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set, get, delete, and update operations.Estimate the scale of the system you are going to design...
get 80%, set 15%, delete 5%).Define what APIs are expected from the system...
/api/set:{ key: string, value: string, ttl: int (optional) }.{ success: boolean }./api/get/{key}:{ key: string }.{ value: string, ttl: int (if applicable) }./api/delete/{key}:{ key: string }.{ success: boolean }./api/compare_and_set:{ key: string, old_value: string, new_value: string }.{ success: boolean }./api/nodes/add:/api/nodes/remove/{node_id}:/api/nodes/status:/api/metrics:/api/replication/status: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...
KeyValueStorekey (Primary Key): Unique identifier for each key.value: Associated value for the key.ttl: Time-to-live in seconds (optional).replica_nodes: List of nodes storing replicas.updated_at: Last modification timestamp.ReplicationLoglog_id (Primary Key): Unique identifier for the log entry.key: Key being replicated.source_node: Node from which the key is replicated.destination_node: Node to which the key is replicated.status: Replication status (e.g., in-progress, completed).timestamp: Timestamp of the replication event.ClusterMetadatanode_id (Primary Key): Unique identifier for each node.capacity: Storage capacity of the node.available_space: Remaining space on the node.health_status: Health of the node (e.g., healthy, degraded).updated_at: Last status update timestamp.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...
Acts as the entry point for all client requests, handling routing, authentication, rate limiting, and load balancing. It ensures secure and efficient communication with backend services.
Determines which node stores a given key and routes requests to the correct node. It implements consistent hashing or range-based partitioning for efficient data distribution.
Store the actual key-value pairs and serve get, set, and delete operations. Each node is responsible for a specific subset of the key space.
Manages data replication across nodes to ensure fault tolerance and availability. It tracks the replication status and re-replicates data from failed nodes to healthy ones.
Provides durable storage for key-value pairs, ensuring no data loss even after node failures or crashes. It uses mechanisms like write-ahead logging (WAL) or snapshots.
Tracks system health, performance, and usage metrics. It detects failures and triggers alerts or recovery actions when anomalies are detected.
Handles cluster-wide operations like adding/removing nodes, rebalancing partitions, and managing configuration changes.
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/set request with the key-value pair.Steps:
GET /api/get/{key} request.Steps:
DELETE /api/delete/{key} request.Steps:
POST /api/compare_and_set request with the key, old value, and new value.Steps:
Steps:
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...
This service determines which storage node is responsible for a given key using partitioning algorithms like consistent hashing. When a key is provided (e.g., for get or set operations), it calculates the hash, finds the appropriate partition, and routes the request to the responsible node.
Implementation Example (Consistent Hashing):
python
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class ConsistentHashing:
def __init__(self, replicas=3):
self.replicas = replicas
self.ring = SortedDict()
def add_node(self, node):
for i in range(self.replicas):
key = hash(f"{node}-{i}")
self.ring[key] = node
def get_node(self, key):
if not self.ring:
return None
hashed_key = hash(key)
node_index = self.ring.bisect_left(hashed_key) % len(self.ring)
return self.ring.peekitem(node_index)[1]
Storage nodes are responsible for storing key-value pairs and ensuring data durability. For set requests, the data is stored in memory and logged in a write-ahead log (WAL) for persistence. For get requests, the node retrieves the value from memory or disk.
Implementation Example (In-Memory Storage with WAL):
python
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class StorageNode:
def init(self):
self.store = {}
self.wal = open("wal.log", "a+")
def set(self, key, value):
self.store[key] = value
self.wal.write(f"SET {key} {value}\n")
def get(self, key):
return self.store.get(key)
The Replication Manager ensures data redundancy by replicating key-value pairs to secondary nodes. It monitors the replication factor and re-replicates data during node failures.
Implementation Example (Replication Tracker):
python
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class ReplicationManager:
def init(self):
self.replica_map = {}
def replicate(self, key, nodes):
self.replica_map[key] = nodes
for node in nodes:
node.set(key, self.store[key])
The Cluster Manager handles cluster-wide operations like adding/removing nodes and rebalancing partitions. It ensures a consistent view of the cluster state and coordinates updates with other services.
Explain any trade offs you have made and why you made certain tech choices...
Consistent Hashing vs. Range-Based Partitioning:
Replication Factor of 3:
In-Memory vs. Persistent Storage:
Eventual Consistency vs. Strong Consistency:
Try to discuss as many failure scenarios/bottlenecks as possible.
Node Failures:
Hot Partitions:
Replication Delays:
Network Partitions:
Storage Limitations:
What are some future improvements you would make? How would you mitigate the failure scenario(s) you described above?
Dynamic Scaling:
Erasure Coding:
Geo-Replication:
Advanced Conflict Resolution:
Improved Monitoring and Self-Healing: