Sub-millisecond analytical APIs: Async ClickHouse for FastAPI.

Day 10 of the WClickHouse Open-Source Engineering Series.

Don't let analytical database queries block your web server. WClickHouse async client delivers non-blocking, sub-millisecond concurrency for modern Python APIs.

The Pain Points We Faced

Synchronous ClickHouse queries blocking the main asyncio thread in FastAPI
API latency degrading when handling multiple concurrent dashboard requests
Worker thread pool exhaustion under heavy analytical traffic

The Implementation

from wclickhouse import get_async_client

# Inside an async FastAPI route:
async def get_dashboard_metrics(org_id: str):
    client = await get_async_client(db_config)
    result = await client.query(
        "SELECT count(), avg(latency) FROM metrics WHERE org_id = {org:String}",
        parameters={"org": org_id}
    )
    return {"summary": result.result_rows}

Why This Architecture Wins

async/await Native: Execute analytical queries without blocking other HTTP requests.
Async Connection Pool: Reuses HTTP/TCP keepalive connections across async tasks.
FastAPI Optimized: Perfect for high-throughput real-time analytics microservices.

Verification & Status

Tested and verified against live ClickHouse server instances with 95%+ test coverage. Built for Python 3.9 through 3.14 with Apache Arrow and Pydantic v2.

GitHub: https://github.com/wisrovi/wclickhouse
PyPI: https://pypi.org/project/wclickhouse