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users table:user_id (PK), name, email (unique), password_hash, phone, created_at, updated_at.user_preferences table:user_id (FK), language, preferred_genres, location.movies table:movie_id (PK), title, genre, language, duration, release_date, rating.theaters table:theater_id (PK), name, location, amenities.showtimes table:showtime_id (PK), theater_id (FK), movie_id (FK), date, time.bookings table:booking_id (PK), user_id (FK), showtime_id (FK), total_price, status, created_at.seats table:seat_id (PK), showtime_id (FK), seat_number, status (available/booked).payments table:payment_id (PK), booking_id (FK), user_id (FK), amount, payment_method, status, created_at.refunds table:refund_id (PK), payment_id (FK), amount, reason, processed_at.notifications table:notification_id (PK), user_id (FK), type (booking/payment), message, status (read/unread), created_at.promotions table:promotion_id (PK), code, discount_percent, valid_from, valid_to, max_uses.booking_metrics table:metric_id (PK), theater_id, movie_id, total_bookings, revenue, date.user_engagement table:engagement_id (PK), user_id, search_queries, tickets_booked, last_active.users table.movies and theaters tables, applying search filters.theaters and showtimes tables for the selected movie.seats table to mark selected seats as "reserved".payments table with transaction details and marks seats as "booked".bookings table.bookings and payments tables with cancelation and refund details.promotions table with usage count.movies, theaters, and showtimes tables in the Movie Database.The User Management Service is responsible for handling all user-related activities, including registration, login, and profile management. During registration, the service validates the user-provided data, hashes the password using a secure algorithm, and stores the user information in the database. When a user logs in, their credentials are validated against stored records, and a session token (e.g., JWT) is issued for secure subsequent interactions. For profile updates or retrievals, the service fetches user data from the database, updates the relevant fields if required, and ensures the changes are reflected in associated caches.
The system leverages a relational schema for user data:
users table: Stores basic user details (e.g., user_id, name, email, password_hash).user_preferences table: Stores optional preferences like genres, language, and preferred locations. For session management, the system uses JWT tokens, which are compact, secure, and easy to validate without frequent database access. In-memory caching (e.g., Redis) is employed for session data to reduce latency.To handle peak traffic, the service employs horizontal scaling, spinning up additional instances behind a load balancer. Frequently accessed data, such as user preferences or session tokens, are cached in Redis to minimize database reads. Rate limiting prevents abuse of registration or login endpoints, ensuring fair access to resources. Additionally, read replicas of the primary database are used to distribute read-heavy operations like profile retrieval.
The Movie and Theater Service fetches details about movies, theaters, and showtimes based on user queries. It integrates with the Search Service to provide fast lookups and filters results by user-provided criteria such as genre, language, or location. Theater owners and admins use this service to manage listings, add showtimes, or update movie details. Changes are synced with the Search Index for real-time updates.
The service employs a relational schema with:
movies table: Contains movie details such as title, genre, duration, and rating.theaters table: Stores theater-specific information, including name and location.showtimes table: Maps movies to theaters with attributes like time and date. To support searches, an inverted index is built using Elasticsearch, enabling fast text-based lookups and relevance-based ranking.The service handles surges by using horizontal scaling of application servers and optimizing database queries with proper indexing on fields like location, genre, and showtimes. Static assets like posters and trailers are delivered via a Content Delivery Network (CDN) to reduce server load. Background synchronization tasks between the database and search index are optimized with batch processing.
The Booking Service manages seat reservations, ensuring real-time availability and secure transaction handling. When a user selects seats, the service temporarily marks them as "reserved" in the database. Upon payment confirmation, the seats are marked as "booked". If the payment fails or the session times out, the reservation is released. The service interacts with the Payment Service to process transactions and updates booking records in the database.
The bookings table tracks transactions with attributes like user_id, showtime_id, and status. A seats table maps individual seats to showtimes and tracks their availability. The system uses row-level locking to ensure only one transaction modifies a seat’s status at a time. ACID-compliant transactions ensure atomicity across seat reservation and payment confirmation steps.
During high traffic periods, the service employs partitioning of the seats table by theater or showtime to reduce contention. Real-time seat availability is cached in Redis, allowing rapid lookups and reducing load on the primary database. Message queues (e.g., Kafka) are used to process non-critical updates asynchronously, ensuring timely user responses.
The Payment Service integrates with third-party gateways to process transactions securely. Upon receiving a payment request, it validates the user’s payment method and forwards the details to the gateway. Upon confirmation or failure, it updates the transaction status in the database. Refunds are processed similarly, with automated workflows for canceled bookings.
The payments table logs transaction details such as booking_id, amount, status, and payment_method. For reliable processing, the service uses transactional queues, ensuring payments are retried if failures occur due to transient errors. Data encryption algorithms protect sensitive payment information during transmission and storage.
To handle surges, the service supports multiple payment gateways, balancing load dynamically. Batch processing consolidates small transactions to reduce gateway overhead. The database is partitioned by user or date to ensure fast queries, and retry mechanisms handle transient failures efficiently.
The Notification Service ensures users are informed about booking confirmations, payment updates, and promotional offers. Notifications are queued and delivered via preferred channels such as SMS, email, or push notifications. The service also logs delivery statuses for future reference.
The notifications table logs user-specific messages with fields like user_id, message, and status. Priority queues ensure time-sensitive notifications, such as booking confirmations, are sent first. Redis Pub/Sub or similar mechanisms support real-time push notifications to connected clients.
The service dynamically scales worker pools to handle peaks in notification traffic. Multichannel delivery spreads the load across SMS, email, and push notification services. Notification templates are cached in memory for rapid retrieval and formatting.
Relational vs NoSQL for Core Data:
Redis for Real-Time Operations:
Elasticsearch for Search:
Multiple Payment Gateways:
Horizontal Scaling:
Database Overload:
Payment Gateway Downtime:
Double Booking:
Search Index Overload:
Notification Delays:
Real-Time Updates Failing:
Network Latency:
Fraudulent Activities:
Dynamic Scaling:
Multi-Region Deployment:
Advanced Caching:
Enhanced Fraud Detection:
Improved Indexing for Search:
Streamlined Notification System:
Data Pipeline for Analytics: