System requirements Functional:

Here's a more polished version of the requirements:

1. View Theaters by Zipcode:

Users can search for and view theaters based on a specific zipcode.

1. Check Available Tickets: Users can see available tickets for various movies, including showtimes, in a selected theater for a specific date.
2. Select Available Seats:

Users can select available seats for a specific show on a chosen date in a theater.

1. Make Payment:

Users can proceed to payment for their selected seats.

1. Handle Concurrent Bookings:

The system ensures that concurrent bookings are managed without conflicts during seat selection and reservation.

1. Receive E-Tickets

After a successful booking, users receive e-tickets that include a QR code, screen information, and seat details.

1. Account Access and Guest Checkout

Users can log in to their accounts or choose to check out as a guest.

1. Search by Actor, Genre, and Language:

Users can search for movies based on actor, genre, and language preferences.

1. Complex Query Handling:

Advanced query capabilities to filter shows by date, time, genre, and other criteria using Elasticsearch or Solr.

1. Handling Last-Minute Changes:

Flexible cancellation and modification policy with real-time updates to seat availability.

1. Concurrency Management:

Implement a grace period for booking confirmations and develop conflict resolution strategies for high-traffic scenarios.

1. Search Functionality:

Sorting and filtering options for search results, with indexing strategies to speed up complex queries and support faceted search.

Non-Functional:

• Reliability: The system is always available.
• System should be consistent so duplicate bookings /conflict bookings are avoided.
• Scalability: System scales with growing number of users
• Resilient
• Secured payment.

 Performance Optimization

Capacity estimation

Let us say there are 10 Million DAU.

This service covers 1000 cities worldwide.

Each city has 4 theaters on average.

There are 50 Movies, 20 shows

There are 20 shows/day/theater

There are 150 seats/screen

Each theater plays on average 10 movies.

Each movie has 4 shows on average.

Overall, 100M movie tickets are sold monthly.

Avg TPS: 10M DAU /86400 =115.74 TPS

Peak TPS: 3* times

Data Volume:

For each booking, I will estimate it would take 256B to store, considering:

• IDs like user, theater, movie, show, ...
• timestamp
• seats
• Status

256 * 100M = 25.6GB / month

In two years, it would require 614.4GB.Considering some user growth and open capacity, let's say we will need 1TB in the main DB.

User Information Data Storage: 75 GB

Theater, Movies, Shows, Screens, Seats Data Storage: 0.25 GB

Search Index Data Storage: 1 MB

Transaction Data Storage: 300 GB

E-Ticket and QR Code Data Storage: 500 GB

API design

• 1.GET /theaterService/v1/theaters?zipcode={zipcode} response: { "theaters": []}
• 2. GET /theaterService/v1/theaters/{theaterId}/tickets?date={date}&movieId={movie}&showtime={time} response: { "seatingLayout": [ { "row": "A", "seats": [ { "seatNumber": "A1", "status": "available" }]]}
• 3. GET /movieService/v1/movies/search?actor=Tom%20Hanks&genre=Drama&language=English
• 4. POST /theaterService/v1/theaters/{theaterId}/shows/{showId}/book

{ "date": "2024-09-01", "seats": ["A1", "A2", "B3"], "userId": "user123", "paymentInfo": { "method": "credit_card", "cardNumber": "4111111111111111", "expirationDate": "12/25", "cvv": "123" } }

Response: {

"bookingId": "booking789",

"theaterId": "123",

"showId": "456",

"date": "2024-09-01",

"seats": ["A1", "A2", "B3"],

"totalPrice": 37.50,

"currency": "USD",

"status": "confirmed",

"eTicket": {

"qrCode": "qrcode-string",

"downloadLink": "https://theaterService.com/tickets/booking789/download"

}

Database design

the key entities are:

• Theaters: Information about theaters.
• Movies: Information about movies.
• Shows: Specific showtimes for movies in theaters.
• Seats: Seat availability and status for shows.
• Bookings: User bookings for seats.
• Users: Information about users (especially for payment processing).

Relational Database Schema Design

a. Theaters Table

CREATE TABLE Theaters (

    theaterId INT PRIMARY KEY,

    name VARCHAR(255),

    address VARCHAR(255),

    city VARCHAR(100),

    state VARCHAR(50),

    zipcode VARCHAR(10)

);

• Purpose: Stores information about theaters and allows querying by zipcode.

b. Movies Table

CREATE TABLE Movies (

    movieId INT PRIMARY KEY,

    title VARCHAR(255),

    genre VARCHAR(100),

    language VARCHAR(50),

    releaseDate DATE

);

• Purpose: Stores information about movies and supports searches by actor, genre, and language.

c. Actors Table

CREATE TABLE Actors (

    actorId INT PRIMARY KEY,

    name VARCHAR(255)

);

• Purpose: Stores actor information, supporting many-to-many relationships with movies.

d. MovieActors Table (Many-to-Many Relationship)

CREATE TABLE MovieActors (

    movieId INT,

    actorId INT,

    FOREIGN KEY (movieId) REFERENCES Movies(movieId),

    FOREIGN KEY (actorId) REFERENCES Actors(actorId),

    PRIMARY KEY (movieId, actorId)

);

• Purpose: Links actors to movies, enabling searches by actor.

e. Shows Table

CREATE TABLE Shows (

    showId INT PRIMARY KEY,

    theaterId INT,

    movieId INT,

    showtime TIME,

    date DATE,

    FOREIGN KEY (theaterId) REFERENCES Theaters(theaterId),

    FOREIGN KEY (movieId) REFERENCES Movies(movieId)

);

• Purpose: Stores specific showtimes for movies in theaters.

f. Seats Table

CREATE TABLE Seats (

    seatId INT PRIMARY KEY,

    showId INT,

    row VARCHAR(10),

    seatNumber VARCHAR(10),

    status VARCHAR(50),

    FOREIGN KEY (showId) REFERENCES Shows(showId)

);

• Purpose: Manages seat availability and status for each show.

g. Bookings Table

CREATE TABLE Bookings (

    bookingId INT PRIMARY KEY,

    userId VARCHAR(50),

    showId INT,

    date DATE,

    totalPrice DECIMAL(10, 2),

    currency VARCHAR(10),

    status VARCHAR(50),

    FOREIGN KEY (showId) REFERENCES Shows(showId)

);

• Purpose: Tracks user bookings, linking them to specific shows.

h. BookingSeats Table (Handles Multiple Seats per Booking)

CREATE TABLE BookingSeats (

    bookingId INT,

    seatId INT,

    FOREIGN KEY (bookingId) REFERENCES Bookings(bookingId),

    FOREIGN KEY (seatId) REFERENCES Seats(seatId),

    PRIMARY KEY (bookingId, seatId)

);

• Purpose: Associates multiple seats with a single booking.

Queries for API Endpoints

a. GET /theaterService/v1/theaters?zipcode={zipcode}

SELECT * FROM Theaters WHERE zipcode = '90210';

b. GET /theaterService/v1/theaters/{theaterId}/tickets?date={date}&movieId={movie}&showtime={time}

sql

SELECT row, seatNumber, status 

FROM Seats 

WHERE showId = (

    SELECT showId 

    FROM Shows 

    WHERE theaterId = {theaterId} AND movieId = {movieId} AND date = {date} AND showtime = {time}

) AND status = 'available';

c. GET /movieService/v1/movies/search?actor={actor}&genre={genre}&language={language}

sql

SELECT m.title, m.genre, m.language 

FROM Movies m

JOIN MovieActors ma ON m.movieId = ma.movieId

JOIN Actors a ON ma.actorId = a.actorId

WHERE a.name = 'Tom Hanks' AND m.genre = 'Drama' AND m.language = 'English';

d. POST /theaterService/v1/theaters/{theaterId}/shows/{showId}/book

• Step 1: Book seats

sql

BEGIN TRANSACTION;

INSERT INTO Bookings (userId, showId, date, totalPrice, currency, status)

VALUES ('user123', {showId}, '2024-09-01', 37.50, 'USD', 'confirmed');

INSERT INTO BookingSeats (bookingId, seatId)

VALUES (LAST_INSERT_ID(), (SELECT seatId FROM Seats WHERE showId = {showId} AND row = 'A' AND seatNumber = 'A1')),

       (LAST_INSERT_ID(), (SELECT seatId FROM Seats WHERE showId = {showId} AND row = 'A' AND seatNumber = 'A2'));

UPDATE Seats

SET status = 'booked'

WHERE showId = {showId} AND (row = 'A' AND seatNumber = 'A1') OR (row = 'A' AND seatNumber = 'A2');

COMMIT;

NoSQL Design Consideration

If you opt for a NoSQL approach (e.g., MongoDB), the schema can be more flexible and denormalized, which is useful for hierarchical data. However, the key trade-off is that it might require more storage space and could be more complex to maintain consistency.

