[英]Cannot Create a Dataproc cluster
我尝试通过 Airflow 和 Google Cloud UI 创建 Dataproc 集群,但集群创建最终总是失败。 以下是我用来创建集群的气流代码 -
# STEP 1: Libraries needed
from datetime import timedelta, datetime
from airflow import models
from airflow.operators.bash_operator import BashOperator
from airflow.contrib.operators import dataproc_operator
from airflow.utils import trigger_rule
from poc.utils.transform import main
from airflow.contrib.hooks.gcp_dataproc_hook import DataProcHook
from airflow.operators.python_operator import BranchPythonOperator
import os
YESTERDAY = datetime.combine(
datetime.today() - timedelta(1),
datetime.min.time())
project_name = os.environ['GCP_PROJECT']
# Can pull in spark code from a gcs bucket
# SPARK_CODE = ('gs://us-central1-cl-composer-tes-fa29d311-bucket/spark_files/transformation.py')
dataproc_job_name = 'spark_job_dataproc'
default_dag_args = {
'depends_on_past': False,
'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'start_date': YESTERDAY,
'retry_delay': timedelta(minutes=5),
'project_id': project_name,
'owner': 'DataProc',
}
with models.DAG(
'dataproc-poc',
description='Dag to run a simple dataproc job',
schedule_interval=timedelta(days=1),
default_args=default_dag_args) as dag:
CLUSTER_NAME = 'dataproc-cluster'
def ensure_cluster_exists(ds, **kwargs):
cluster = DataProcHook().get_conn().projects().regions().clusters().get(
projectId=project_name,
region='us-east1',
clusterName=CLUSTER_NAME
).execute(num_retries=5)
print(cluster)
if cluster is None or len(cluster) == 0 or 'clusterName' not in cluster:
return 'create_dataproc'
else:
return 'run_spark'
# start = BranchPythonOperator(
# task_id='start',
# provide_context=True,
# python_callable=ensure_cluster_exists,
# )
print_date = BashOperator(
task_id='print_date',
bash_command='date'
)
create_dataproc = dataproc_operator.DataprocClusterCreateOperator(task_id='create_dataproc',
cluster_name=CLUSTER_NAME,
num_workers=2,
use_if_exists='true',
zone='us-east1-b',
master_machine_type='n1-standard-1',
worker_machine_type='n1-standard-1')
# Run the PySpark job
run_spark = dataproc_operator.DataProcPySparkOperator(
task_id='run_spark',
main=main,
cluster_name=CLUSTER_NAME,
job_name=dataproc_job_name
)
# dataproc_operator
# Delete Cloud Dataproc cluster.
# delete_dataproc = dataproc_operator.DataprocClusterDeleteOperator(
# task_id='delete_dataproc',
# cluster_name='dataproc-cluster-demo-{{ ds_nodash }}',
# trigger_rule=trigger_rule.TriggerRule.ALL_DONE)
# STEP 6: Set DAGs dependencies
# Each task should run after have finished the task before.
print_date >> create_dataproc >> run_spark
# print_date >> start >> create_dataproc >> run_spark
# start >> run_spark
我检查了集群日志并看到以下错误 -
Cannot start master: Timed out waiting for 2 datanodes and nodemanagers. Operation timed out: Only 0 out of 2 minimum required datanodes running. Operation timed out: Only 0 out of 2 minimum required node managers running.
此错误表明工作节点无法与主节点通信。 当工作节点无法在给定的时间范围内向主节点报告时,集群创建失败。
请检查您是否设置了正确的防火墙规则以允许虚拟机之间的通信。
您可以参考以下网络配置最佳实践: https : //cloud.google.com/dataproc/docs/concepts/configuring-clusters/network#overview
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