I built a site to instant-search 32 Million Songs in milliseconds
Songs Search with Typesense
I built a site to instant-search 32 Million Songs in milliseconds launched on November 6, 2020 with 16 votes, #912 of 3,461 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 5 years on.
In plain words
This is a music search tool that indexes 32 million songs and returns results in milliseconds. It lets users quickly find tracks across a massive database. The site was built to demonstrate fast search capabilities and is useful for anyone looking to discover or locate specific songs efficiently. It launched in November 2020.
written from the facts on this page · September 2026
From the sources
This search experience is powered by Typesense which is a blazing-fast, open source typo-tolerant search-engine. It is an open source alternative to Algolia and an easier-to-use alternative to ElasticSearch. The songs dataset is from MusicBrainz which is an open music encyclopedia that collects music metadata and makes it available to the public. Please contribute to it if you're able to! The app was built using the Typesense Adapter for InstantSearch.js and is hosted on DigitalOcean's App Platform . The search backend is powered by a geo-distributed 3-node Typesense cluster running on Typesense Cloud , with nodes in Oregon, Frankfurt and Mumbai. Here is the source-code that shows you how…from songs-search.typesense.org
Does a similar job
all alternatives →- ITI trained an AI model on 120M+ songs from iTunes2023 · maroofy.com · ▲753
Hey HN! I just shipped a project I’ve been working on called Maroofy: https://maroofy.com You can search for any song, and it’ll use the song’s audio to find other similar-sounding music. Demo: https://twitter.com/subby_tech/status/1621293770779287554 How does it work? I’ve indexed ~120M+ songs from the iTunes catalog with a custom AI audio model that I built for understanding music. My model analyzes raw music audio as input and produces embedding vectors as output. I then store the embedding vectors for all songs into a vector database, and use semantic…

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