Sharding, Partitioning, and Replication

a. Sharding Strategy

• Sharding Key for Theaters: zipcode to allow efficient retrieval of theaters based on location.
• Sharding Key for Movies: movieId to distribute movies across shards efficiently.
• Sharding Key for Shows and Seats: theaterId to ensure that all related data for a theater's shows and seats are stored together.

b. Partitioning

• Date-based Partitioning: Partition Shows and Seats tables by date to optimize queries for specific dates.

c. Replication

• Multi-Region Replication: Use synchronous replication for Bookings and Seats tables to ensure consistency and prevent double booking. Use asynchronous replication for Movies and Theaters to balance performance and availability.

Database Tradeoffs

• Normalization vs. Denormalization: SQL databases benefit from normalization to avoid data redundancy, while NoSQL databases may use denormalization to improve query performance at the cost of storage efficiency.
• Consistency vs. Availability: For booking operations, prioritize consistency (CP system). For reads like searching for movies, prioritize availability (AP system).
• Scalability: Sharding and partitioning strategies ensure the system can handle high traffic and large datasets.

High-level design

a. API Gateway

• Role: Entry point for all API requests.
• Functions:
  • Routing: Directs requests to the appropriate backend service.
  • Throttling: Implements rate limiting to protect the backend services.
  • Authentication & Authorization: Ensures only authenticated and authorized users can access certain endpoints.
  • Monitoring & Logging: Tracks and logs all incoming requests for auditing and analysis.

b. Theater Service

• Role: Manages data related to theaters, including their locations, movies being shown, and showtimes.
• Database: SQL (e.g., MySQL or PostgreSQL) with sharding based on zipcode.
• Caching: Uses Redis for frequently accessed queries like theaters by zipcode.

c. Movie Service

• Role: Manages information about movies, including actors, genres, and languages.
• Database: NoSQL (e.g., MongoDB) with indexing on actors, genres, and languages.
• Function: Supports complex queries for movie searches.

d. Seat Management Service

• Role: Tracks seat availability and handles concurrency during seat selection.
• Database: SQL with sharding based on theaterId and synchronous replication for strong consistency.
• Concurrency Control: Implements pessimistic or distributed locking to prevent double booking.

e. Booking Service

• Role: Manages the booking process, ensuring atomic transactions and interacting with the Payment Service.
• Database: SQL with sharding based on userId and synchronous replication for booking consistency.
• Atomicity: Ensures that seat reservation, payment processing, and booking confirmation happen as a single transaction.

f. Payment Service

• Role: Handles payment processing securely and interacts with third-party payment gateways.
• Security: PCI DSS compliant, ensuring that sensitive payment information is securely processed.

g. Notification Service

• Role: Sends booking confirmations and e-tickets via email or SMS.
• Integration: Generates QR codes for e-tickets and integrates with the Booking Service.

Caching Strategies

• Popular Movie Listings Cache:
  • What to Cache: Frequently accessed movie listings, especially popular movies, genre-based lists, or current top-rated movies.
  • Where to Cache: Use a distributed cache like Redis or Memcached to store the results of these queries.
  • Expiration: Set an appropriate time-to-live (TTL) for cached movie listings, such as 10 minutes, to ensure the cache remains fresh while reducing load on the Movie Service.
  • Update Strategy: The cache can be refreshed periodically or invalidated based on certain triggers, such as when new movies are added or ratings are updated.
• Available Seats Cache:
  • What to Cache: The current availability of seats for specific shows, especially for popular or near-term showtimes.
  • Where to Cache: Again, Redis or Memcached can be used to cache seat availability data.
  • Expiration: Shorter TTLs (e.g., 1-2 minutes) may be used for seat availability to ensure data is up-to-date, especially for high-demand shows.
  • Update Strategy: Cache can be updated or invalidated when a booking is confirmed or when a user initiates a seat selection, ensuring the availability data is accurate.

