (per service) - **Containerization**: Docker, Docker Compose - **Monitoring**: Custom Dashboard, Grafana-ready - **API**: RESTful APIs ## Architecture Overview ``` Microservices (User, Task, Notification, etc.) - Generate logs and events - Stream to Kafka Kafka Topics Apache Kafka - application-logs - application-logs-errors Consumer filters errors Log Monitor Service - Error filtering - Error storage - API endpoints Dashboard Grafana (Web UI) Integration ``` ## Use Cases ### 1. **DevOps Monitoring** - Monitor multiple microservices from one dashboard - Track errors in real-time - Identify problematic services quickly - Set up alerts for critical errors ### 2. **Development & Debugging** - Real-time error visibility - Service-level error tracking - Error context and stack traces - Historical error analysis ### 3. **Production Monitoring** - Production error monitoring - Service health tracking - Performance insights - Grafana dashboards for operations ### 4. **Learning & Portfolio** - Demonstrates microservices architecture - Shows Kafka event streaming - Redis caching patterns - Docker containerization - Real-time monitoring systems ## What Makes It Stand Out ### For Recruiters/Employers: - **DevOps Skills**: Real-time monitoring, log aggregation - **Microservices**: Scalable, distributed architecture - **Event-Driven**: Kafka for event streaming - **Production Ready**: Docker, health checks, error handling - **Modern Stack**: Kafka, Redis, Python, Flask - **Full-Stack**: Backend APIs + Dashboard UI ### Technical Highlights: - Real-time processing with Kafka - Scalable microservices architecture - Caching and session management - Error filtering and alerting - Dashboard visualization - Grafana integration ready ## Project Structure ``` EventStreamMonitor/ services/ # Microservices usermanagement/ # User service task/ # Task service notification/ # Notification service logmonitor/ # Log monitoring service common/ # Shared libraries infrastructure/ # Docker, K8s configs docs/ # Documentation scripts/ # Utility scripts ``` ## Quick Start ```bash # Start all services docker-compose up -d # Access dashboard open http://localhost:5004 # Stream test errors python3 scripts/quick_stream_errors.py ``` ## Perfect For - **First GitHub Project**: Professional, complete, showcases skills - **Portfolio Project**: Demonstrates real-world DevOps practices - **Learning Project**: Covers microservices, Kafka, monitoring - **Interview Project**: Shows system design and architecture skills ## Value Proposition **EventStreamMonitor** demonstrates your ability to: 1. Build production-ready microservices 2. Implement real-time monitoring systems 3. Work with modern DevOps tools (Kafka, Redis, Docker) 4. Create scalable, event-driven architectures 5. Build monitoring and observability tools This is exactly what recruiters look for - practical, production-ready code that solves real problems! 

REDIS_SETUP.md

@@ -57,7 +57,7 @@ user = redis_helper.get_cached_user(123)
 
 ### 3. Service-Specific Helpers
 - `services/usermanagement/app/redis_helper.py` - User Management Redis helper
-- `services/booking/app/redis_helper.py` - Booking Redis helper
+- `services/taskprocessing/app/redis_helper.py` - Task Processing Redis helper
 
 ### 4. Dependencies
 - Added `redis` to `requirements.txt`

docker-compose.yml

@@ -34,7 +34,7 @@ services:
       retries: 5
 
   # Task Processing Service Database
-  booking-db:
+  taskprocessing-db:
     image: postgres:latest
     environment:
       - POSTGRES_USER=airlineradmin
@@ -43,7 +43,7 @@ services:
     ports:
       - 3306:5432
     volumes:
-      - booking-db-data:/var/lib/postgresql/data
+      - taskprocessing-db-data:/var/lib/postgresql/data
     healthcheck:
       test: ["CMD-SHELL", "pg_isready -U airlineradmin"]
       interval: 10s
@@ -140,22 +140,22 @@ services:
       retries: 3
 
   # Task Processing Service
-  booking-service:
+  taskprocessing-service:
     build:
       context: .
-      dockerfile: services/booking/Dockerfile
+      dockerfile: services/taskprocessing/Dockerfile
     ports:
       - 5002:9092
     depends_on:
-      - booking-db
+      - taskprocessing-db
       - kafka
       - redis
     environment:
       - FLASK_ENV=production
       - DEBUG=false
-      - BOOKING_SERVER_IPADDRESS=0.0.0.0
-      - BOOKING_SERVER_PORT=9092
-      - DATABASE_URL=postgresql://airlineradmin:testeventstreammonitor#123@booking-db:5432/TASK_PROCESSING
+      - TASKPROCESSING_SERVER_IPADDRESS=0.0.0.0
+      - TASKPROCESSING_SERVER_PORT=9092
+      - DATABASE_URL=postgresql://airlineradmin:testeventstreammonitor#123@taskprocessing-db:5432/TASK_PROCESSING
       - KAFKA_BOOTSTRAP_SERVERS=kafka:29092
       - REDIS_HOST=redis
       - REDIS_PORT=6379
@@ -223,6 +223,6 @@ services:
 volumes:
   registration-db-data:
   auth-db-data:
-  booking-db-data:
+  taskprocessing-db-data:
   notification-db-data:
   redis-data:

