[英]Calling TaskGroup with Dynamic sub task id from BranchPythonOperator
I want to call a TaskGroup with a Dynamic sub-task id from BranchPythonOperator.我想从 BranchPythonOperator 调用一个带有动态子任务 ID 的任务组。
This is the DAG flow that I have:这是我拥有的 DAG 流程:
My case is I want to check whether a table exists in BigQuery or not.我的情况是我想检查 BigQuery 中是否存在一个表。
If exists: do nothing and end the DAG如果存在:什么都不做并结束 DAG
If not exists: Ingest the data from Postgres to Google Cloud Storage如果不存在:将数据从 Postgres 提取到 Google Cloud Storage
I know that to call a TaskGroup from BranchPythonOperator is by calling the task id with following format:我知道从 BranchPythonOperator 调用 TaskGroup 是通过调用具有以下格式的任务 ID:
group_task_id.task_id
The problem is, my task group's sub task id is dynamic, depends on how many time I loop the TaskGroup.问题是,我的任务组的子任务 ID 是动态的,取决于我循环任务组的次数。 So the sub_task will be:
所以 sub_task 将是:
parent_task_id.sub_task_1
parent_task_id.sub_task_2
parent_task_id.sub_task_3
...
parent_task_id.sub_task_x
This is the following code for the DAG that I have:这是我拥有的 DAG 的以下代码:
import airflow
from airflow.providers.google.cloud.transfers.postgres_to_gcs import PostgresToGCSOperator
from airflow.utils.task_group import TaskGroup
from google.cloud.exceptions import NotFound
from airflow import DAG
from airflow.operators.python import BranchPythonOperator
from airflow.operators.dummy import DummyOperator
from google.cloud import bigquery
default_args = {
'owner': 'Airflow',
'start_date': airflow.utils.dates.days_ago(2),
}
with DAG(dag_id='branch_dag', default_args=default_args, schedule_interval=None) as dag:
def create_task_group(worker=1):
var = dict()
with TaskGroup(group_id='parent_task_id') as tg1:
for i in range(worker):
var[f'sub_task_{i}'] = PostgresToGCSOperator(
task_id = f'sub_task_{i}',
postgres_conn_id = 'some_postgres_conn_id',
sql = 'test.sql',
bucket = 'test_bucket',
filename = 'test_file.json',
export_format = 'json',
gzip = True,
params = {
'worker': worker
}
)
return tg1
def is_exists_table():
client = bigquery.Client()
try:
table_name = client.get_table('dataset_id.some_table')
if table_name:
return 'task_end'
except NotFound as error:
return 'parent_task_id'
task_start = DummyOperator(
task_id = 'start'
)
task_branch_table = BranchPythonOperator(
task_id ='check_table_exists_in_bigquery',
python_callable = is_exists_table
)
task_pg_to_gcs_init = create_task_group(worker=3)
task_end = DummyOperator(
task_id = 'end',
trigger_rule = 'all_done'
)
task_start >> task_branch_table >> task_end
task_start >> task_branch_table >> task_pg_to_gcs_init >> task_end
When I run the dag, it returns当我运行 dag 时,它返回
** airflow.exceptions.TaskNotFound: Task parent_task_id not found
** **
airflow.exceptions.TaskNotFound: Task parent_task_id not found
**
But this is expected, what I don't know is how to iterate the parent_task_id.sub_task_x
on is_exists_table
function. Or are there any workaround?但这是预料之中的,我不知道如何在
parent_task_id.sub_task_x
上迭代is_exists_table
。或者有什么解决方法吗?
This is the test.sql
file if it's needed如果需要,这是
test.sql
文件
SELECT
id,
name,
country
FROM some_table
WHERE 1=1
AND ABS(MOD(hashtext(id::TEXT), 3)) = {{params.worker}};
-- returns 1M+ rows
I already seen this question as reference Question but I think my case is more specific.我已经将此问题视为参考问题,但我认为我的情况更具体。
When designing your data pipelines, you may encounter use cases that require more complex task flows than "Task A > Task B > Task C."在设计数据管道时,您可能会遇到需要比“任务 A > 任务 B > 任务 C”更复杂任务流的用例。 For example, you may have a use case where you need to decide between multiple tasks to execute based on the results of an upstream task.
例如,您可能有一个用例,您需要根据上游任务的结果在要执行的多个任务之间做出决定。 Or you may have a case where part of your pipeline should only run under certain external conditions.
或者您可能会遇到这样的情况,即您的管道的一部分应该只在特定的外部条件下运行。 Fortunately, Airflow has multiple options for building conditional logic and/or branching into your DAGs.
幸运的是,Airflow 有多个选项可用于构建条件逻辑和/或分支到您的 DAG。
I found a dirty way around it.我发现了一个肮脏的方法。
What I did is creating 1 additional task using DummyOperator called task_pass.我所做的是使用名为 task_pass 的 DummyOperator 创建 1 个额外的任务。
task_pass = DummyOperator(
task_id = 'pass_to_task_group'
)
So the DAG flow now looks like this:所以 DAG 流程现在看起来像这样:
task_start >> task_branch_table >> task_end
task_start >> task_branch_table >> task_pass >> task_pg_to_gcs_init >> task_end
Also there is 1 mistake that I made from the code above, notice that the params I set was worker.我在上面的代码中也犯了 1 个错误,请注意我设置的参数是 worker。 This is wrong because worker is the constant while the thing that I need to iterate is the i variable.
这是错误的,因为 worker 是常量,而我需要迭代的是 i 变量。 So I change it from:
所以我将其更改为:
params: worker
to:到:
params: i
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