Payment ProcessingSuccessFailureMax Retries ReachedFallback SuccessFallback FailureInitial Payment AttemptBooking RequestConfirm BookingRetry LogicFallback to Secondary GatewayTransaction RollbackRelease Seats & Cancel BookingNotify User of FailureLog Failure for Manual ReviewUser ClientAPI GatewayBooking ServiceSeat Management ServicePayment GatewayRedis LockingSQL Database Seats TableBookings DatabaseRetry QueueAvailable Seats CachePopular Movies Cache

Request flows

a. User Searches for Theaters by Zipcode

• Flow:
  • Request sent to API Gateway.
  • API Gateway applies throttling and routes to Theater Service.
  • Theater Service queries SQL DB and returns results via API Gateway.

b. User Checks Available Tickets

• Flow:
  • Request sent to API Gateway.
  • API Gateway routes to Theater Service.
  • Theater Service queries Seat Management Service.
  • Seat Management Service checks Seats Table and returns availability.

c. User Searches for Movies

• Flow:
  • Request sent to API Gateway.
  • API Gateway routes to Movie Service.
  • Movie Service queries NoSQL DB and returns matching movies.

d. User Books Seats

• Flow:
  • Booking request sent to API Gateway.
  • API Gateway routes to Booking Service.
  • Booking Service locks seats via Seat Management Service.
  • Booking Service processes payment via Payment Service.
  • Booking Service confirms booking, updates seats, and releases locks.
  • Notification Service sends confirmation and e-ticket to the user.

Caching Flow Details:

• Request for Popular Movies:
  • The API Gateway checks the Popular Movies Cache (T).
  • If the data is cached, it returns the movie listings directly to the user.
  • If the data is not cached, it queries the Movie Service, retrieves the data, stores it in the cache, and then returns the data to the user.
• Request for Available Seats:
  • The Seat Management Service checks the Available Seats Cache (S).
  • If the seat data is cached, it returns the availability directly.
  • If the seat data is not cached or the cache has expired, the service queries the SQL database, updates the cache, and then returns the data.

Detailed component design

Let's dive deeper into two critical components of the system: Seat Management Service and 

1. Seat Management Service

Overview

The Seat Management Service is responsible for tracking seat availability, handling concurrent seat selections, and updating seat statuses during the booking process. This service ensures that seats cannot be double-booked, even under high traffic conditions.

Detailed Design

Key Responsibilities

• Track Seat Availability: Maintain the current status of each seat for a specific show (e.g., available, reserved, booked).
• Concurrency Control: Ensure that multiple users cannot book the same seat at the same time.
• Lock Management: Implement locking mechanisms to prevent race conditions during seat selection.

Core Components

• Seats Database: A SQL database (e.g., MySQL) with tables partitioned by theaterId and showId. Each seat has a unique identifier, row, seat number, and status.
• Locking Mechanism: Implemented using Redis with the Redlock algorithm for distributed locking.

CREATE TABLE Seats (

seatId INT PRIMARY KEY,

showId INT,

theaterId INT,

row VARCHAR(10),

seatNumber VARCHAR(10),

status ENUM('available', 'reserved', 'booked'),

version INT DEFAULT 0,

FOREIGN KEY (showId) REFERENCES Shows(showId),

INDEX (theaterId, showId, row, seatNumber)

);

Locking and Concurrency Control

• Distributed Locking with Redis:
  • When a user selects a seat, the Seat Management Service attempts to acquire a distributed lock using Redis. The lock is identified by a unique key, such as seat:{showId}:{row}:{seatNumber}.
  • The lock is time-limited (e.g., 30 seconds) to ensure it’s automatically released if the user doesn’t complete the booking in time.
  • If the lock is successfully acquired, the seat status is temporarily set to reserved.
• Optimistic Locking:
  • The version column in the Seats table is used to implement optimistic locking.
  • Before updating a seat's status to booked, the Seat Management Service checks the current version number. If the number has changed since the seat was reserved, the update is aborted, and the user must retry.

Scaling Considerations

• Horizontal Scalability: The Seat Management Service is stateless, allowing it to scale horizontally by adding more instances behind a load balancer.
• Redis Scalability: Redis can be deployed in a clustered mode to handle large volumes of locks. Partitioning Redis instances by theater or region can help distribute the load.
• Database Partitioning: The Seats table is partitioned by theaterId and showId, allowing the database to handle a high volume of concurrent reads and writes without contention.

Performance Optimization

• Caching: Frequently accessed seat data (e.g., for popular shows) can be cached in Redis to reduce the load on the database.
• Batch Operations: When multiple seats are selected, the Seat Management Service can batch database operations to minimize the number of transactions and reduce contention.

Booking Service. The Booking Service is responsible for processing seat reservations, handling payments, and finalizing bookings. This service ensures that the booking process is atomic, meaning that either all steps are completed successfully or none are.

Detailed Design

Key Responsibilities

• Atomic Booking Transactions: Ensure that seat reservation, payment processing, and booking confirmation are handled as a single, atomic transaction.
• Integration with Payment Gateway: Securely process payments using a third-party payment gateway.
• Concurrency Handling: Work closely with the Seat Management Service to handle concurrency during booking.

Core Components

• Bookings Database: A SQL database with tables partitioned by userId to distribute the load and allow efficient querying.
• Payment Gateway Integration: The service securely interacts with a payment gateway (e.g., Stripe, PayPal) to process payments.

CREATE TABLE Bookings (

bookingId INT PRIMARY KEY,

userId VARCHAR(50),

showId INT,

theaterId INT,

date DATE,

seats JSON,

totalPrice DECIMAL(10, 2),

currency VARCHAR(10),

status ENUM('pending', 'confirmed', 'failed'),

paymentStatus ENUM('pending', 'completed', 'failed'),

createdAt TIMESTAMP DEFAULT CURRENT_TIMESTAMP,

FOREIGN KEY (showId) REFERENCES Shows(showId),

INDEX (userId)

);

Booking Process Flow

• Seat Reservation:
  • The Booking Service first calls the Seat Management Service to reserve the selected seats. The seats are locked and marked as reserved.
• Payment Processing:
  • The Booking Service interacts with the Payment Gateway to process the payment.
  • If the payment is successful, the status in the Bookings table is updated to confirmed, and the seats are updated to booked in the Seats table.
• Confirmation:
  • The Booking Service updates the booking status in the database and sends a confirmation response to the user.
  • The Notification Service is triggered to send the booking confirmation and e-tickets

Scaling Considerations

• Horizontal Scalability: The Booking Service can scale horizontally by adding more instances behind a load balancer.
• Database Sharding: The Bookings table is partitioned by userId, allowing the system to distribute the load evenly across database shards.
• Async Payment Processing: Payment processing can be handled asynchronously, allowing the Booking Service to remain responsive even during high traffic.

Performance Optimization

• Bulk Operations: For group bookings, the Booking Service can handle multiple seats in a single transaction to minimize database contention.
• Retry Logic: If a booking fails due to a temporary issue (e.g., network failure during payment processing), the Booking Service can implement retry logic to attempt the booking again.

Notification service:

Asynchronous Task Handling:

• Email Notifications: When a booking is confirmed, the Booking Service can place a message in a queue (e.g., Kafka topic) indicating that an email notification should be sent. This decouples the notification process from the booking process, allowing the Booking Service to respond to the user more quickly.
• Payment Processing: For some non-critical payment operations (e.g., sending a receipt), the system can use a queue to handle these tasks asynchronously.

Queue Implementation:

• Message Producer: The Booking Service acts as a producer, placing messages onto the queue whenever a booking is confirmed or an action needs to be taken.
• Message Consumer: A separate Notification Service acts as a consumer, listening to the queue and processing messages (e.g., sending emails or updating external systems).

Payment Service:

Immediate Retry:

• Exponential Backoff: If a payment attempt fails, the system should implement a retry logic with exponential backoff. This means the system waits for progressively longer periods before attempting the payment again, to avoid overwhelming the payment gateway.
• Idempotency: Ensure that each payment request is idempotent. This is achieved by sending a unique transaction ID with each request. If the payment gateway receives the same transaction ID multiple times due to retries, it should process the transaction only once.

Fallback to Alternative Payment Gateway:

• Secondary Payment Provider: If the primary payment gateway fails after multiple retries, the system can attempt to process the payment through an alternative payment gateway. This requires pre-integration with a secondary provider and logic in the Booking Service to handle the switch.
• Seamless User Experience: Users should be unaware of the switch, ensuring a smooth experience even if the fallback mechanism is triggered.

