FastAPI Backend for iOS Dating App
Client: Soulmate
A high-performance backend system engineered to power real-time messaging and intelligent matchmaking for thousands of concurrent users, de…
What we set out to build and why it mattered to Soulmate.
A high-performance backend system engineered to power real-time messaging and intelligent matchmaking for thousands of concurrent users, delivering instant communication alongside computationally intensive compatibility scoring–all within a scalable, asynchronous architecture.
Constraints, risks and the problem we had to solve first.
One of the core challenges was orchestrating three demanding layers simultaneously: instant WebSocket message delivery, heavy computational match scoring, and reliable database persistence–all without compromising performance or responsiveness. We solved this by designing an asynchronous architecture where computationally intensive operations are offloaded from the critical path. Beyond algorithmic performance, We tackled database scalability by optimizing PostgreSQL with composite indexes tailored to the system's most frequent and complex query patterns.
How we delivered.
- 01
We solved this by designing an asynchronous architecture where computationally intensive operations are offloaded from the critical path. Beyond algorithmic performance,
- 02
We tackled database scalability by optimizing PostgreSQL with composite indexes tailored to the system's most frequent and complex query patterns.
Languages, runtimes and platforms used to ship this engagement.
“Under the hood, the system leverages FastAPI with async endpoints, Socket.IO for persistent WebSocket connections, NumPy and Pandas for vectorized compatibility calculations, and PostgreSQL with composite indexing and connection pooling.”
Screens & deliverables.
The full story.
A high-performance backend system engineered to power real-time messaging and intelligent matchmaking for thousands of concurrent users, delivering instant communication alongside computationally intensive compatibility scoring–all within a scalable, asynchronous architecture.
One of the core challenges was orchestrating three demanding layers simultaneously: instant WebSocket message delivery, heavy computational match scoring, and reliable database persistence–all without compromising performance or responsiveness. We solved this by designing an asynchronous architecture where computationally intensive operations are offloaded from the critical path.
Beyond algorithmic performance, We tackled database scalability by optimizing PostgreSQL with composite indexes tailored to the system's most frequent and complex query patterns.
Under the hood, the system leverages FastAPI with async endpoints, Socket.IO for persistent WebSocket connections, NumPy and Pandas for vectorized compatibility calculations, and PostgreSQL with composite indexing and connection pooling.
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