infrastructure/docker/docker-compose.yml

@@ -33,17 +33,17 @@ services:
       timeout: 5s
       retries: 5
 
-  # Flight Booking Service Database
-  booking-db:
+  # Task Processing Service Database
+  taskprocessing-db:
     image: postgres:latest
     environment:
       - POSTGRES_USER=airlineradmin
       - POSTGRES_PASSWORD=testeventstreammonitor#123
-      - POSTGRES_DB=FLIGHT_BOOKINGS
+      - POSTGRES_DB=TASK_PROCESSING
     ports:
       - 3306:5432
     volumes:
-      - booking-db-data:/var/lib/postgresql/data
+      - taskprocessing-db-data:/var/lib/postgresql/data
     healthcheck:
       test: ["CMD-SHELL", "pg_isready -U airlineradmin"]
       interval: 10s
@@ -118,22 +118,22 @@ services:
       timeout: 10s
       retries: 3
 
-  # Flight Booking Service
-  booking-service:
+  # Task Processing Service
+  taskprocessing-service:
     build:
       context: .
-      dockerfile: services/booking/Dockerfile
+      dockerfile: services/taskprocessing/Dockerfile
     ports:
       - 5002:9092
     depends_on:
-      - booking-db
+      - taskprocessing-db
       - kafka
     environment:
       - FLASK_ENV=production
       - DEBUG=false
-      - BOOKING_SERVER_IPADDRESS=0.0.0.0
-      - BOOKING_SERVER_PORT=9092
-      - DATABASE_URL=postgresql://airlineradmin:testeventstreammonitor#123@booking-db:5432/FLIGHT_BOOKINGS
+      - TASKPROCESSING_SERVER_IPADDRESS=0.0.0.0
+      - TASKPROCESSING_SERVER_PORT=9092
+      - DATABASE_URL=postgresql://airlineradmin:testeventstreammonitor#123@taskprocessing-db:5432/TASK_PROCESSING
       - KAFKA_BOOTSTRAP_SERVERS=kafka:29092
     healthcheck:
       test: ["CMD", "python", "-c", "import requests; requests.get('http://localhost:9092/health', timeout=5)"]
@@ -167,5 +167,5 @@ services:
 volumes:
   registration-db-data:
   auth-db-data:
-  booking-db-data:
+  taskprocessing-db-data:
   notification-db-data:

scripts/redis_dry_run.py

@@ -121,17 +121,20 @@ def test_user_management_helper():
         return False
 
 
-def test_booking_helper():
-    """Test Booking Redis helper"""
-    print("\n[TEST 5] Testing Booking Redis Helper...")
+def test_taskprocessing_helper():
+    """Test Task Processing Redis helper (BookingRedisHelper - legacy class name)"""
+    print("\n[TEST 5] Testing Task Processing Redis Helper...")
     try:
-        from services.booking.app.redis_helper import BookingRedisHelper
+        from services.taskprocessing.app.redis_helper import BookingRedisHelper
 
+        # Test import and instantiation
         helper = BookingRedisHelper()
-        print(" BookingRedisHelper imported successfully")
+        # Verify helper has the expected interface
+        assert hasattr(helper, 'redis_client'), "Helper missing redis_client"
+        print(" Task Processing Redis helper (BookingRedisHelper) imported successfully")
         return True
     except Exception as e:
-        print(f" Failed to import BookingRedisHelper: {e}")
+        print(f" Failed to import Task Processing Redis helper: {e}")
         return False
 
 
@@ -160,8 +163,8 @@ def main():
     # Test 4: User Management Helper
     results.append(("User Management Helper", test_user_management_helper()))
 
-    # Test 5: Booking Helper
-    results.append(("Booking Helper", test_booking_helper()))
+    # Test 5: Task Processing Helper (uses legacy BookingRedisHelper class)
+    results.append(("Task Processing Helper", test_taskprocessing_helper()))
 
     # Summary
     print("\n" + "=" * 60)

scripts/setup.sh

@@ -10,8 +10,8 @@ echo "Creating symlinks for common library..."
 if [ ! -L "services/usermanagement/common" ]; then
     ln -s ../../common services/usermanagement/common
 fi
-if [ ! -L "services/booking/common" ]; then
-    ln -s ../../common services/booking/common
+if [ ! -L "services/taskprocessing/common" ]; then
+    ln -s ../../common services/taskprocessing/common
 fi
 if [ ! -L "services/notification/common" ]; then
     ln -s ../../common services/notification/common