System requirements

Functional Requirements

1. User Management

• Allow users to register, log in, and manage profiles.
• Enable users to view booking history and manage preferences.
• Support guest checkouts for non-registered users.

2. Movie Listings

• Display movies currently playing, upcoming releases, and showtimes.
• Allow users to search, filter, and sort movies by genre, language, ratings, and location.
• Provide detailed movie descriptions, trailers, and reviews.

3. Theater and Seat Selection

• Display theater details, including location, amenities, and showtimes.
• Show real-time seat availability and allow users to select preferred seats.
• Provide seating plans with different pricing tiers.

4. Ticket Booking

• Support booking multiple tickets in one transaction.
• Enable users to reserve tickets temporarily during the checkout process.
• Display ticket pricing, including discounts, taxes, and service fees.

5. Payment Processing

• Support multiple payment methods (e.g., credit/debit cards, digital wallets, UPI, net banking).
• Allow users to save payment details for future use securely.
• Handle failed payments with retries or alternate payment options.

6. Booking Confirmation

• Provide real-time booking confirmation with ticket details (e.g., QR codes, showtimes, seat numbers).
• Send booking confirmation via email, SMS, and in-app notifications.

7. Cancelation and Refunds

• Allow users to cancel bookings within allowed time frames.
• Process refunds for canceled bookings according to the platform’s policy.

8. Promotions and Offers

• Support promo codes, discounts, and loyalty programs.
• Allow integration with third-party vendors for special offers.

9. Notifications and Alerts

• Send notifications for upcoming shows, payment reminders, or booking updates.
• Inform users of special promotions and new releases.

10. Admin Features

• Enable theater owners to manage movie listings, seat availability, and pricing.
• Provide system administrators tools to manage users, bookings, and disputes.

Non-Functional Requirements

1. Performance

• Handle 1,000+ concurrent bookings during peak times.
• Ensure ticket availability is updated in real-time (<200ms latency).

2. Scalability

• Support millions of users and scale horizontally to handle increasing traffic.
• Design for region-specific scaling based on demand.

3. Reliability

• Ensure 99.9% uptime with failover mechanisms.
• Implement robust retry mechanisms for failed payment or booking transactions.

4. Security

• Use encryption for sensitive data (e.g., user credentials, payment details).
• Comply with standards like PCI-DSS for secure payment processing.
• Protect against bots and fraud with CAPTCHA and activity monitoring.

5. Usability

• Provide a user-friendly interface for both web and mobile platforms.
• Support multiple languages and regional customizations.

6. Availability

• Ensure 24/7 availability with maintenance windows scheduled during off-peak hours.
• Use a content delivery network (CDN) for faster loading of static assets.

7. Data Consistency

• Maintain strong consistency for seat availability and booking confirmation.
• Allow eventual consistency for less critical data like movie reviews.

8. Maintainability

• Use modular architecture for easier feature updates and bug fixes.
• Implement detailed logging and monitoring for debugging and analytics.

9. Extensibility

• Support integration with third-party platforms (e.g., payment gateways, ticket aggregators).
• Provide APIs for external developers to integrate additional features.

10. Analytics

• Collect and analyze data on user preferences, booking trends, and revenue.
• Provide dashboards for administrators to monitor system performance.

Capacity estimation

1. User Base

• Monthly Active Users (MAU): ~5 million users.
• Daily Active Users (DAU): ~500,000 users.
• Concurrent Users: Assume 5% of DAU are active simultaneously during peak hours → ~25,000 concurrent users.

2. Movies and Showtimes

• Movies: ~200 movies playing at any time across multiple regions.
• Theaters: ~2,000 theaters.
• Shows Per Theater: ~8 shows/day/theater.
• Seats Per Show: ~200 seats.
• Total Shows Per Day: ~16,000 shows (2,000 theaters * 8 shows).
• Total Seats Available Per Day: ~3.2 million seats.

