FastAPI单条插入CPU耗时1-2ms是否正常？性能优化咨询

我正在构建一款可扩展的FastAPI应用，目标是支持每秒10000次以上请求（RPS）。当前应用逻辑复杂，但已定位到核心扩容瓶颈：单条数据库插入操作的CPU耗时约1-2毫秒，这直接限制了单个worker的性能——包含多次插入的接口在单pod/worker下仅能达到约300 RPS，必须大量扩容worker才能提升RPS，效率极低。需要说明的是，我不关注数据库侧延迟，核心矛盾是CPU耗时限制了扩容能力。

我搭建了简化的可复现示例，测试结果显示单条插入的CPU耗时确实在1-2毫秒区间。我想确认这个性能表现对于FastAPI是否正常，或是我的配置存在问题？之前看过性能更优的基准测试，所以想明确是否还有优化空间。

SQLAlchemy版本测试代码（app.py） #

import time
from contextlib import asynccontextmanager

from fastapi import FastAPI
from sqlalchemy import AsyncAdaptedQueuePool, text
from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine
from sqlalchemy.orm import sessionmaker

db_username = "****"
db_password = "****"
host = "****"

async def setup_database():
    dsn = f"postgresql+asyncpg://{db_username}:{db_password}@{host}:5432/postgres"
    engine = create_async_engine(
        dsn,
        pool_size=20,
        poolclass=AsyncAdaptedQueuePool,
    )
    return sessionmaker(engine, class_=AsyncSession)


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Setup the database connection pool
    app.state.db_session = await setup_database()
    yield
    # Close the database connection pool
    await app.state.db_session.close()


app = FastAPI(lifespan=lifespan)


@app.get("/test-insert")
async def test_insert():
    start_time = time.time()
    start_cpu_time = time.process_time()

    insert_query = text("INSERT INTO simple_text (text) VALUES (:text) RETURNING id")
    params = {"text": "Test"}

    async with app.state.db_session.begin() as sess:
        result = await sess.execute(insert_query, params)
        text_id = result.scalar_one()
        await sess.commit()

    end_time = time.time()
    end_cpu_time = time.process_time()
    duration = end_time - start_time
    cpu_duration = end_cpu_time - start_cpu_time

    print(
        f"Test Insert: {duration:.6f} seconds, CPU time: {cpu_duration:.6f} CPU seconds"
    )

    return {"id": text_id, "duration": duration, "cpu_duration": cpu_duration}


if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="0.0.0.0", port=8000)

Dockerfile #

# Use an official Python 3.12 runtime as a parent image
FROM python:3.12-slim

# Set the working directory in the container
WORKDIR /app

# Copy the current directory contents into the container at /app
COPY . /app

# Install the dependencies
RUN pip install --no-cache-dir -r requirements.txt

# Expose port 8000 to the outside world
EXPOSE 8000

# Command to run the FastAPI application
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

requirements.txt #

fastapi
uvicorn[standard]
sqlalchemy
asyncpg

asyncpg版本优化代码片段 #

绕过SQLAlchemy直接使用asyncpg后，核心代码如下：

async def setup_database():
    return await asyncpg.create_pool(
        user=db_username,
        password=db_password,
        database="postgres",
        host=host,
        port=5432,
        min_size=20,
        max_size=20,
    )

@app.get("/test-insert")
async def test_insert():
    start_time = time.time()
    start_cpu_time = time.process_time()

    insert_query = """
                INSERT INTO simple_text (text)
                VALUES ($1)
                RETURNING id
            """
    params = ("Test",)
    async with app.state.connection_pool.acquire() as connection:
        text_id = await connection.fetchval(insert_query, *params)

    end_time = time.time()
    end_cpu_time = time.process_time()
    duration = end_time - start_time
    cpu_duration = end_cpu_time - start_cpu_time

    print(
        f"Test Insert: {duration:.6f} seconds, CPU time: {cpu_duration:.6f} CPU seconds"
    )

    return {"id": text_id, "duration": duration, "cpu_duration": cpu_duration}

测试结果与疑问 #

测试显示，直接使用asyncpg时，单条插入的平均CPU耗时约0.0004秒，性能比SQLAlchemy版本提升了约3倍。我想咨询：

• 这个asyncpg的性能表现是否是当前场景下的最优水平？
• SQLAlchemy与asyncpg之间的这种性能差距是否符合预期？

补充背景：PostgreSQL中的simple_text表仅包含主键id和字符列text；测试环境包括MacBook Pro M3+Colima+Rosetta，且该性能差异同样出现在GCP K8s的Linux Pod中。

内容的提问来源于stack exchange，提问作者Reyflex