Transaction Rollback:

• Partial Rollback: If the payment ultimately fails (after retries and fallback attempts), the Booking Service should roll back any partial transactions. This includes releasing any reserved seats back to available status and removing any pending bookings from the database.
• Compensating Transactions: In distributed systems where operations span multiple services, compensating transactions may be required to undo operations in other services (e.g., canceling seat reservations in the Seat Management Service).

User Notification:

• Error Handling: Notify the user immediately if the payment fails after retries and fallback attempts. The notification should be clear, explaining the issue and offering options (e.g., retry payment, choose another payment method).
• Queue for Follow-up: In case of failure, the system should log the failure and potentially place it in a queue for later manual review or retry.

Audit Logging:

• Transaction Logs: Every payment attempt, including retries and fallbacks, should be logged in a transaction log. This ensures traceability and allows for auditing in case of disputes or issues.
• Monitoring and Alerts: Integrate monitoring tools that trigger alerts if the failure rate for payment processing exceeds a certain threshold, enabling prompt investigation

Booking Cancellation Handling

Process Overview:

• Users may choose to cancel their booking after reserving seats but before completing the payment or finalizing the booking.
• The system needs to ensure that canceled bookings result in the immediate release of the reserved seats.

Implementation:

• Cancellation Request:
  • If a user initiates a cancellation request after selecting seats but before confirming the booking, the system should immediately process the cancellation.
  • The system should handle the cancellation as a high-priority operation to avoid keeping seats unnecessarily locked.
• Seat Status Update:
  • The system updates the seat status in the database from "reserved" back to "available" as soon as the cancellation is confirmed.
  • This action should be performed within a transaction to ensure that if any part of the cancellation fails, the system remains consistent.
• User Notification:
  • The system notifies the user that their booking has been canceled and that the reserved seats have been released.
  • The notification can include an option for the user to start a new booking or receive a confirmation of the cancellation.
• Grace Period Handling:
  • If the user cancels during the grace period (before the final confirmation), the system should immediately release the seats, ensuring they are available for other users.
  • This helps maintain high seat availability and prevents unnecessary holds.
• Audit Logging:
  • Similar to payment failure, the system should log the cancellation event and seat release for future reference and analysis.

Trade offs/Tech choices

Distributed Locking (Redis) vs. Database Locking

Technology Choice: Distributed Locking with Redis

Trade-offs:

• Performance: Distributed locking with Redis provides low-latency locks that are well-suited for high-throughput environments. However, managing distributed locks can be complex, especially in ensuring that locks are released properly in failure scenarios.
• Scalability: Redis is highly scalable and can handle large numbers of locks across a distributed system. In contrast, database-level locking is simpler to implement but can become a bottleneck as the system scales, particularly in high-concurrency environments.
• Failure Handling: Distributed locks require careful handling to avoid deadlocks or stale locks (e.g., using timeouts). Database locks are more straightforward but can lead to performance degradation under load.

Why Distributed Locking?

• Redis was chosen for distributed locking in the Seat Management Service to handle high concurrency during seat selection and booking. Its low-latency operations and scalability made it a better fit than traditional database locks, especially in a microservices environment.

SQL was chosen for Bookings and Seats because of the need for strong consistency, transactional integrity, and complex relational queries. NoSQL was selected for Movies and Theaters to handle large-scale, flexible data storage where schema flexibility and horizontal scalability are more critical.

Failure scenarios/bottlenecks

Handling Exhausted Retries:

• If all retries fail, the Booking Service stops retrying and either:
  • Queues the Booking: The failed booking is added to a queue for later processing. An alert is sent to an operator for manual intervention.
  • Notifies the User: The user is notified that the booking could not be completed due to a payment failure and is given options to retry manually or choose a different payment method.

The Seat Management Service is responsible for handling seat reservations and ensuring that no double bookings occur. If this service fails, users might be unable to check seat availability or complete bookings.

Bottlenecks

• Concurrency Issues: High contention for popular shows could lead to locking issues or increased latency in handling seat reservations.
• Lock Timeout: If a seat lock is held too long due to a failure, it could prevent other users from booking that seat.

Mitigation Strategies

• Distributed Locking: Use a highly available distributed locking mechanism (e.g., Redis with Redlock) to handle concurrency across multiple instances.
• Timeout Management: Implement short lock timeouts with automatic release to prevent stale locks from blocking seat availability.
• Graceful Degradation: If the Seat Management Service is under heavy load, consider temporarily limiting the number of simultaneous seat checks or reservations to maintain performance.

Booking Service Failure Scenario

The Booking Service is responsible for processing and confirming bookings. A failure here could lead to incomplete bookings, double charges, or a poor user experience.

Bottlenecks

• Atomicity Issues: If the service fails mid-transaction (e.g., after reserving seats but before processing payment), it could leave the system in an inconsistent state.
• Payment Gateway Integration: Dependency on external payment gateways introduces the risk of failures due to network issues or gateway unavailability.

Mitigation Strategies

• Transaction Management: Ensure that all booking operations are atomic using transactions. If a failure occurs, the transaction should roll back to maintain consistency.
• Retry Logic: Implement retry logic with exponential backoff for transient failures, particularly during payment processing.
• Circuit Breaker Pattern: Use the circuit breaker pattern to detect failures in the payment gateway and temporarily halt further requests until the service is restored.

Payment Gateway Failures

Scenario

The Payment Gateway is a critical external dependency. If it fails, users will not be able to complete payments, leading to abandoned bookings.

Bottlenecks

• Network Latency: High network latency or loss of connectivity can delay or prevent payments from being processed.
• Gateway Unavailability: The payment gateway might be temporarily unavailable due to maintenance or high load.

Mitigation Strategies

• Fallback Mechanism: If the primary payment gateway fails, consider routing payments through an alternative gateway.
• Timeouts and Retries: Implement timeouts for payment requests, and use retry logic to handle temporary failures.
• Payment Tokens: Use idempotent payment requests with unique transaction IDs to ensure that retries do not result in double charges.

Benefits of Caching:

• Reduced Latency: By serving data from the cache, the system can return responses much faster than querying the database every time.
• Lower Database Load: Caching reduces the frequency of database reads, freeing up resources for other operations and reducing the risk of bottlenecks.
• Improved Scalability: As the system scales to handle more users, caching helps to manage the load more effectively, allowing the system to handle more requests with the same underlying infrastructure.
• Microservices Resilience:
  • Improvement:
    • Circuit Breaker Pattern: Implement the Circuit Breaker pattern to handle service failures gracefully. If a service like the Seat Management Service becomes unresponsive, the circuit breaker stops sending requests to it and triggers fallback mechanisms, such as offering users alternative theaters or suggesting that they try again later.
    • Bulkhead Pattern: Apply the Bulkhead pattern to isolate different services, ensuring that a failure in one service (e.g., payment processing) does not affect the overall system's availability. By compartmentalizing services, you enhance the system's resilience and prevent cascading failures.
• Algorithm Enhancements:
  • Improvement:
    • Spatial Indexing for Seats: Introduce spatial indexing to optimize seat availability searches. Technologies like PostGIS, integrated with a SQL database, can be used to handle spatial data, enabling quicker and more efficient seat selection processes.
    • Efficient Query Algorithms: Leverage advanced algorithms to improve the speed and efficiency of checking available seats, especially for large theaters or during high traffic times.
• NoSQL Utilization:
  • Improvement:
    • Enhanced Query Capabilities: Use MongoDB or another NoSQL database to manage semi-structured data like tickets. Implement advanced query features such as filtering and searching over nested documents, enabling more flexible and performant data retrieval.
    • Optimized Data Structures: Design data models in NoSQL that optimize performance for high-read operations, such as retrieving available tickets or user preferences.

Future improvements

• Implement a Global CDN: Use a CDN (e.g., Cloudflare, AWS CloudFront) to cache and deliver static content from edge locations closer to users worldwide.
• Edge Computing: Consider pushing some processing (e.g., generating QR codes for e-tickets) to edge locations to further reduce latency.

Benefits:

• Faster load times for users globally.
• Reduced load on origin servers by offloading content delivery to the CDN.
• Improved user experience, particularly for international users.
• The system implements standard security practices, but with increasing cyber threats, additional measures may be required.

Improvement:

• Multi-Factor Authentication (MFA): Implement MFA for user accounts, especially for high-value transactions like booking tickets.
• End-to-End Encryption: Enhance data security by implementing end-to-end encryption for sensitive data, ensuring it remains encrypted both in transit and at rest.
• Security Audits and Penetration Testing: Regularly conduct security audits and penetration testing to identify and address vulnerabilities.