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System Design - Design Spotify Music Streaming Platform

Functional Requirements

  1. As a user, I should be able to search for songs based on the song name/artist name etc.
  2. As a user, I should be able to play the selected song.

Non-Functional Requirements

  1. Latency: Low latency is vital for immediate music playback after a user's selection, quick search results, and responsive user interface interactions.
  2. Scalability: Scalability ensures that as the number of users increases, the system can handle the additional load without performance degradation
  3. Availability: The users should be able to access music streaming, search functions, and other features without interruptions
  4. Robustness: Robustness includes the ability to deal with invalid user input, network issues, server failures, and unexpected behavior from client applications.

Capacity Requirements

  1. Assume that there are around 100 millions songs
  2. Assume that the size per file is around 5MB.
  3. Assume that the song metadata size is around 1KB.

This implies that there is a storage requirement of 500 TB to store the song data. Additionally, the storage requirement of the song metadata is around 1 GB.

High Level Design:



  • SpotifyWebServer: It acts as a BFF that performs authorization, rate limiting and other validations.
  • SongSearchService: It is used to return the query result for song search by user
  • Elasticsearch: To speed up the search results on song name/artist/lyrics or other metadata, an indexing service like Elasticsearch can be used. This creates an index of all the searchable content, allowing for quick retrieval of search results.
  • SongMetadataService: Service that has APIs for getting data from MetadataDB.
  • MetadataDB: System of record for the Songs Metadata
  • SongStreamingService: It is used to get the song audio file for streaming
  • ObjectStore: System of record for the audio filea
  • CDN: Content Delivery Network that caches songs for better Latency

Design Dive Deep:

Metadata Datastore

Approach 1: Using Relational Database

  • There is already a defined schema for the record stored that may rarely get updated.
  • The data can be normalized to store artist, producers, or other information in separate tables which would help reducing data redundancy.
  • As there is only a requirement to store 1GB of data, it can easily reside in a single database server.

Approach 2: Using NoSQL Database

  • We would have to store redundant data for a song's artist information or other relevant information that is common between multiple songs.
id: String
name: String
audioURL: String

Object Store

  • We can use s3 to store the songs
  • We can tiers songs such that less popular songs move to lower tier like S3 Standard-Infrequent Access Storage Class

How Song Streaming Works?

Approach 1: HTTP Range Requests

  • For Audio or Video streaming, we can store the file as a whole in blob storage and use HTTP Range Requests to get partial data.
  • In case the user has a slower network bandwidth, it can lead to buffering when the partial data is requested with a higher bitrate.

Approach 2: Adaptive Bitrate with HTTP Range Requests

  • Store multiple version of an audio file in the object store with varying bitrate.
  • The client application starts by fetching a default (lets say 320 kbps) bitrate version. This allows the app to download and play only the initial part of the audio file, reducing the initial load time.
  • Concurrently, the appl monitors the user's internet bandwidth. If it detects that the bandwidth is insufficient, it decides to switch to lower bitrate
  • This approach requires more manual control and coordination by the app.

Approach 3: HLS or DASH for Adaptive Bitrate Streaming

  • In standard Adaptive Streaming Protocols like HLS (HTTP Live Streaming) and DASH (Dynamic Adaptive Streaming over HTTP), the audio/video content is pre-segmented into short chunks, each available in different bitrates.
  • The media player reads a manifest file to adaptively select the appropriate segment based on the current network conditions.

NOTE: We can have a hybrid approach to store the manifest and chunks in CDN for popular songs and in S3 for less popular songs. When the client requests for a song to play, the backend service decides based on the popularity and other variables to return the URL of the manifest file from the CDN or S3 in response (only 1 manifest file for all bitrate chunks)

Elasticsearch Failover

  • Elasticsearch is not the system of record for the songs metadata. If there is a failure, we can rebuild the cache by using the data in metadata DB.
  • Additionally, any update to the Metadata DB will be reflected to the Elasticsearch cluster via an event trigerred on update.

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