3. Ticket Bookings

• Bookings Per Day: ~10% of DAU book tickets → 50,000 bookings/day.
• Seats Per Booking: ~2 seats/booking.
• Peak Bookings Per Hour: ~20% of daily bookings occur during peak hours → 10,000 bookings/hour.
• Peak Bookings Per Second (BPS): ~10,000 bookings/hour / 3600 seconds = ~3 BPS.

4. Search Requests

• Searches Per User: ~5 searches per user/day.
• Total Searches Per Day: ~500,000 users * 5 = ~2.5 million searches/day.
• Peak Search Requests Per Second (RPS): ~10% of searches occur during peak hours → 250,000 searches/hour = ~70 RPS.

5. Notifications

• Notifications Per Booking: ~3 notifications (booking confirmation, payment receipt, reminder).
• Total Notifications Per Day: ~50,000 bookings * 3 = ~150,000 notifications/day.
• Peak Notifications Per Second: ~20% during peak hours → 30,000 notifications/hour = ~8 notifications/second.

6. Payment Transactions

• Transactions Per Booking: 1 payment transaction per booking.
• Total Transactions Per Day: ~50,000 transactions/day.
• Peak Payment Transactions Per Second: ~10% during peak → ~5,000 transactions/hour = ~1.4 TPS.

7. Database Size

• User Data:
  • ~5 million users * 10 KB/user = ~50 GB.
• Movie Data:
  • ~200 movies * 50 KB/movie = ~10 MB.
• Theater Data:
  • ~2,000 theaters * 100 KB/theater = ~200 MB.
• Booking Data:
  • ~50,000 bookings/day * 1 KB/booking = ~50 MB/day → ~18 GB/year.
• Payment Data:
  • ~50,000 transactions/day * 1 KB/transaction = ~50 MB/day → ~18 GB/year.
• Total Yearly Storage: ~100 GB (users, theaters, bookings, payments).

8. Bandwidth Requirements

• Search Requests:
  • ~2.5 million searches/day * 2 KB/request = ~5 GB/day.
• Booking Requests:
  • ~50,000 bookings/day * 5 KB/request = ~250 MB/day.
• Notifications:
  • ~150,000 notifications/day * 1 KB = ~150 MB/day.
• Total Daily Bandwidth: ~5.5 GB/day.

9. Compute Requirements

• API Gateway:
  • Handle ~100 RPS during peak traffic.
• Search Service:
  • Serve ~70 RPS during peak.
• Booking Service:
  • Handle ~3 BPS during peak.

API design

1. User Management APIs

• POST /user/signup:
• Allows users to register by providing personal details like name, email, and password.
• POST /user/login:
• Authenticates a user and returns a token for session management.
• GET /user/profile:
• Fetches user profile details, including booking history and preferences.

2. Movie and Theater APIs

• GET /movies:
• Retrieves a list of movies currently playing or upcoming, filtered by location, language, and genre.
• GET /theaters:
• Provides a list of theaters showing a selected movie in a specific location.
• GET /movie/details/{movie_id}:
• Fetches detailed information about a movie, including trailers, synopsis, and reviews.

3. Booking and Seat APIs

• GET /theater/seats:
• Retrieves real-time seat availability for a specific showtime at a theater.
• POST /booking/create:
• Creates a booking request, temporarily locking selected seats.
• POST /booking/confirm:
• Confirms the booking and marks the selected seats as sold.
• POST /booking/cancel:
• Cancels a previously confirmed booking and processes refunds.

4. Payment APIs

• POST /payment/initialize:
• Initiates a payment for a booking and provides payment options.
• POST /payment/confirm:
• Confirms the payment after successful transaction processing.
• GET /payment/status/{payment_id}:
• Checks the status of a payment transaction.

5. Notification APIs

• POST /notifications/send:
• Sends notifications for booking confirmations, reminders, or promotions.
• GET /notifications:
• Retrieves all unread notifications for a user.

6. Promotions and Offers APIs

• GET /promotions:
• Retrieves active promotional offers and discount codes.
• POST /promotions/apply:
• Applies a discount or promotional code to the current booking.

7. Admin APIs

• POST /admin/movie/add:
• Adds a new movie listing to the platform.
• POST /admin/theater/add:
• Adds or updates theater details and showtimes.
• GET /admin/reports:
• Fetches system-wide reports on bookings, payments, and revenue.

Database design

1. User Database

• Database Schema Details:
  • users table:
    • Attributes: user_id (PK), name, email (unique), password_hash, phone, created_at, updated_at.
  • user_preferences table:
    • Attributes: user_id (FK), language, preferred_genres, location.
• Purpose:
  • Store user profiles, login credentials, and preferences.
• Tech Used:
  • PostgreSQL
• Reason:
  • Strong relational structure supports complex queries for user management and personalized recommendations.

2. Movie and Theater Database

• Database Schema Details:
  • movies table:
    • Attributes: movie_id (PK), title, genre, language, duration, release_date, rating.
  • theaters table:
    • Attributes: theater_id (PK), name, location, amenities.
  • showtimes table:
    • Attributes: showtime_id (PK), theater_id (FK), movie_id (FK), date, time.
• Purpose:
  • Manage movie listings, theater details, and showtimes.
• Tech Used:
  • MySQL
• Reason:
  • Optimized for structured data with frequent queries and updates.

3. Booking and Seat Database

• Database Schema Details:
  • bookings table:
    • Attributes: booking_id (PK), user_id (FK), showtime_id (FK), total_price, status, created_at.
  • seats table:
    • Attributes: seat_id (PK), showtime_id (FK), seat_number, status (available/booked).
• Purpose:
  • Track user bookings and manage real-time seat availability.
• Tech Used:
  • MySQL
• Reason:
  • Relational structure ensures consistency for seat status and booking confirmations.

4. Payment Database

• Database Schema Details:
  • payments table:
    • Attributes: payment_id (PK), booking_id (FK), user_id (FK), amount, payment_method, status, created_at.
  • refunds table:
    • Attributes: refund_id (PK), payment_id (FK), amount, reason, processed_at.
• Purpose:
  • Track payment transactions and refunds.
• Tech Used:
  • PostgreSQL
• Reason:
  • Strong ACID compliance ensures reliability for financial transactions.

5. Notification Database

• Database Schema Details:
  • notifications table:
    • Attributes: notification_id (PK), user_id (FK), type (booking/payment), message, status (read/unread), created_at.
• Purpose:
  • Manage and deliver notifications related to bookings and promotions.
• Tech Used:
  • Redis for real-time delivery, PostgreSQL for persistent storage.
• Reason:
  • Redis ensures low-latency delivery, while PostgreSQL provides durability for history.

6. Promotions and Offers Database

• Database Schema Details:
  • promotions table:
    • Attributes: promotion_id (PK), code, discount_percent, valid_from, valid_to, max_uses.
• Purpose:
  • Manage promotional offers and discount codes.
• Tech Used:
  • MySQL
• Reason:
  • Relational database handles structured promotional data efficiently.

7. Analytics Database

• Database Schema Details:
  • booking_metrics table:
    • Attributes: metric_id (PK), theater_id, movie_id, total_bookings, revenue, date.
  • user_engagement table:
    • Attributes: engagement_id (PK), user_id, search_queries, tickets_booked, last_active.
• Purpose:
  • Aggregate data for analytics and reporting.
• Tech Used:
  • Amazon Redshift or Google BigQuery
• Reason:
  • Optimized for OLAP queries and large-scale data aggregation.

High-level design

1. User Interface (UI)

• Overview:
  • The web and mobile apps allow users to interact with the platform.
• Features:
  • Search for movies, select seats, manage bookings, and view ticket details.
  • Supports multi-language and regional customization.

2. API Gateway

• Overview:
  • Acts as a single entry point for all client requests.
• Features:
  • Routes requests to backend services, handles authentication, rate limiting, and logging.

3. User Management Service

• Overview:
  • Handles user registration, login, profile management, and preferences.
• Features:
  • Manages user sessions with secure token-based authentication.

4. Movie and Theater Service

• Overview:
  • Manages movie details, theater information, and showtimes.
• Features:
  • Provides APIs for searching movies, viewing theater details, and fetching showtimes.

5. Booking Service

• Overview:
  • Handles ticket bookings, including seat selection and temporary reservation.
• Features:
  • Manages seat availability in real-time and supports booking updates or cancellations.

6. Payment Service

• Overview:
  • Handles payment processing, including integration with multiple gateways.
• Features:
  • Supports secure transactions, refunds, and payment status tracking.

7. Notification Service

• Overview:
  • Sends real-time notifications for booking confirmations, reminders, and promotions.
• Features:
  • Supports SMS, email, and in-app notifications.

8. Search and Recommendation Service

• Overview:
  • Provides search functionality for movies and theaters and personalized recommendations.
• Features:
  • Leverages user preferences and booking history for dynamic suggestions.

9. Promotions and Offers Service

• Overview:
  • Manages promotional codes and special offers.
• Features:
  • Validates and applies discounts to bookings.

10. Moderation and Review Service

• Overview:
  • Allows users to review movies and report inappropriate content.
• Features:
  • Moderates user-submitted content and ensures quality standards.

11. Analytics and Reporting Service

• Overview:
  • Aggregates and analyzes data for insights into user behavior, booking trends, and revenue.
• Features:
  • Provides dashboards for administrators and theater owners.

12. Admin Dashboard

• Overview:
  • Provides tools for administrators and theater owners to manage listings, view bookings, and monitor performance.
• Features:
  • Allows manual intervention for user support and dispute resolution.

13. Real-Time Availability Service

• Overview:
  • Ensures accurate seat availability updates during the booking process.
• Features:
  • Implements locking mechanisms to prevent double booking of seats.

14. Content Delivery Network (CDN)

• Overview:
  • Speeds up the delivery of static assets like images, movie trailers, and posters.
• Features:
  • Reduces latency for geographically distributed users.

15. Load Balancer

• Overview:
  • Distributes incoming traffic across backend services to maintain performance.
• Features:
  • Ensures fault tolerance and high availability during peak traffic.

16. Data Pipeline

• Overview:
  • Processes real-time and batch data for analytics and reporting.
• Features:
  • Streams data to the analytics database and machine learning models for future predictions.

17. Machine Learning Engine

• Overview:
  • Enhances recommendations and predicts demand for specific movies or theaters.
• Features:
  • Analyzes booking patterns and user behavior for better personalization.

Request flows

1. User Registration

1. User Interaction:
   • User provides name, email, password, and phone number.
2. API Gateway:
   • Forwards the request to the User Management Service.
3. User Management Service:
   • Validates input data, hashes the password, and stores user details in the User Database.
4. Database Interaction:
   • Inserts new user details into the users table.
5. Response:
   • Returns success confirmation with the user ID.

2. Movie Search

1. User Interaction:
   • User searches for movies by location, genre, or language.
2. API Gateway:
   • Routes the request to the Movie and Theater Service.
3. Movie and Theater Service:
   • Queries the Movie Database for matching movies.
4. Database Interaction:
   • Fetches data from the movies and theaters tables, applying search filters.
5. Response:
   • Sends a list of matching movies and their details to the user.

3. Fetch Theater and Showtimes

1. User Interaction:
   • User selects a movie to view available theaters and showtimes.
2. API Gateway:
   • Forwards the request to the Movie and Theater Service.
3. Movie and Theater Service:
   • Retrieves theater details and showtimes from the Theater Database.
4. Database Interaction:
   • Fetches data from the theaters and showtimes tables for the selected movie.
5. Response:
   • Returns a list of theaters with available showtimes.

4. Seat Selection and Booking

1. User Interaction:
   • User selects a theater, showtime, and seats to book.
2. API Gateway:
   • Routes the request to the Booking Service.
3. Booking Service:
   • Checks seat availability and temporarily reserves seats in the Seat Database.
4. Database Interaction:
   • Updates the seats table to mark selected seats as "reserved".
5. Response:
   • Confirms temporary reservation and proceeds to payment.

5. Payment Processing

1. User Interaction:
   • User initiates payment after selecting seats.
2. API Gateway:
   • Routes the request to the Payment Service.
3. Payment Service:
   • Validates payment details and communicates with a Payment Gateway.
4. Payment Gateway Interaction:
   • Processes the transaction and returns success or failure status.
5. Database Interaction:
   • Updates the payments table with transaction details and marks seats as "booked".
6. Response:
   • Confirms booking with ticket details (e.g., QR code, seat numbers).

6. Booking Confirmation

1. Notification Service:
   • Sends confirmation details via email, SMS, and in-app notifications.
2. Database Interaction:
   • Stores booking details in the bookings table.
3. Response:
   • Displays the booking confirmation in the user’s profile.

7. Cancelation and Refund

1. User Interaction:
   • User requests to cancel a booking.
2. API Gateway:
   • Routes the request to the Booking Service.
3. Booking Service:
   • Validates cancellation rules and updates the booking status to "canceled".
4. Payment Service:
   • Processes refund if applicable.
5. Database Interaction:
   • Updates the bookings and payments tables with cancelation and refund details.
6. Response:
   • Confirms cancellation and refund status to the user.

8. Promotional Offers

1. User Interaction:
   • User applies a promo code during checkout.
2. API Gateway:
   • Routes the request to the Promotions Service.
3. Promotions Service:
   • Validates the promo code and calculates the discount.
4. Database Interaction:
   • Updates the promotions table with usage count.
5. Response:
   • Confirms the discount and updates the total price.

9. Admin Movie Management

1. Admin Interaction:
   • Admin adds a new movie and showtimes.
2. API Gateway:
   • Forwards the request to the Admin Service.
3. Admin Service:
   • Updates the movies, theaters, and showtimes tables in the Movie Database.
4. Response:
   • Confirms the addition of the new movie and showtimes.

10. User Notifications

1. Trigger Event:
   • Booking confirmation or promotional offers are triggered.
2. Notification Service:
   • Fetches user preferences from the User Database.
3. Delivery:
   • Sends notifications via SMS, email, or push notifications.
4. Response:
   • Confirms delivery status and logs the notification.

Detailed component design

End-to-End Working

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.

Data Structures and Algorithms Used

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.

Scaling Strategies for Peak Traffic

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.

Edge Cases and Handling

1. Duplicate Registrations:
   • Handled by enforcing unique constraints on the email and phone fields in the database.
2. Forgotten Passwords:
   • The system provides secure recovery via email or SMS OTP verification, ensuring only authorized users can reset passwords.
3. Session Hijacking:
   • Short-lived tokens and device/IP validation prevent unauthorized session reuse.

2. Movie and Theater Service

End-to-End Working

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.

Data Structures and Algorithms Used

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.

Scaling Strategies for Peak Traffic

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.

Edge Cases and Handling

1. Movie Not Found:
   • Suggests similar movies based on genre or popular listings in the user’s region.
2. Overlapping Showtimes:
   • Validates admin inputs to prevent duplicate or conflicting entries for theaters.
3. Stale Data:
   • Periodic background jobs ensure the Search Index is updated with the latest changes.

3. Booking Service

End-to-End Working

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.

Data Structures and Algorithms Used

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.

Scaling Strategies for Peak Traffic

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.

Edge Cases and Handling

1. Double Booking:
   • Prevented using row-level locks during seat updates, ensuring atomic operations.
2. Payment Timeout:
   • Reserved seats are released automatically if the payment is not completed within a set time.
3. Cancellation:
   • The system immediately restores canceled seats to the pool and updates user records.

4. Payment Service

End-to-End Working

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.

Data Structures and Algorithms Used

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.

Scaling Strategies for Peak Traffic

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.

Edge Cases and Handling

1. Gateway Downtime:
   • The system switches to alternate gateways or queues transactions for later processing.
2. Fraudulent Transactions:
   • Machine learning models flag suspicious behavior, such as repeated failed payments.
3. Partial Payments:
   • Marks bookings as pending and prompts users to complete payment within a specified timeframe.

5. Notification Service

End-to-End Working

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.

Data Structures and Algorithms Used

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.

Scaling Strategies for Peak Traffic

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.

Edge Cases and Handling

1. Undelivered Notifications:
   • Implements retries with exponential backoff for failed deliveries.
2. Duplicate Notifications:
   • Maintains a deduplication layer to prevent repeated messages.
3. User Opt-Outs:
   • Respects user preferences by excluding unsubscribed channels.

Trade offs/Tech choices

Relational vs NoSQL for Core Data:

• Trade-Off: Chose relational databases (PostgreSQL, MySQL) for strict ACID compliance and complex queries, but they require additional scaling for high traffic.
• Reason: Ensures consistency for bookings and payments, which are critical to user trust.

Redis for Real-Time Operations:

• Trade-Off: Chose Redis for speed, but data persistence is limited.
• Reason: Ideal for caching seat availability and user sessions, reducing load on primary databases.

Elasticsearch for Search:

• Trade-Off: High resource consumption but delivers low-latency and relevance-based results.
• Reason: Essential for fast movie and theater searches with filtering and sorting.

Multiple Payment Gateways:

• Trade-Off: Increased integration complexity but ensures reliability during gateway downtimes.
• Reason: Critical for handling payment spikes and avoiding failed transactions.

Horizontal Scaling:

• Trade-Off: Requires robust load balancers and distributed architecture.
• Reason: Handles peak traffic efficiently without overloading individual servers.

Failure scenarios/bottlenecks

Database Overload:

• Issue: High traffic on the bookings or seat availability database.
• Mitigation: Use read replicas, caching, and table partitioning.

Payment Gateway Downtime:

• Issue: Failed transactions during gateway unavailability.
• Mitigation: Integrate multiple gateways and implement retry mechanisms.

Double Booking:

• Issue: Concurrent users booking the same seat.
• Mitigation: Enforce row-level locking and transaction isolation.

Search Index Overload:

• Issue: High query volume on Elasticsearch.
• Mitigation: Shard the index and cache frequent queries.

Notification Delays:

• Issue: Backlogged queues during peak traffic.
• Mitigation: Scale worker pools and prioritize time-sensitive notifications.

Real-Time Updates Failing:

• Issue: Delays in updating seat availability or movie schedules.
• Mitigation: Use message queues for eventual consistency.

Network Latency:

• Issue: Slower responses during regional traffic surges.
• Mitigation: Deploy regional CDNs and edge servers.

Fraudulent Activities:

• Issue: Fake bookings or payment fraud.
• Mitigation: Implement fraud detection models and monitoring.

Future improvements

Dynamic Scaling:

• Improvement: Implement auto-scaling for databases, API gateways, and worker pools.
• Mitigation: Addresses database overload and notification delays during peak traffic.

Multi-Region Deployment:

• Improvement: Deploy services and databases across regions for localized access.
• Mitigation: Reduces network latency and ensures availability during regional failures.

Advanced Caching:

• Improvement: Cache search results and seat availability more effectively.
• Mitigation: Alleviates load on search indexes and databases during surges.

Enhanced Fraud Detection:

• Improvement: Use machine learning models to detect and flag suspicious transactions.
• Mitigation: Reduces the risk of fraudulent bookings or payments.

Improved Indexing for Search:

• Improvement: Optimize Elasticsearch with dynamic sharding based on query patterns.
• Mitigation: Handles search index overload and improves query response times.

Streamlined Notification System:

• Improvement: Implement digest notifications for non-critical updates.
• Mitigation: Reduces notification backlog and improves delivery efficiency.

Data Pipeline for Analytics:

• Improvement: Build a real-time data pipeline for better monitoring and insights.
• Mitigation: Helps proactively detect bottlenecks and optimize resource usage.