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Estimate the scale of the system you are going to design...
Define what APIs are expected from the system...
/api/files/upload: Upload a file to the system./api/files/download/{file_id}: Retrieve a file by its ID./api/files/delete/{file_id}: Delete a file by its ID./api/files/metadata/{file_id}: Fetch metadata for a file./api/files/metadata/update/{file_id}: Update metadata for a file./api/access/grant: Grant access permissions to a user./api/access/revoke: Revoke access permissions for a user./api/nodes/status: Fetch the health and status of nodes./api/nodes/add: Add a new node to the cluster./api/nodes/remove/{node_id}: Remove a node from the cluster./api/monitoring/usage: Fetch storage usage and performance metrics./api/monitoring/logs: Retrieve operational logs for the system.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...
FileMetadatafile_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.ReplicationLoglog_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.AccessControlaccess_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.MonitoringLogslog_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.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...
Provides users and applications with an interface to interact with the file system. It supports file uploads, downloads, deletions, and metadata retrieval.
Manages metadata for files, including file locations, replication status, and versioning. It ensures consistency and provides details about where file data is stored.
Store file data and serve it during read operations. These nodes are responsible for managing data replication and ensuring durability.
Ensures data redundancy by replicating file chunks across multiple storage nodes.
Handles authentication, authorization, and encryption for file system operations.
Tracks system health, usage patterns, and operational metrics. Provides alerts for failures or anomalies.
Handles distributed consensus and locking mechanisms for consistency during concurrent operations.
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...
Steps:
POST /api/files/upload request with the file and metadata.Steps:
GET /api/files/download/{file_id} request.Steps:
DELETE /api/files/delete/{file_id} request.Steps:
GET /api/files/metadata/{file_id} request.Steps:
Steps:
POST /api/access/grant or /revoke request with user and file details.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...
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.
Implementation Example (File Chunking):
python
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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
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.
Implementation Example (Metadata Storage):
python
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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)
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.
Implementation Example (Chunk Storage):
python
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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)
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.
Implementation Example (Replication Tracking):
python
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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, [])
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.
Implementation Example (Distributed Locking):
python
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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)
Explain any trade offs you have made and why you made certain tech choices...
Replication Factor of 3:
Distributed Hash Table (DHT):
Consistent Hashing for Replication:
Eventual Consistency for Non-Critical Updates:
Try to discuss as many failure scenarios/bottlenecks as possible.
Metadata Service Overload:
Node Failures:
Replication Delays:
Network Partitions:
Deadlocks in Distributed Locking:
What are some future improvements you would make? How would you mitigate the failure scenario(s) you described above?
Erasure Coding:
Predictive Scaling:
Global Data Distribution:
Enhanced Monitoring and Self-Healing:
Improved Access Control: