MagicStack / MagicStack/asyncpg

Asyncio task fails to cancel with asyncpg, gets stuck in a permanent loop

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Lingua principale
Python
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Descrizione

  • asyncpg version: 0.25.0, combined with sqlalchemy1.4.22, can also be reproduced with 0.24.0
  • PostgreSQL version: 13.4
  • Do you use a PostgreSQL SaaS? If so, which? Can you reproduce
    the issue with a local PostgreSQL install?
    : Reproduced Locally
  • Python version: Python3.9.7, also Python3.8.8
  • Platform: Windows 10 x64, also Centos7
  • Do you use pgbouncer?: No
  • Did you install asyncpg with pip?: yes
  • If you built asyncpg locally, which version of Cython did you use?:
  • Can the issue be reproduced under both asyncio and
    uvloop?
    : yes

when creating an asyncio task that does database updates with sqlalchemy and asyncpg, in certain circumstances when the task is cancelled, the task will fail to cancel and the task will end up in a pending state that will never be able to finish
image
When stuck in this state, exceptions can't even be raised to break out of the loop
image
The only way to exit out of this loop is to cancel the task again, either through keyboardInterrupt or executing task.cancel() again. However, there are chances when the second task.cancel() fails as well.

This is quite hard to reproduce as I am unsure exactly at which point in asyncpg running task.cancel() will cause this issue, but by fiddling around with some magic numbers, I am able to consistently reporduce this issue by retrying it a large amount of times.

Attached below is the code that i used to reproduce this issue

# Use postgres/example user/password credentials
version: '3.1'

services:

  db:
    image: postgres:13.4
    restart: always
    environment:
      POSTGRES_PASSWORD: example
    ports:
      - 5432:5432
# coding: utf-8
from sqlalchemy import String, text, Column, BigInteger
from sqlalchemy.dialects.postgresql import TIMESTAMP
from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base()
metadata = Base.metadata


class DummyTable(Base):
    __tablename__ = 'dummy_table'

    id = Column(BigInteger, primary_key=True)
    col_1 = Column(String(100), nullable=False, server_default=text("''::character varying"))
    col_2 = Column(String(100), nullable=False, server_default=text("''::character varying"))
    col_3 = Column(String(100), nullable=False, server_default=text("''::character varying"))

import sys
from pathlib import Path

sys.path.append(str(Path(__file__).parents[2]))

import asyncio
from loguru import logger
from dummy_table import metadata, DummyTable
from sqlalchemy.ext.asyncio import create_async_engine
from sqlalchemy.pool import NullPool
from sqlalchemy import insert, update, bindparam

db_info = {
    "user":"postgres",
    "password":"example",
    "host":"localhost",
    "port":5432,
    "db":"postgres"
}

error_time_offset = 0.0
error_time_diff = 0.069

async def insert_to_db():
    engine = create_async_engine(
        "postgresql+asyncpg://{user}:{password}@{host}:{port}/{db}".format(
            **db_info
        ),
        poolclass=NullPool,
        # echo=True
    )
    async with engine.begin() as session:
        await asyncio.sleep(error_time_offset)
        update_values = [
            {
                "col_1":str(i+1),
                "bind_col_2":str(i)
            } for i in range(10000)
        ]
        stmt = DummyTable.__table__.update().where(
            DummyTable.col_2 == bindparam("bind_col_2")
        )
        logger.info("start update")
        await session.execute(stmt, update_values)
        logger.info("done update")

async def test_cancel():
    while True:
        logger.info("start_task")
        task = asyncio.create_task(insert_to_db())
        logger.info(task)
        await asyncio.sleep(error_time_offset + error_time_diff)
        task.cancel()
        logger.info(task)
        await asyncio.sleep(0.1)
        logger.info(task)
        print('hi')
        while not task.done():
            logger.error("cancel failed")
            # logger.warning(task)
            # print(task.get_stack)
            # break
            logger.info("throw exception now")
            raise Exception()
            # logger.info(task)
            # task.cancel()
            # logger.info(task)
            await asyncio.sleep(0.1)
        logger.success("task cancelled")

async def create_dummy_table():
    engine = create_async_engine(
        "postgresql+asyncpg://{user}:{password}@{host}:{port}/{db}".format(
            **db_info
        ),
        poolclass=NullPool,
        echo=False
    )
    async with engine.connect() as conn:
        await conn.run_sync(metadata.drop_all)
        await conn.run_sync(metadata.create_all)
        insert_values = [
            {
                "col_1":str(i),
                "col_2":str(i),
                "col_3":str(i),
            } for i in range(10000)
        ]
        stmt = insert(
            DummyTable
        ).values(
            insert_values
        )
        await conn.execute(stmt)
        await conn.commit()

async def main():
    await create_dummy_table()
    await test_cancel()


if __name__ == "__main__":
    asyncio.run(main())

you might have to fiddle around with the error_time_diff to reproduce this issue. What I think is causing the issue is the task is being cancelled right at the moment the asyncpg starts communicating with the database, shown in the log below, which is around the code's error_time_diff which is 0.07ms
image

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  1. Leggi tutta la issue e poi la guida ai contributi del progetto.
  2. Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
  3. Fai un fork del repository e lavora su un branch.
  4. Apri una pull request che faccia riferimento al numero della issue.

Direzione di ricerca

Inizia con la riproduzione fornita di create_dummy_table e test_cancel usando PostgreSQL 13.4, Python 3.9, SQLAlchemy e asyncpg, variando error_time_diff come descritto. Traccia il percorso di annullamento attorno a session.execute e determina perché il task rimane in sospeso. Il lavoro è completato quando il task riprodotto raggiunge in modo affidabile il completamento dopo l’annullamento senza richiedere un secondo annullamento.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Valutazione

Stack tecnologico
postgresql, python, sqlalchemy
Ambito
backend, databases
Tipo di issue
Bug
Difficoltà
4/5
Tempo stimato
3-5 giorni
Stato di attività
Ferma
Chiarezza
Da chiarire
Idoneità per principianti
25/100

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