My Solution for Design a Distributed File System with Score: 8/10

by iridescent_luminous693

System requirements


Functional Requirements

  1. File Storage and Retrieval:
    • Users can upload, download, and delete files.
    • Support versioning and metadata for each file.
  2. Data Distribution and Replication:
    • Store files across multiple machines for scalability.
    • Replicate files across nodes to ensure fault tolerance.
  3. Concurrent Access:
    • Provide multi-client concurrent read and write access.
    • Ensure data consistency during concurrent operations.
  4. Fault Tolerance:
    • Automatically handle node failures and maintain data availability.
    • Support seamless recovery by replicating data.
  5. Scalability:
    • Handle increasing volumes of data and users.
    • Support horizontal scaling by adding new nodes dynamically.
  6. Access Control and Security:
    • Enforce role-based access control (RBAC) and encryption for file storage and transfer.

Non-Functional Requirements

  1. Reliability:
    • Ensure data durability and availability with replication and distributed consensus.
  2. Performance:
    • Low-latency file access and efficient replication mechanisms.
    • Optimize reads and writes with caching and partitioning.
  3. Consistency:
    • Ensure strong or eventual consistency for file operations depending on use cases.
  4. Availability:
    • Maintain high uptime, targeting 99.99% availability.
  5. Scalability:
    • Scale to handle petabytes of data and thousands of concurrent users.
  6. Durability:
    • Use replication and erasure coding to guarantee data durability over extended periods.





Capacity estimation

Estimate the scale of the system you are going to design...


Assumptions:

  1. Files and Metadata:
    • Total storage requirement: 10 PB.
    • Average file size: 10 MB.
    • Number of files: 10 PB÷10 MB=1 billion files10 \, \text{PB} \div 10 \, \text{MB} = 1 \, \text{billion files}10PB÷10MB=1billion files.
  2. Replication Factor:
    • Replication factor: 3 (each file is stored on 3 nodes).
    • Total storage with replication: 10 PB×3=30 PB10 \, \text{PB} \times 3 = 30 \, \text{PB}10PB×3=30PB.
  3. Throughput:
    • File uploads/downloads per day: 10 million.
    • Peak operations per second: 10,000.
  4. Node Estimation:
    • Node storage capacity: 10 TB.
    • Total nodes required: 30 PB÷10 TB=3,000 nodes30 \, \text{PB} \div 10 \, \text{TB} = 3,000 \, \text{nodes}30PB÷10TB=3,000nodes.




API design

Define what APIs are expected from the system...


1. File Management APIs

  • POST /api/files/upload: Upload a file to the system.
  • GET /api/files/download/{file_id}: Retrieve a file by its ID.
  • DELETE /api/files/delete/{file_id}: Delete a file by its ID.

2. Metadata Management APIs

  • GET /api/files/metadata/{file_id}: Fetch metadata for a file.
  • PUT /api/files/metadata/update/{file_id}: Update metadata for a file.

3. Access Control APIs

  • POST /api/access/grant: Grant access permissions to a user.
  • POST /api/access/revoke: Revoke access permissions for a user.

4. Node Management APIs

  • GET /api/nodes/status: Fetch the health and status of nodes.
  • POST /api/nodes/add: Add a new node to the cluster.
  • DELETE /api/nodes/remove/{node_id}: Remove a node from the cluster.

5. Monitoring APIs

  • GET /api/monitoring/usage: Fetch storage usage and performance metrics.
  • GET /api/monitoring/logs: Retrieve operational logs for the system.




Database design

Defining the system data model early on will clarify how data will flow among different components of the system. Also you could draw an ER diagram using the diagramming tool to enhance your design...


1. Metadata Database

  • Schema Details:
    • Table Name: FileMetadata
      • file_id (Primary Key): Unique identifier for each file.
      • file_name: Name of the file.
      • file_size: Size of the file in bytes.
      • replica_nodes: List of nodes storing replicas of the file.
      • created_at: Timestamp of file creation.
      • updated_at: Timestamp of the last metadata update.
  • Purpose:
    • Manage metadata for files, including replication details and storage locations.
  • Tech Used:
    • Relational Database (e.g., PostgreSQL) or NoSQL (e.g., DynamoDB).
  • Tradeoff:
    • Relational:
      • Pros: Ensures strong consistency for metadata operations.
      • Cons: Requires partitioning for large-scale metadata.
    • NoSQL:
      • Pros: High availability and scalability.
      • Cons: Limited support for complex queries.

