Question

我正在编写一个气流任务来读取一个大型的csv并将其保存到postgresql数据库中。我发现此asyncpg软件包具有复制功能，其运行速度比任何其他软件包都快。但是，它是异步的，我不知道如何将其合并到Airflow中。这是示例代码：

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime, timedelta
from pandas import DataFrame
import asyncpg

async def to_sql(dataframe, table_name, schema_name='public', timeout=None, truncate=False):
    connection = await asyncpg.connect(user='postgres', host='host.docker.internal', database='quantaxis', password='123456')
    result = await connection.copy_records_to_table(
        table_name,
        records=dataframe.values.tolist(),
        columns=shared_columns,
        schema_name=schema_name,
        timeout=timeout)
    await connection.close()
    return result


default_args = {
    'owner': 'Airflow',
    'depends_on_past': False,
    'start_date': datetime(2020, 1, 1),
    'retries': 1,
    'retry_delay': timedelta(minutes=1),
}

dag = DAG('pythonexp2123', default_args=default_args, schedule_interval=timedelta(days=1))

async def save_file_to_database(ds):
    df = pd.read_csv("data{0}.csv".format(ds))
    r = await to_sql(df, 'test')
    return r

t1 = PythonOperator(
    task_id='pushing_task',
    provide_context=True,
    python_callable=save_file_to_database,
    dag=dag
    )

t1

当我运行它时，它将返回错误：

Can't Pickle Object <Corountine>

如何更改功能以使Dag正常工作？由于其速度，我仍然要使用asyncpg软件包。

Answer 1

您可以尝试使用asyncio在事件循环中运行异步功能。如果您使用的是python 3.7>，则只需调用asyncio.run（async_function（））

https://docs.python.org/3/library/asyncio-task.html

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime, timedelta
from pandas import DataFrame
import asyncpg
import asyncio

async def to_sql(dataframe, table_name, schema_name='public', timeout=None, truncate=False):
    connection = await asyncpg.connect(user='postgres', host='host.docker.internal', database='quantaxis', password='123456')
    result = await connection.copy_records_to_table(
        table_name,
        records=dataframe.values.tolist(),
        columns=shared_columns,
        schema_name=schema_name,
        timeout=timeout)
    await connection.close()
    return result



default_args = {
    'owner': 'Airflow',
    'depends_on_past': False,
    'start_date': datetime(2020, 1, 1),
    'retries': 1,
    'retry_delay': timedelta(minutes=1),
}

dag = DAG('pythonexp2123', default_args=default_args, schedule_interval=timedelta(days=1))

async def save_file_to_database(ds):
    df = pd.read_csv("data{0}.csv".format(ds))
    r = await to_sql(df, 'test')
    return r

def run_async(ds):
   loop = asyncio.get_event_loop()
   result = loop.run_until_complete(save_file_to_database(ds))
   return result

t1 = PythonOperator(
    task_id='pushing_task',
    provide_context=True,
    python_callable=run_async,
    dag=dag
    )

t1

如何在Airflow中运行异步功能？

1 个答案: