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.

Database Choice

User Data:
Database Type: Relational Database (e.g., PostgreSQL, MySQL)
CAP Focus: Balanced (Consistency and Availability)
Reasoning: User data often requires a balance between consistency (ensuring accurate and up-to-date user information) and availability (ensuring users can access the system). Relational databases are designed to provide a balanced approach.
File Metadata and Sharing Data:
Database Type: Relational Database (e.g., PostgreSQL, MySQL)
CAP Focus: Balanced (Consistency and Availability)
Reasoning: Similar to user data, file metadata and sharing data benefit from a balanced approach to ensure that users see accurate and consistent information while still allowing for system availability.
Search Data:
Database Type: Search Engine (e.g., Elasticsearch)
CAP Focus: Availability
Reasoning: Search functionality benefits from a focus on availability, allowing users to retrieve search results quickly. Search engines like Elasticsearch are optimized for distributed and scalable search operations.
Audit Logs and Version History:
Database Type: Relational Database (e.g., PostgreSQL, MySQL) or NoSQL Database (e.g., MongoDB)
CAP Focus: Depends on the use case
Reasoning: Depending on the specific requirements, the focus may vary. For strict consistency, a relational database may be suitable. If flexibility and availability are prioritized, a NoSQL database could be preferred.
File Chunks:
Storage Service: Amazon S3 or similar object storage service
CAP Focus: Availability
Reasoning: Cloud-based object storage services are specifically designed for storing large volumes of binary data, offering high availability, durability, and scalability. They are optimized for read and write operations and provide low-latency access to stored objects.

Data Partitioning:

Strategy: Hash-Based Partitioning
Explanation: For file hosting services, hash-based partitioning is often a suitable strategy. It evenly distributes data across multiple partitions based on a hash function applied to a chosen key (e.g., user ID, file ID). This ensures a balanced distribution of data and efficient retrieval.

Regional or Geographical Partitioning:

Applicability: Not necessary initially, but consider for scalability and performance optimization.
Explanation: Initially, a global approach may be sufficient. However, as the user base grows and the service expands globally, you might consider regional or geographical partitioning. This can enhance performance by placing data closer to users and addressing data residency and compliance requirements.

Sharding Strategy:

Strategy: Range-Based Sharding
Explanation: Range-based sharding involves dividing the dataset into ranges based on a specific criteria (e.g., user IDs, file IDs). This can be effective for tables that are expected to grow significantly, such as file chunks or version history. Each shard can then handle a specific range of data, enabling horizontal scalability.

Sharding Key Selection:

Key Criteria: Choose a sharding key that evenly distributes data and avoids hotspots.
Explanation: The choice of sharding key is crucial. It should distribute the data evenly across shards to prevent hotspots. For example, sharding files based on a user's geographical location might lead to uneven distribution if certain regions have a higher concentration of users.

Replication:

Strategy: Master-Slave Replication
Explanation: Implement master-slave replication to ensure data durability and availability. Writes can be directed to the master node, while read queries can be distributed across slave nodes, enhancing both read and write scalability.

Load Balancing:

Load Balancer Type: DNS Load Balancing for Global Distribution
Explanation: Use DNS load balancing to distribute incoming requests across multiple servers globally. This ensures efficient load distribution and improved response times for users in different regions.

Submissions

Displaying Solutions with Scores Ranging from 1 to 10