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使用 Cloud Composer 的工作流

在本文档中，您将使用 Google Cloud 的以下收费组件：

Dataproc
Compute Engine
Cloud Composer

您可使用价格计算器根据您的预计使用情况来估算费用。 Google Cloud 新用户可能有资格申请免费试用。

准备工作

设置项目

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Go to project selector

Make sure that billing is enabled for your Google Cloud project.

Enable the Dataproc, Compute Engine, and Cloud Composer APIs.

Enable the APIs

Install the Google Cloud CLI.

To initialize the gcloud CLI, run the following command:

gcloud init

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Go to project selector

Make sure that billing is enabled for your Google Cloud project.

Enable the Dataproc, Compute Engine, and Cloud Composer APIs.

Enable the APIs

Install the Google Cloud CLI.

To initialize the gcloud CLI, run the following command:

gcloud init

创建 Dataproc 工作流模板

在本地终端窗口或 Cloud Shell 中复制并运行下面列出的命令，以创建和定义工作流模板。

创建 sparkpi 工作流模板。

gcloud dataproc workflow-templates create sparkpi \
    --region=us-central1

将 Spark 作业添加到 sparkpi 工作流模板。“compute”step-id 标志用于识别 SparkPi 作业。

gcloud dataproc workflow-templates add-job spark \
    --workflow-template=sparkpi \
    --step-id=compute \
    --class=org.apache.spark.examples.SparkPi \
    --jars=file:///usr/lib/spark/examples/jars/spark-examples.jar \
    --region=us-central1 \
    -- 1000

使用托管单节点集群运行工作流。Dataproc 将创建集群，对其运行工作流，然后在工作流完成时删除集群。

gcloud dataproc workflow-templates set-managed-cluster sparkpi \
    --cluster-name=sparkpi \
    --single-node \
    --region=us-central1

确认创建工作流模板。
控制台

点击 Dataproc 上的 sparkpi 名称 工作流程 页面，以打开 工作流模板详情页面。点击工作流模板的名称以确认 sparkpi 模板特性。
gcloud 命令

运行以下命令：
```
gcloud dataproc workflow-templates describe sparkpi --region=us-central1
    
```

创建 DAG 并将其上传到 Cloud Storage

创建或使用现有的 Cloud Composer 环境。
设置环境变量。
Airflow 界面
1. 在工具栏中，点击 Admin > Variables。
2. 点击创建。
3. 请输入以下信息：
  - Key：project_id
  - Val：PROJECT_ID - 您的 Google Cloud 项目 ID
4. 点击保存。
gcloud 命令

输入以下命令：
- ENVIRONMENT 是 Cloud Composer 环境的名称
- LOCATION 是 Cloud Composer 环境所在的地区
- PROJECT_ID 是包含 Cloud Composer 环境的项目的 ID
```
    gcloud composer environments run ENVIRONMENT --location LOCATION variables set -- project_id PROJECT_ID
    
```

将以下 DAG 代码从本地复制到名为“composer-dataproc-dag.py”的文件中，该文件使用 DataprocInstantiateWorkflowTemplateOperator。

Airflow 2


"""Example Airflow DAG that kicks off a Cloud Dataproc Template that runs a
Spark Pi Job.

This DAG relies on an Airflow variable
https://airflow.apache.org/docs/apache-airflow/stable/concepts/variables.html
* project_id - Google Cloud Project ID to use for the Cloud Dataproc Template.
"""

import datetime

from airflow import models
from airflow.providers.google.cloud.operators.dataproc import (
    DataprocInstantiateWorkflowTemplateOperator,
)
from airflow.utils.dates import days_ago

project_id = "{{var.value.project_id}}"


default_args = {
    # Tell airflow to start one day ago, so that it runs as soon as you upload it
    "start_date": days_ago(1),
    "project_id": project_id,
}

# Define a DAG (directed acyclic graph) of tasks.
# Any task you create within the context manager is automatically added to the
# DAG object.
with models.DAG(
    # The id you will see in the DAG airflow page
    "dataproc_workflow_dag",
    default_args=default_args,
    # The interval with which to schedule the DAG
    schedule_interval=datetime.timedelta(days=1),  # Override to match your needs
) as dag:
    start_template_job = DataprocInstantiateWorkflowTemplateOperator(
        # The task id of your job
        task_id="dataproc_workflow_dag",
        # The template id of your workflow
        template_id="sparkpi",
        project_id=project_id,
        # The region for the template
        region="us-central1",
    )

Airflow 1


"""Example Airflow DAG that kicks off a Cloud Dataproc Template that runs a
Spark Pi Job.

This DAG relies on an Airflow variable
https://airflow.apache.org/docs/apache-airflow/stable/concepts/variables.html
* project_id - Google Cloud Project ID to use for the Cloud Dataproc Template.
"""

import datetime

from airflow import models
from airflow.contrib.operators import dataproc_operator
from airflow.utils.dates import days_ago

project_id = "{{var.value.project_id}}"


default_args = {
    # Tell airflow to start one day ago, so that it runs as soon as you upload it
    "start_date": days_ago(1),
    "project_id": project_id,
}

# Define a DAG (directed acyclic graph) of tasks.
# Any task you create within the context manager is automatically added to the
# DAG object.
with models.DAG(
    # The id you will see in the DAG airflow page
    "dataproc_workflow_dag",
    default_args=default_args,
    # The interval with which to schedule the DAG
    schedule_interval=datetime.timedelta(days=1),  # Override to match your needs
) as dag:
    start_template_job = dataproc_operator.DataprocWorkflowTemplateInstantiateOperator(
        # The task id of your job
        task_id="dataproc_workflow_dag",
        # The template id of your workflow
        template_id="sparkpi",
        project_id=project_id,
        # The region for the template
        # For more info on regions where Dataflow is available see:
        # https://cloud.google.com/dataflow/docs/resources/locations
        region="us-central1",
    )

将 DAG 上传到 Cloud Storage 中的环境文件夹。上传成功完成后，点击 Cloud Composer 环境页面上的 DAGs 文件夹链接。

查看任务的状态

Airflow 界面

打开 Airflow 网页界面。
在 DAG 页面上，点击 DAG 名称（例如 dataproc_workflow_dag）。
在 DAG 详细信息页面上，点击 Graph View。
查看状态：
- 失败：任务被红色框圈起。您还可以将指针悬停在任务上，然后查看 State: Failed。
- 成功：任务被绿色框圈起。您还可以将指针悬停在任务上，然后查看 State: Success。

控制台

点击“工作流”标签页以查看工作流状态。

gcloud 命令

gcloud dataproc operations list \
    --region=us-central1 \
    --filter="labels.goog-dataproc-workflow-template-id=sparkpi"

清理

为避免系统向您的 Google Cloud 账号收取费用，您可以删除本教程中使用的资源：

后续步骤

请参阅 Dataproc 工作流模板概览
请参阅工作流安排解决方案