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.
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