microsoft / microsoft/mssql-python

True async query execution

Abierto
#9 19 comentarios 24 reacciones 1 asignado Ver en GitHub

@sumitmsft ya está trabajando en esto.

Desde el 3/3/2025.

area: api-compliance enhancement triage done
Lenguaje dominante
Python
Estrellas
473
Forks
60
Merge medio
2 d 11 h
PR fusionados (30 d)
36

Descripción

Is your feature request related to a problem? Please describe.

Currently, mssql-python does not support true async execution, forcing developers to use thread-based workarounds (asyncio.to_thread() or aioodbc). This leads to performance limitations, increased memory usage, and suboptimal scalability in high-throughput applications, such as web servers and data processing pipelines.

For applications using FastAPI, Django Async, or AI/ML workloads, true async support is critical for handling multiple concurrent database operations efficiently. Without it, developers face blocking I/O that slows down event-driven applications.

Describe the solution you'd like
  • Truly non-blocking I/O using asyncio (asyncio.StreamReader / asyncio.StreamWriter).
  • Async query execution with await conn.execute("SELECT ...").
  • Streaming large result sets asynchronously using async for row in cursor.
  • Better scalability for FastAPI, Django Async, and other async frameworks.
Example API:
async def stream_users():
    conn = await asyncpg.connect(...)
    
    async with conn.transaction():  # Ensures rollback on failure
        async for row in conn.cursor("SELECT * FROM users"):
            print(row)  # Process each row asynchronously
    
    await conn.close()

asyncio.run(stream_users())

### Describe alternatives you've considered

- asyncio.to_thread() → Still uses blocking I/O and threads, increasing overhead.
- aioodbc → Uses a thread pool, not true async, leading to performance bottlenecks.
- Custom Thread Management → Adds complexity and does not scale well under high concurrency.

### Additional context

Guía de contribución

Abrir la guía de contribución

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

Evaluación

Este issue todavía no se ha evaluado.

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.