MagicStack / MagicStack/asyncpg
Asyncpg.pool creates more connections than its max_size
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- 主要语言
- Python
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描述
* **asyncpg version**:0.29.0
* **PostgreSQL version**: "PostgreSQL 16.0 (Debian 16.0-1.pgdg120+1) on aarch64-unknown-linux-gnu, compiled by gcc (Debian 12.2.0-14) 12.2.0, 64-bit"
* **Do you use a PostgreSQL SaaS? If so, which? Can you reproduce
the issue with a local PostgreSQL install?**: Nope, using Dockerized container
* **Python version**: 3.11.5
* **Platform**: MacBook-Air Darwin Kernel Version 21.1.0: Wed Oct 13 17:33:24 PDT 2021; root:xnu-8019.41.5~1/RELEASE_ARM64_T8101 arm64
* **Do you use pgbouncer?**: Nope
* **Did you install asyncpg with pip?**: Yes
* **If you built asyncpg locally, which version of Cython did you use?**: n/a
* **Can the issue be reproduced under both asyncio and
[uvloop](https://github.com/magicstack/uvloop)?**:Nope
I have a singleton for a Database entity, which is then used to write some primitive data. When I run the write function 1000 times concurrently using `asyncio.gather()`, the database reports that there is more connections than the `max_size` of the `asyncpg.pool`. For example, when I was testing there were 857 active db connections, but only 62 active pool connections. No other clients/operations were running during the test. When I use uvloop to do the same thing, it just crashes with `ConnectionResetError: [Errno 54] Connection reset by peer` if I try to run more tasks than the size of the pool.
Is this a normal pool behavior?
I use code below (the write function is simplified though):
The database code:
```
class Database:
_instance = None
_pool = None
db_params = {
'host': os.getenv('DATABASE_HOST'),
'port': os.getenv('DATABASE_PORT'),
'database': os.getenv('DATABASE_NAME'),
'user': os.getenv('DATABASE_USER'),
'password': os.getenv('DATABASE_PASSWORD')
}
def __new__(cls, *args, **kwargs):
if cls._instance is None:
cls._instance = super(Database, cls).__new__(cls)
#print(cls._instance)
return cls._instance
@classmethod
async def get_pool(cls):
if cls._pool is None:
cls._pool = await asyncpg.create_pool(**cls.db_params, min_size=1, max_size=150)
#print(cls._pool)
return cls._pool
@classmethod
async def write(cls, result):
pool = await cls.get_pool()
try:
async with pool.acquire() as connection:
result = await connection.execute('''
INSERT INTO tables.results(
result
) VALUES($1)
''', result)
return
except Exception as e:
raise e
```
The demo write code
```
async def fake_result(i):
print(f'generating fake result {i}')
await db.write(i)
return
async def run_functions_concurrently():
tasks = [fake_result(i) for i in range(1000)]
await asyncio.gather(*tasks)
def main():
asyncio.run(run_functions_concurrently())
if __name__ == "__main__":
main()
```
贡献指南
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- Fork 仓库,在一个分支上完成修改。
- 提交 Pull Request,并在描述里引用这个 Issue 编号。
调研方向
从 asyncpg.create_pool、pool.acquire 以及 issue 中展示的写入路径开始。使用 1000 个 gather 的任务重现 run_functions_concurrently,并将 PostgreSQL 的活动连接数与 pool 的 max_size 150 进行比较。完成的标准是确定连接数是否符合预期,并记录或修正观察到的行为。
由索引模型根据 Issue 内容生成。
评估
- 技术栈
- postgresql, python
- 领域
- databases
- Issue 类型
- 缺陷
- 难度
- 4/5
- 预计耗时
- 3-5 天
- 活跃度
- 停滞
- 描述清晰度
- 需要澄清
- 新手友好度
- 35/100