Database design

For the tables required in this design, refer to the class diagram, the list of classes is not exhaustive but this is a good number of tables to start with.

Relational Database for User, Booking, and Payment Data:
Reasoning: User data and booking information require strong consistency and relational structure to maintain data integrity and enforce ACID properties.
Database Type: Relational Database (e.g., PostgreSQL, MySQL).
CAP Theorem Focus: Consistency is prioritized to ensure accurate and reliable user information and booking details.
NoSQL Database for Event, Location, Reviews, and Comments Data:
Reasoning: Event details and location information may vary and evolve, requiring a flexible schema and scalability, making NoSQL databases suitable.
Database Type: NoSQL Database (e.g., MongoDB, Cassandra).
CAP Theorem Focus: Partition tolerance and availability are crucial to handle potential changes in data structure and support scalable operations.
Document Store for Search Data:
Reasoning: Search data benefits from a document-oriented storage approach, enabling fast and efficient retrieval of movie and event-related information.
Database Type: Document Store (e.g., Elasticsearch).
CAP Theorem Focus: Availability is emphasized for quick and responsive search capabilities.
Media Database for Media Files:
Reasoning: Multimedia files, such as movie trailers, require efficient storage and retrieval, making media databases suitable for this purpose.
Database Type: Media Database (e.g., Amazon S3, MongoDB with GridFS).
CAP Theorem Focus: Emphasis on Availability and Partition Tolerance to ensure media files are accessible and retrievable.
Key-Value Store for Metadata and Quick Lookups of popular events:
Reasoning: Metadata and quick lookups can benefit from a key-value store for simplicity and fast access to specific information.
Database Type: Key-Value Store (e.g., Redis).
CAP Theorem Focus: Emphasis on Availability and quick lookups, while still maintaining Partition Tolerance.

Data Partitioning Strategy:

Best Strategy: Hash-Based Partitioning.
Reasoning: Hash-based partitioning ensures even distribution of data and avoids hotspots, which is essential for a system dealing with various entities like users, events, and bookings. It provides a balanced approach to data distribution across partitions.

Regional or Geographical Partitioning:

Applicability: Consider regional or geographical partitioning for tables that have location-based data, such as Events or Locations.
Reasoning: Regional partitioning can enhance performance by allowing the system to focus on a specific geographical region when serving requests related to events, locations, or cinemas. This is especially useful for platforms that have a global presence.

Partitioning Adjustments:

Applicability: Required as specific tables grow disproportionately.
Reasoning: Periodically reassess the partitioning strategy, especially for tables like Bookings or Events, and adjust as needed to maintain optimal data distribution and prevent hotspots.

Sharding Strategy:

Best Strategy: Range-Based Sharding for Events or Bookings, Hash-Based Sharding for Users or Reviews.
Reasoning:
Range-Based Sharding: Suitable for tables where data can be logically grouped, such as Events based on date ranges or Bookings based on booking IDs.
Hash-Based Sharding: Ideal for tables where uniform distribution of data is crucial, preventing hotspots. For example, Users or Reviews tables can benefit from hash-based sharding.

Scaling Strategy

Horizontal Scaling:
Applicability: Essential as the system grows and experiences increased demand.
Reasoning: With a large user base and varying levels of activity, horizontal scaling by adding more database nodes or servers helps distribute the load and handle increased concurrency effectively.

Read/Write Separation:
Applicability: Suitable when there is a clear distinction between read and write workloads.
Reasoning: By directing read operations to read replicas and write operations to the primary database, the system can efficiently handle more concurrent users and improve overall responsiveness.

Submissions

Displaying Solutions with Scores Ranging from 1 to 10

Submissions

Displaying Solutions with Scores Ranging from 1 to 10