Concurrency and Async / Await

JustAPI supports both regular functions (def) and asynchronous functions (async def) as request handlers. This page explains the difference and when to use each.

Synchronous vs Asynchronous

from justapi import JustAPIApp

app = JustAPIApp()

# Synchronous handler (def)
@app.get("/sync")
def sync_handler(request):
    return {"message": "Synchronous"}

# Asynchronous handler (async def)
@app.get("/async")
async def async_handler(request):
    return {"message": "Asynchronous"}

Both work identically from the user’s perspective. The difference is internal.

`def` — Synchronous

JustAPI runs synchronous handlers in a dedicated thread pool so they don’t block the Rust event loop. This is handled automatically by the framework.

Use def when your handler does CPU-bound work or calls blocking libraries:

import time

@app.get("/cpu-work")
def cpu_work(request):
    # Simulate CPU-bound work
    result = sum(i * i for i in range(10_000))
    return {"result": result}

`async def` — Asynchronous

Async handlers run directly on the Rust async runtime. They can await other async operations without blocking.

Use async def when your handler does I/O-bound work (network calls, database queries, file I/O):

import httpx

@app.get("/fetch")
async def fetch_data(request):
    async with httpx.AsyncClient() as client:
        response = await client.get("https://api.example.com/data")
        return response.json()

When to Use Which

Scenario	Use	Why
Calling a database ORM (sqlx, SQLAlchemy async)	`async def`	Avoids blocking the runtime
HTTP calls to external APIs	`async def`	Non-blocking I/O
Simple data transformation	`def`	Cleaner code, no async overhead
CPU-bound computation	`def`	Thread pool handles it
Mixed I/O + computation	`async def`	Better concurrency
Using blocking libraries	`def`	Thread pool prevents blocking

JustAPI’s Concurrency Model

JustAPI uses a Rust async runtime (tokio) for all request handling. The Python handler boundary is designed to avoid GIL contention:

async def handlers run on the tokio async runtime directly.
def handlers run in a dedicated Python worker thread pool (GIL pool).
The native fast path (native=True) bypasses Python entirely — no GIL, no threads.

This means you get true concurrency for async handlers even on standard CPython, because the GIL is released during Python handler execution.

Async Database Access

import sqlite3
from justapi import JustAPIApp

app = JustAPIApp()

@app.get("/users")
async def list_users(request):
    # Use an async database client
    import aiosqlite
    async with aiosqlite.connect("users.db") as db:
        async with db.execute("SELECT * FROM users") as cursor:
            users = await cursor.fetchall()
            return {"users": users}

Parallel Async Operations

Async handlers can run multiple I/O operations concurrently:

import httpx

@app.get("/aggregate")
async def aggregate(request):
    async with httpx.AsyncClient() as client:
        # Run both requests concurrently
        users_task = client.get("https://api.example.com/users")
        items_task = client.get("https://api.example.com/items")

        users_response = await users_task
        items_response = await items_task

        return {
            "users": users_response.json(),
            "items": items_response.json(),
        }

Summary

Use def for synchronous, blocking, or CPU-bound work.
Use async def for I/O-bound work that benefits from concurrency.
JustAPI handles both correctly — you don’t need to worry about blocking the runtime.
For maximum throughput, consider the native fast path (native=True) which bypasses Python entirely.