System requirements

Functional:

1. File Operations:
2. Support basic file operations such as read, write, append, and delete.
3. Efficiently handle large file sizes to accommodate diverse data types and user needs.
4. Metadata Management:
5. Maintain metadata for each file, including permissions, timestamps, and file locations.
6. Ensure consistency of metadata across all replicas and nodes in the system.
7. Fault Tolerance:
8. Provide replication of data across multiple servers to ensure fault tolerance.
9. Detect and recover from server failures to maintain data availability and integrity.
10. Concurrency Control:
11. Support concurrent access to files while maintaining data consistency.
12. Implement distributed locking mechanisms to prevent conflicts between operations.
13. Security:
14. Ensure data integrity and confidentiality through encryption mechanisms.
15. Implement access control mechanisms to restrict unauthorized access to files and metadata.

Non-Functional:

1. Performance:
2. Ensure low latency for file operations and data access to meet user expectations.
3. Optimize throughput and response times to handle concurrent access from multiple clients.
4. Reliability:
5. Maintain high reliability by minimizing the risk of data loss or corruption.
6. Implement robust error handling and recovery mechanisms to mitigate system failures.
7. Scalability:
8. Design the system to scale horizontally to accommodate growing storage and user demands.
9. Ensure seamless integration with additional servers and resources to support increased workload.
10. Security:
11. Enforce strict access control measures to prevent unauthorized access to sensitive data.
12. Implement encryption and authentication mechanisms to protect data in transit and at rest.
13. Availability:
14. Ensure high availability of services through redundant components and failover mechanisms.
15. Implement proactive monitoring and alerting to detect and address potential issues promptly.
16. Manageability:
17. Provide tools and interfaces for efficient system monitoring, management, and troubleshooting.
18. Support automation for routine tasks such as backup, replication, and maintenance.
19. Data Consistency:
20. Guarantee strong consistency for file operations across distributed nodes to maintain data integrity.
21. Handle eventual consistency for certain types of data access to optimize performance.

Capacity estimation

File Size Distribution:

• The system will handle a mix of file sizes, including small, medium, and large files.
• Statistical analysis reveals an average file size of 1 GB with variations depending on user data.

Replication Factor:

• The chosen replication factor for fault tolerance and data redundancy is set to 3.
• This means that each file will have two additional replicas stored on separate servers.

Total Storage Requirements:

• Considering the average file size of 1 GB and a replication factor of 3:
• Total Storage = Average file size * Replication factor * Total number of files
• Total Storage = 1 GB * 3 * 10 million = 30 million GB
• Therefore, the estimated total storage capacity required for the Distributed File System is 30 million GB, equivalent to 30 PB (petabytes).

By factoring in the file size distribution, replication factor, and total number of files, we can estimate the storage capacity needed to support the system's requirements effectively.

API design

The customer will require a set of APIs to interact with the Distributed File System efficiently. These APIs should provide functionality for performing various file operations, managing metadata, accessing data, and ensuring system reliability and security. Here's a list of essential APIs:

1. File Operations API:
2. Read File: Allows customers to retrieve the contents of a file from the Distributed File System.
3. Write File: Enables customers to create or update files by writing data to the system.
4. Append to File: Provides the ability to append data to an existing file without overwriting its contents.
5. Delete File: Allows customers to delete files from the system when no longer needed.
6. Metadata Management API:
7. Get Metadata: Retrieves metadata information for a specific file, including permissions, timestamps, and replication details.
8. Update Metadata: Allows customers to update metadata attributes such as permissions and timestamps for files.
9. Data Access API:
10. List Files: Provides a list of files and directories available in the Distributed File System, allowing customers to navigate the file hierarchy.
11. Search Files: Enables customers to search for files based on specified criteria, such as file name or metadata attributes.
12. Download File: Allows customers to download files from the system to their local environment for offline access.
13. Replication and Fault Tolerance API:
14. Replicate Data: Provides functionality to replicate data across multiple servers to ensure fault tolerance and data redundancy.
15. Recover from Failures: Allows customers to recover data and restore system functionality in the event of server failures or data loss.
16. Concurrency Control API:
17. Acquire Lock: Enables customers to acquire locks on files to prevent concurrent access and maintain data consistency.
18. Release Lock: Allows customers to release locks on files once they have finished accessing or modifying them.
19. Security and Access Control API:
20. Authenticate User: Provides authentication mechanisms for verifying user identities and ensuring secure access to the system.
21. Authorize Access: Allows customers to authorize access to files based on user permissions and roles defined within the system.
22. Monitoring and Management API:
23. Monitor System Health: Provides APIs for monitoring system health, including server status, storage utilization, and performance metrics.
24. Manage Replication: Allows customers to manage replication settings and configurations to optimize system performance and reliability.