2. Replication Log Database

  • Schema Details:
    • Table Name: ReplicationLog
      • log_id (Primary Key): Unique identifier for each log entry.
      • file_id (Foreign Key): Associated file ID.
      • node_id: Node responsible for replication.
      • status: Status of replication (e.g., pending, completed).
      • timestamp: Timestamp of the replication event.
  • Purpose:
    • Track the status of replication tasks and ensure fault tolerance.
  • Tech Used:
    • Time-Series Database (e.g., InfluxDB).
  • Tradeoff:
    • Pros: Optimized for sequential write operations.
    • Cons: Not ideal for complex relational queries.

3. Access Control Database

  • Schema Details:
    • Table Name: AccessControl
      • access_id (Primary Key): Unique identifier for access permissions.
      • file_id (Foreign Key): Associated file ID.
      • user_id: ID of the user granted access.
      • permissions: JSON field specifying permissions (e.g., read, write).
      • created_at: Timestamp of permission creation.
  • Purpose:
    • Manage access permissions for users and groups.
  • Tech Used:
    • Relational Database (e.g., MySQL).
  • Tradeoff:
    • Pros: Relational integrity and complex query support.
    • Cons: Requires indexing for large-scale access logs.

4. Monitoring Database

  • Schema Details:
    • Table Name: MonitoringLogs
      • log_id (Primary Key): Unique identifier for each log entry.
      • node_id: Node that generated the log.
      • event: Description of the event (e.g., failure, recovery).
      • timestamp: Timestamp of the event.
  • Purpose:
    • Store operational and performance logs for monitoring.
  • Tech Used:
    • NoSQL Database (e.g., MongoDB).
  • Tradeoff:
    • Pros: Scalable for high write throughput and large data volumes.
    • Cons: Requires periodic cleanup to manage storage costs.




High-level design

You should identify enough components that are needed to solve the actual problem from end to end. Also remember to draw a block diagram using the diagramming tool to augment your design. If you are unfamiliar with the tool, you can simply describe your design to the chat bot and ask it to generate a starter diagram for you to modify...



1. Client Interface

Overview:

Provides users and applications with an interface to interact with the file system. It supports file uploads, downloads, deletions, and metadata retrieval.

Responsibilities:

  • Send requests for file operations to the system.
  • Authenticate and validate user requests.
  • Handle errors and retries for client-side operations.

2. Metadata Service

Overview:

Manages metadata for files, including file locations, replication status, and versioning. It ensures consistency and provides details about where file data is stored.

Responsibilities:

  • Maintain a map of file IDs to storage nodes.
  • Track replication status and manage versioning.
  • Handle metadata updates for file operations (e.g., new uploads, deletions).

3. Storage Nodes

Overview:

Store file data and serve it during read operations. These nodes are responsible for managing data replication and ensuring durability.

Responsibilities:

  • Store file chunks and their replicas.
  • Perform data replication and recovery on failure.
  • Respond to read/write requests.

4. Replication Manager

Overview:

Ensures data redundancy by replicating file chunks across multiple storage nodes.

Responsibilities:

  • Enforce replication policies (e.g., replicate a file 3 times).
  • Monitor node health and redistribute data from failed nodes.
  • Optimize data placement for load balancing and locality.

5. Access Control and Security

Overview:

Handles authentication, authorization, and encryption for file system operations.

Responsibilities:

  • Enforce access policies based on user roles.
  • Provide secure file transfer using encryption protocols.
  • Log access requests for auditing.

6. Monitoring and Logging Service

Overview:

Tracks system health, usage patterns, and operational metrics. Provides alerts for failures or anomalies.

Responsibilities:

  • Monitor storage node health and resource usage.
  • Log file system operations for debugging and analytics.
  • Generate alerts for failures or performance issues.

7. Coordination Service

Overview:

Handles distributed consensus and locking mechanisms for consistency during concurrent operations.

Responsibilities:

  • Ensure consistent metadata updates using distributed locks.
  • Resolve conflicts during concurrent file operations.
  • Use consensus algorithms (e.g., Paxos, Raft) for distributed coordination.



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...


1. File Upload Request

Objective: Store a new file in the distributed system.

Steps:

  1. Client Interface:
    • Sends a POST /api/files/upload request with the file and metadata.
    • Splits the file into chunks if it exceeds a certain size.
  2. Metadata Service:
    • Assigns a unique file ID and generates chunk IDs for the file.
    • Determines storage nodes for each chunk based on load and location.
  3. Storage Nodes:
    • Receive file chunks and store them locally.
    • Send acknowledgments back to the Metadata Service.
  4. Replication Manager:
    • Replicates each chunk to additional storage nodes based on the replication factor.
    • Updates the Metadata Service with the replica locations.
  5. Metadata Service:
    • Updates the metadata database with file locations and replication details.
  6. Response:
    • Returns a success message with the file ID to the client.

2. File Download Request

Objective: Retrieve a file from the system.

Steps:

  1. Client Interface:
    • Sends a GET /api/files/download/{file_id} request.
  2. Metadata Service:
    • Looks up the file ID in the metadata database.
    • Fetches the list of chunks and their storage locations.
  3. Storage Nodes:
    • Retrieve the requested chunks and send them to the client.
    • Handle retries if a node is unavailable by fetching data from a replica.
  4. Client Interface:
    • Reassembles the file chunks into the original file.
  5. Response:
    • Sends the complete file to the client.

3. File Deletion Request

Objective: Remove a file from the system.

Steps:

  1. Client Interface:
    • Sends a DELETE /api/files/delete/{file_id} request.
  2. Metadata Service:
    • Validates the request and fetches the chunk locations for the file.
    • Marks the file as deleted in the metadata database.
  3. Storage Nodes:
    • Delete the file chunks from their local storage.
    • Notify the Metadata Service about the deletion status.
  4. Replication Manager:
    • Removes replicas associated with the file.
  5. Response:
    • Confirms the deletion to the client.

4. Metadata Retrieval

Objective: Fetch metadata for a file.

Steps:

  1. Client Interface:
    • Sends a GET /api/files/metadata/{file_id} request.
  2. Metadata Service:
    • Looks up the metadata database for the file ID.
    • Retrieves details like size, creation date, replication status, and storage locations.
  3. Response:
    • Returns the metadata to the client.

5. Node Failure Recovery

Objective: Recover data from a failed storage node.

Steps:

  1. Monitoring and Logging Service:
    • Detects node failure via heartbeat checks and alerts the Replication Manager.
  2. Replication Manager:
    • Identifies affected files by querying the Metadata Service.
    • Fetches replicas of the lost chunks from other nodes.
    • Redistributes these chunks to healthy nodes.
  3. Metadata Service:
    • Updates the chunk location details in the metadata database.
  4. Response:
    • Confirms recovery and restores redundancy.

6. Access Control

Objective: Grant or revoke access to a file.

Steps:

  1. Client Interface:
    • Sends a POST /api/access/grant or /revoke request with user and file details.
  2. Access Control and Security:
    • Validates the request and updates the access control database.
    • Logs the request for auditing.
  3. Response:
    • Confirms the change in access permissions.




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...


1. Client Interface

End-to-End Working:

The Client Interface is the entry point for users or applications to interact with the distributed file system. It provides functionalities like file uploads, downloads, deletions, and metadata retrieval. When a user requests an operation, the Client Interface validates the input, splits large files into chunks (if applicable), and communicates with backend services for processing. It also handles retries and error reporting.

Communication:

  • Protocols Used:
    • HTTP/HTTPS: Facilitates secure communication with backend services.
    • WebSockets: Supports real-time updates for operations like file uploads or status tracking.
  • Inter-Service Communication:
    • Sends requests to the Metadata Service for file location details.
    • Forwards data directly to Storage Nodes for uploads/downloads.

Data Structures and Algorithms:

  • Chunking Algorithm:
    • Splits files into fixed-size chunks (e.g., 64 MB) for distributed storage.
  • Retry Logic:
    • Implements exponential backoff for failed operations to handle transient issues.

Implementation Example (File Chunking):

python Copy code def split_file(file, chunk_size=64 * 1024 * 1024): chunks = [] with open(file, "rb") as f: while chunk := f.read(chunk_size): chunks.append(chunk) return chunks

Scaling for Peak Traffic:

  • Load Balancer:
    • Distributes incoming user requests across multiple instances of the Client Interface.
  • Rate Limiting:
    • Prevents abuse by capping the number of requests per user/IP.

Edge Cases:

  • Large File Uploads:
    • Handles uploads in chunks to prevent memory overload.
  • Network Failures:
    • Implements retries and allows resuming uploads using checkpoints.

2. Metadata Service

End-to-End Working:

The Metadata Service is the brain of the distributed file system, responsible for managing file metadata, chunk locations, and replication details. When a user uploads a file, the service generates a unique file ID, determines storage nodes for each chunk, and stores this information. For file retrievals, it provides the client with chunk locations.

Communication:

  • Protocols Used:
    • HTTP/HTTPS: Handles client requests for metadata operations.
    • gRPC: Communicates efficiently with the Replication Manager and Coordination Service.
  • Inter-Service Communication:
    • Sends chunk location details to the Client Interface during downloads.
    • Notifies the Replication Manager to initiate chunk replication.

Data Structures and Algorithms:

  • Distributed Hash Table (DHT):
    • Maps file IDs to metadata and ensures fast lookups.
  • Replication Tracking:
    • Uses a map to track chunk replication status across nodes.

Implementation Example (Metadata Storage):

python Copy code class MetadataStore: def __init__(self): self.metadata = {} def add_file(self, file_id, chunks): self.metadata[file_id] = chunks def get_file(self, file_id): return self.metadata.get(file_id, None)

Scaling for Peak Traffic:

  • Partitioning:
    • Partitions metadata by file ID to distribute load across multiple nodes.
  • Caching:
    • Caches frequently accessed metadata in Redis for faster lookups.

Edge Cases:

  • Metadata Corruption:
    • Uses checksums to validate metadata integrity.
  • Node Failures:
    • Ensures redundancy by replicating metadata across multiple nodes.

3. Storage Nodes

End-to-End Working:

Storage Nodes are responsible for storing file chunks and their replicas. They serve read and write requests from clients and replicate data as instructed by the Replication Manager. Each node maintains a local index of stored chunks and their metadata.

Communication:

  • Protocols Used:
    • HTTP/HTTPS: Receives data for uploads and serves file chunks for downloads.
    • gRPC: Communicates with the Replication Manager for replication tasks.
  • Inter-Service Communication:
    • Registers with the Metadata Service to indicate available storage capacity.
    • Reports health status to the Monitoring Service.

Data Structures and Algorithms:

  • Local Chunk Index:
    • Maintains a map of chunk IDs to file locations on disk.
  • Replication Algorithm:
    • Uses the chain replication model for fault tolerance.

Implementation Example (Chunk Storage):

python Copy code class StorageNode: def __init__(self): self.chunks = {} def store_chunk(self, chunk_id, data): self.chunks[chunk_id] = data def retrieve_chunk(self, chunk_id): return self.chunks.get(chunk_id)

Scaling for Peak Traffic:

  • Horizontal Scaling:
    • Adds new storage nodes dynamically as data volume grows.
  • Data Sharding:
    • Distributes file chunks across nodes to balance load.

Edge Cases:

  • Node Failures:
    • Replicates lost chunks to healthy nodes.
  • Disk Corruption:
    • Uses erasure coding for data reconstruction.

4. Replication Manager

End-to-End Working:

The Replication Manager ensures fault tolerance by replicating file chunks across multiple storage nodes. It monitors node health and redistributes data from failed nodes to maintain redundancy.

Communication:

  • Protocols Used:
    • gRPC: Communicates with Storage Nodes for replication tasks.
    • HTTP/HTTPS: Fetches metadata from the Metadata Service.
  • Inter-Service Communication:
    • Queries the Metadata Service for replication details.
    • Notifies the Monitoring Service of replication status.

Data Structures and Algorithms:

  • Replica Tracker:
    • Maintains a map of chunk IDs to their replica locations.
  • Replication Algorithm:
    • Uses consistent hashing to distribute replicas evenly.

Implementation Example (Replication Tracking):

python Copy code class ReplicationManager: def __init__(self): self.replica_map = {} def add_replica(self, chunk_id, node_id): self.replica_map.setdefault(chunk_id, []).append(node_id) def get_replicas(self, chunk_id): return self.replica_map.get(chunk_id, [])

Scaling for Peak Traffic:

  • Load Balancing:
    • Distributes replication tasks across multiple worker nodes.
  • Batch Replication:
    • Processes replication tasks in batches to optimize bandwidth usage.

Edge Cases:

  • Replication Delays:
    • Prioritizes critical files to maintain high availability.
  • Replica Overload:
    • Balances replica distribution to prevent hotspots.

5. Coordination Service

End-to-End Working:

The Coordination Service handles distributed locks and consensus for consistent metadata updates. It ensures that only one operation modifies a file’s metadata at a time and resolves conflicts during concurrent updates.

Communication:

  • Protocols Used:
    • gRPC: Handles lock requests from Metadata and Replication Managers.
  • Inter-Service Communication:
    • Notifies Metadata Service about lock status.
    • Sends consensus decisions to participating nodes.

Data Structures and Algorithms:

  • Lock Table:
    • Stores active locks for file operations.
  • Consensus Algorithm:
    • Uses Paxos or Raft to ensure consistent updates.

Implementation Example (Distributed Locking):

python Copy code class LockTable: def __init__(self): self.locks = {} def acquire_lock(self, file_id, node_id): if file_id not in self.locks: self.locks[file_id] = node_id return True return False def release_lock(self, file_id): self.locks.pop(file_id, None)

Scaling for Peak Traffic:

  • Shard Locks:
    • Partition lock tables by file ID to reduce contention.
  • Replicated Coordination Nodes:
    • Run multiple instances of the Coordination Service for fault tolerance.

Edge Cases:

  • Deadlocks:
    • Implements timeout-based lock releases to avoid deadlocks.
  • Network Partitions:
    • Uses quorum-based consensus to ensure consistency.




Trade offs/Tech choices

Explain any trade offs you have made and why you made certain tech choices...



Replication Factor of 3:

  • Trade-off: Increased storage overhead (3x).
  • Reason: Provides high availability and fault tolerance, ensuring data durability even if multiple nodes fail.

Distributed Hash Table (DHT):

  • Trade-off: Complexity in maintaining consistent hashing and rebalancing during node addition/removal.
  • Reason: Enables efficient metadata lookups and evenly distributes load across nodes.

Consistent Hashing for Replication:

  • Trade-off: Potential imbalance if node capacities vary.
  • Reason: Simplifies replication and ensures minimal data movement when scaling.

Eventual Consistency for Non-Critical Updates:

  • Trade-off: Temporary inconsistencies in metadata updates.
  • Reason: Balances performance and availability, especially for large-scale systems.



Failure scenarios/bottlenecks

Try to discuss as many failure scenarios/bottlenecks as possible.


Metadata Service Overload:

  • Issue: High traffic may cause delays in metadata lookups.
  • Mitigation: Use sharding and in-memory caching (e.g., Redis) for frequently accessed metadata.

Node Failures:

  • Issue: Data may become unavailable if a node storing chunks fails.
  • Mitigation: Use replication to maintain multiple copies and redistribute chunks from healthy replicas.

Replication Delays:

  • Issue: Replication tasks may fall behind during high load.
  • Mitigation: Prioritize replication for critical data and process tasks in parallel using worker pools.

Network Partitions:

  • Issue: Partitioned nodes may serve outdated or inconsistent data.
  • Mitigation: Use quorum-based reads and writes to ensure consistency.

Deadlocks in Distributed Locking:

  • Issue: Concurrent updates may cause deadlocks.
  • Mitigation: Implement timeouts for locks and detect cycles in lock dependencies.




Future improvements

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


Erasure Coding:

  • Improvement: Replace full replication with erasure coding for reduced storage overhead.
  • Mitigation: Ensure data reconstruction algorithms are efficient to minimize retrieval latency.

Predictive Scaling:

  • Improvement: Use machine learning to predict traffic spikes and proactively scale resources.
  • Mitigation: Avoid service degradation during high-traffic periods.

Global Data Distribution:

  • Improvement: Implement geo-replication to reduce latency for global users.
  • Mitigation: Use region-based partitions and ensure cross-region consistency with asynchronous replication.

Enhanced Monitoring and Self-Healing:

  • Improvement: Automate fault detection and recovery with real-time monitoring and AI-driven self-healing.
  • Mitigation: Minimize downtime and improve system reliability.

Improved Access Control:

  • Improvement: Introduce fine-grained access control with policies at the file and folder levels.
  • Mitigation: Prevent unauthorized access while maintaining performance