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Return the top k most played songs in the last time window (1 day in this case). The time window is optional and defaults to all time if not specified. The response is a list of items with their IDs and play counts.

{
  items: [
    { id: string, count: number }
  ]
}


Round-robin partitioning distributes events evenly across partitions.

Round-Robin

Fixed Hash

[Consistent Hashing](/fundamentals/consistent-hashing) is used to dynamically adjust the number of partitions.


Dynamic Hash

Partitioning Schemes

Design a system to find the top k heavy hitters in a stream of data. This could be
- the top k most played songs in a music streaming service like Spotify
- the top k most viewed videos on a video streaming service like YouTube
- the top k most bought items in an e-commerce service like Amazon
- the top k most frequent items in a social media service like Twitter
- the top k most accessed IP addresses on a network


Approximation Techniques

Count-Min Sketch

Trade-offs

Data Structure

Algorithm to Maintain Top-K

Global Window Top K, Low QPS

Scaling Strategies

Global Window Top K, High QPS

Sliding Window Top K, High QPS

Latency

Accuracy

Storage

Design Spotify Top K Songs, Top K Heavy Hitters | System Design Interview Question

Design Spotify Top K Songs

Non-Functional Requirements

API (optional)

The system should allow users to view the top k most played songs of all time. The traffic is limited so that a single instance can handle it.


High Level Design

The system should allow users to view the top k most played songs of all time. The traffic is high so that we need to scale out the system.


The system should allow users to view the top k most played songs in the last X time in a sliding window.