These APIs will empower customers to interact with the Distributed File System seamlessly, enabling them to perform essential file operations, manage metadata, ensure data integrity and reliability, and maintain system security effectively.

Database design

Choice of Database:

Customer, File, Metadata: Relational Database

Entity List:

• Customer: Represents customers interacting with the file system.
• File: Represents files stored in the system.
• Metadata: Represents metadata associated with each file

Database Type and Example: SQL database (e.g., PostgreSQL)

Reasoning for Choosing the Database:

• SQL Database (Customer, File, Metadata):
• Example: PostgreSQL
• Reasoning: SQL databases are suitable for storing structured data such as customer information and file metadata. They provide strong consistency and ACID properties, making them ideal for maintaining relational data integrity.

CAP Theorem Focus:

• SQL Database (Customer, File, Metadata): CP (Consistency and Partition Tolerance)
• SQL databases prioritize consistency and partition tolerance, ensuring that data remains consistent across distributed nodes while tolerating network partitions.

Data, Replica: Distributed file storage system

Entity List:

• Data: Represents the actual file data stored in the system.
• Replica: Represents replicas of file data for fault tolerance.

b. Database Type and Example: Distributed file storage system (e.g., Hadoop Distributed File System - HDFS)

c. Reasoning for Choosing the Database:

• Distributed File Storage System (Data, Replica):
• Example: Hadoop Distributed File System (HDFS)
• Reasoning: Distributed file storage systems are designed to handle large volumes of unstructured data efficiently. They offer fault tolerance, scalability, and high availability, making them well-suited for storing file data and replicas across distributed nodes.

CAP Theorem Focus:

• Distributed File Storage System (Data, Replica): AP (Availability and Partition Tolerance)
• Distributed file storage systems prioritize availability and partition tolerance, ensuring that data remains available for access even in the event of network partitions or node failures. Strong consistency may be sacrificed in favor of high availability and fault tolerance.

Data Partitioning

To efficiently partition the data in the Distributed File System, we can employ a combination of strategies tailored to the specific characteristics of each entity. Here's how we can partition the data for each entity along with the key columns used for partitioning:

• Customer Entity:
• Partitioning Strategy: Horizontal Partitioning based on Customer ID.
• Key Columns: Customer ID.
• File Entity:
• Partitioning Strategy: Horizontal Partitioning based on Customer ID for multi-tenancy support.
• Key Columns: Customer ID.
• Metadata Entity:
• Partitioning Strategy: Horizontal Partitioning based on File ID for efficient retrieval and management.
• Key Columns: File ID.
• Data Entity:
• Partitioning Strategy: Hash-based Partitioning for even distribution across nodes.
• Key Columns: File ID.
• Replica Entity:
• Partitioning Strategy: Hash-based Partitioning for even distribution across nodes.
• Key Columns: Data ID.

Geographical Partitioning:

Geographical partitioning may not be necessary for this system since it primarily operates in a distributed environment across multiple nodes. However, if the system spans across different geographical regions with specific performance requirements, geographical partitioning could be considered to optimize data access and latency.

By implementing these partitioning strategies and selecting appropriate key columns, we can efficiently distribute the data across nodes while ensuring scalability, performance, and fault tolerance in the Distributed File System.

Horizontal Scaling:

• Description: Add more nodes to the cluster to handle increasing data volumes and user load.
• Approach:
• Dynamically add new nodes to the system as the demand for storage and processing power grows.
• Distribute data and workload evenly across the expanded cluster to maintain performance and reliability.

Data Locality Optimization:

• Description: Optimize data access by storing data closer to where it is frequently accessed.
• Approach:
• Implement data locality techniques to minimize network latency and improve data access performance.
• Use caching mechanisms to store frequently accessed data locally on nodes, reducing the need to fetch data from remote locations.
• Distribute data replicas strategically across nodes to ensure redundancy while minimizing data transfer overhead.

High-level design

In the high-level design of the Distributed File System, several components are necessary to address the various aspects of file storage, metadata management, fault tolerance, scalability, and data access. Here are the key components needed to solve the problem from end to end:

1. Client Interface:

• Description: Responsible for interacting with users and applications to perform file operations.
• Functionality:
• Accepts file operation requests (read, write, delete) from users and applications.
• Translates file operations into commands understood by the Distributed File System.
• Handles authentication and authorization of users.

2. Metadata Service:

• Description: Manages metadata information associated with files stored in the system.
• Functionality:
• Stores metadata such as file permissions, timestamps, and file locations.
• Ensures consistency of metadata across replicas.
• Provides APIs for querying and updating metadata.

3. Data Storage:

• Description: Stores the actual file data distributed across multiple nodes.
• Functionality:
• Stores file data in a distributed manner across data nodes.
• Provides mechanisms for efficient data retrieval and storage.
• Implements fault tolerance mechanisms such as data replication and recovery.

4. Replication Manager:

• Description: Manages data replication across multiple nodes for fault tolerance.
• Functionality:
• Monitors the health of replicas and nodes in the system.
• Coordinates data replication to maintain data availability.
• Handles data recovery in case of node failures.

5. Namespace Node:

• Description: Manages the hierarchy of files and directories in the file system.
• Functionality:
• Maps file paths to actual data locations.
• Handles file path resolution and directory operations.
• Ensures consistency and integrity of the namespace.

6. Concurrency Control Manager:

• Description: Handles distributed locking mechanisms to support concurrent file access.
• Functionality:
• Implements distributed locking protocols to prevent conflicts between file operations.
• Coordinates access to shared resources to maintain data consistency.
• Manages concurrency and isolation levels for file operations.

7. Security Module:

• Description: Ensures data integrity, confidentiality, and access control.
• Functionality:
• Implements encryption mechanisms to secure data transmission and storage.
• Provides authentication and authorization mechanisms to control access to files and resources.
• Enforces security policies and regulations.

8. Load Balancer:

• Description: Distributes incoming requests across multiple nodes to ensure load balancing.
• Functionality:
• Routes requests to available nodes based on workload and resource availability.
• Monitors node health and performance to optimize request distribution.
• Prevents overloading of individual nodes and ensures scalability.

9. Monitoring and Logging:

• Description: Monitors system health, performance, and usage metrics.
• Functionality:
• Collects and analyzes system logs, metrics, and events.
• Provides real-time monitoring dashboards and alerts.
• Helps in diagnosing issues, optimizing performance, and planning capacity.

10. Admin Interface:

• Description: Provides administrative tools and interfaces for system management.
• Functionality:
• Allows administrators to configure system settings, manage users, and monitor system health.
• Provides tools for performance tuning, troubleshooting, and maintenance tasks.
• Ensures smooth operation and management of the Distributed File System.

Request flows

Explain how the request flows from end to end in your high level design. Also you could draw a sequence diagram using the diagramming tool to enhance your explanation...

Detailed component design

Dig deeper into 2-3 components and explain in detail how they work. For example, how well does each component scale? Any relevant algorithm or data structure you like to use for a component? Also you could draw a diagram using the diagramming tool to enhance your design...

Trade offs/Tech choices

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Failure scenarios/bottlenecks

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Future improvements

What are some future improvements you would make? How would you mitigate the failure scenario(s) you described above?