Airflow - EMR 操作员中的任务实例
Airflow - Task Instance in EMR operator
在 Airflow 中,我面临着需要将 job_flow_id
传递到我的 emr-steps 之一的问题。我能够从操作员那里检索 job_flow_id
,但是当我要创建提交到集群的步骤时,task_instance
值不正确。
我有以下代码:
def issue_step(name, args):
return [
{
"Name": name,
"ActionOnFailure": "CONTINUE",
"HadoopJarStep": {
"Jar": "s3://....",
"Args": args
}
}
]
dag = DAG('example',
description='My dag',
schedule_interval='0 8 * * 6',
dagrun_timeout=timedelta(days=2))
try:
create_emr = EmrCreateJobFlowOperator(
task_id='create_job_flow',
aws_conn_id='aws_default',
dag=dag
)
load_data_steps = issue_step('load', ['arg1', 'arg2'])
load_data_steps[0]["HadoopJarStep"]["Args"].append('--cluster-id')
load_data_steps[0]["HadoopJarStep"]["Args"].append(
"{{ task_instance.xcom_pull('create_job_flow', key='return_value') }}") # the value here is not exchanged with the actual job_flow_id
load_data = EmrAddStepsOperator(
task_id='load_data',
job_flow_id="{{ task_instance.xcom_pull('create_job_flow', key='return_value') }}", # this is correctly exchanged with the job_flow_id - same for the others
aws_conn_id='aws_default',
steps=load_data_steps,
dag=dag
)
check_load_data = EmrStepSensor(
task_id='watch_load_data',
job_flow_id="{{ task_instance.xcom_pull('create_job_flow', key='return_value') }}",
step_id="{{ task_instance.xcom_pull('load_data', key='return_value')[0] }}",
aws_conn_id='aws_default',
dag=dag
)
cluster_remover = EmrTerminateJobFlowOperator(
task_id='remove_cluster',
job_flow_id="{{ task_instance.xcom_pull('create_job_flow', key='return_value') }}",
aws_conn_id='aws_default',
dag=dag
)
create_emr_recommendations >> load_data
load_data >> check_load_data
check_load_data >> cluster_remover
except AirflowException as ae:
print ae.message
问题是,当我检查 EMR 时,我没有在 load_data
步骤中看到 --cluster-id j-1234
,而是看到 --cluster-id "{{task_instance.xcom_pull('create_job_flow', key='return_value')}}"
,这导致我的步骤失败。
如何在步进函数中获取实际值?
谢谢,节日快乐
我发现气流存储库上有关于 this 的 PR。问题是 EmrAddStepsOperator
中的步骤没有模板。为了克服这个问题,我做了以下事情:
- 创建了一个继承自
EmrAddStepsOperator
的自定义运算符
- 将此运算符添加为插件
- 在我的 DAG 文件中调用了新的操作符
这里是文件 custom_emr_add_step_operator.py
中的自定义运算符和插件的代码(参见下面的树)
from __future__ import division, absolute_import, print_function
from airflow.plugins_manager import AirflowPlugin
from airflow.utils import apply_defaults
from airflow.contrib.operators.emr_add_steps_operator import EmrAddStepsOperator
class CustomEmrAddStepsOperator(EmrAddStepsOperator):
template_fields = ['job_flow_id', 'steps'] # override with steps to solve the issue above
@apply_defaults
def __init__(
self,
*args, **kwargs):
super(CustomEmrAddStepsOperator, self).__init__(*args, **kwargs)
def execute(self, context):
super(CustomEmrAddStepsOperator, self).execute(context=context)
# Defining the plugin class
class CustomPlugin(AirflowPlugin):
name = "custom_plugin"
operators = [CustomEmrAddStepsOperator]
在我的 DAG 文件中我是这样调用插件的
from airflow.operators import CustomEmrAddStepsOperator
我的项目和插件的结构如下所示:
├── config
│ └── airflow.cfg
├── dags
│ ├── __init__.py
│ └── my_dag.py
├── plugins
│ ├── __init__.py
│ └── operators
│ ├── __init__.py
│ └── custom_emr_add_step_operator.py
└── requirements.txt
如果您使用的是 IDE,例如 PyCharm,这会报错,因为它说找不到模块。但是当你运行airflow时,这个问题就不会出现了。
还请记住确保在您的 airflow.cfg
中您将指向正确的 plugins
文件夹,以便 Airflow 能够读取您新创建的插件。
在 Airflow 中,我面临着需要将 job_flow_id
传递到我的 emr-steps 之一的问题。我能够从操作员那里检索 job_flow_id
,但是当我要创建提交到集群的步骤时,task_instance
值不正确。
我有以下代码:
def issue_step(name, args):
return [
{
"Name": name,
"ActionOnFailure": "CONTINUE",
"HadoopJarStep": {
"Jar": "s3://....",
"Args": args
}
}
]
dag = DAG('example',
description='My dag',
schedule_interval='0 8 * * 6',
dagrun_timeout=timedelta(days=2))
try:
create_emr = EmrCreateJobFlowOperator(
task_id='create_job_flow',
aws_conn_id='aws_default',
dag=dag
)
load_data_steps = issue_step('load', ['arg1', 'arg2'])
load_data_steps[0]["HadoopJarStep"]["Args"].append('--cluster-id')
load_data_steps[0]["HadoopJarStep"]["Args"].append(
"{{ task_instance.xcom_pull('create_job_flow', key='return_value') }}") # the value here is not exchanged with the actual job_flow_id
load_data = EmrAddStepsOperator(
task_id='load_data',
job_flow_id="{{ task_instance.xcom_pull('create_job_flow', key='return_value') }}", # this is correctly exchanged with the job_flow_id - same for the others
aws_conn_id='aws_default',
steps=load_data_steps,
dag=dag
)
check_load_data = EmrStepSensor(
task_id='watch_load_data',
job_flow_id="{{ task_instance.xcom_pull('create_job_flow', key='return_value') }}",
step_id="{{ task_instance.xcom_pull('load_data', key='return_value')[0] }}",
aws_conn_id='aws_default',
dag=dag
)
cluster_remover = EmrTerminateJobFlowOperator(
task_id='remove_cluster',
job_flow_id="{{ task_instance.xcom_pull('create_job_flow', key='return_value') }}",
aws_conn_id='aws_default',
dag=dag
)
create_emr_recommendations >> load_data
load_data >> check_load_data
check_load_data >> cluster_remover
except AirflowException as ae:
print ae.message
问题是,当我检查 EMR 时,我没有在 load_data
步骤中看到 --cluster-id j-1234
,而是看到 --cluster-id "{{task_instance.xcom_pull('create_job_flow', key='return_value')}}"
,这导致我的步骤失败。
如何在步进函数中获取实际值?
谢谢,节日快乐
我发现气流存储库上有关于 this 的 PR。问题是 EmrAddStepsOperator
中的步骤没有模板。为了克服这个问题,我做了以下事情:
- 创建了一个继承自
EmrAddStepsOperator
的自定义运算符
- 将此运算符添加为插件
- 在我的 DAG 文件中调用了新的操作符
这里是文件 custom_emr_add_step_operator.py
中的自定义运算符和插件的代码(参见下面的树)
from __future__ import division, absolute_import, print_function
from airflow.plugins_manager import AirflowPlugin
from airflow.utils import apply_defaults
from airflow.contrib.operators.emr_add_steps_operator import EmrAddStepsOperator
class CustomEmrAddStepsOperator(EmrAddStepsOperator):
template_fields = ['job_flow_id', 'steps'] # override with steps to solve the issue above
@apply_defaults
def __init__(
self,
*args, **kwargs):
super(CustomEmrAddStepsOperator, self).__init__(*args, **kwargs)
def execute(self, context):
super(CustomEmrAddStepsOperator, self).execute(context=context)
# Defining the plugin class
class CustomPlugin(AirflowPlugin):
name = "custom_plugin"
operators = [CustomEmrAddStepsOperator]
在我的 DAG 文件中我是这样调用插件的
from airflow.operators import CustomEmrAddStepsOperator
我的项目和插件的结构如下所示:
├── config
│ └── airflow.cfg
├── dags
│ ├── __init__.py
│ └── my_dag.py
├── plugins
│ ├── __init__.py
│ └── operators
│ ├── __init__.py
│ └── custom_emr_add_step_operator.py
└── requirements.txt
如果您使用的是 IDE,例如 PyCharm,这会报错,因为它说找不到模块。但是当你运行airflow时,这个问题就不会出现了。
还请记住确保在您的 airflow.cfg
中您将指向正确的 plugins
文件夹,以便 Airflow 能够读取您新创建的插件。