Airflow: 如何在不同的Docker容器中触发Spark？

Question

我在Docker Compose中运行了官方的Airflow 2镜像和Apache Spark，想通过SparkSubmitOperator（https://airflow.apache.org/docs/apache-airflow/1.10.14/_api/airflow/contrib/operators/spark_submit_operator/index.html）执行一个触发Spark脚本的DAG，但是失败了。在Airflow Web服务器上，我看到了以下日志:

*** Reading local file: /opt/airflow/logs/timetable/spark-job/2021-05-16T07:18:57.288610+00:00/1.log
[2021-05-16 07:18:58,856] {taskinstance.py:877} INFO - Dependencies all met for <TaskInstance: timetable.spark-job 2021-05-16T07:18:57.288610+00:00 [queued]>
[2021-05-16 07:18:58,906] {taskinstance.py:877} INFO - Dependencies all met for <TaskInstance: timetable.spark-job 2021-05-16T07:18:57.288610+00:00 [queued]>
[2021-05-16 07:18:58,906] {taskinstance.py:1068} INFO - 
--------------------------------------------------------------------------------
[2021-05-16 07:18:58,906] {taskinstance.py:1069} INFO - Starting attempt 1 of 4
[2021-05-16 07:18:58,906] {taskinstance.py:1070} INFO - 
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[2021-05-16 07:18:58,926] {taskinstance.py:1089} INFO - Executing <Task(SparkSubmitOperator): spark-job> on 2021-05-16T07:18:57.288610+00:00
[2021-05-16 07:18:58,941] {standard_task_runner.py:76} INFO - Running: ['airflow', 'tasks', 'run', 'timetable', 'spark-job', '2021-05-16T07:18:57.288610+00:00', '--job-id', '164', '--pool', 'default_pool', '--raw', '--subdir', 'DAGS_FOLDER/dag.py', '--cfg-path', '/tmp/tmp0opwomfp', '--error-file', '/tmp/tmpl4ctddqc']
[2021-05-16 07:18:58,935] {standard_task_runner.py:52} INFO - Started process 69 to run task
[2021-05-16 07:18:58,941] {standard_task_runner.py:77} INFO - Job 164: Subtask spark-job
[2021-05-16 07:18:59,000] {logging_mixin.py:104} INFO - Running <TaskInstance: timetable.spark-job 2021-05-16T07:18:57.288610+00:00 [running]> on host 94b160a4f0d4
[2021-05-16 07:18:59,053] {taskinstance.py:1283} INFO - Exporting the following env vars:
AIRFLOW_CTX_DAG_ID=timetable
AIRFLOW_CTX_TASK_ID=spark-job
AIRFLOW_CTX_EXECUTION_DATE=2021-05-16T07:18:57.288610+00:00
AIRFLOW_CTX_DAG_RUN_ID=manual__2021-05-16T07:18:57.288610+00:00
[2021-05-16 07:18:59,055] {base.py:78} INFO - Using connection to: id: spark_default. Host: spark, Port: 8080, Schema: , Login: None, Password: None, extra: None
[2021-05-16 07:18:59,057] {spark_submit.py:364} INFO - Spark-Submit cmd: spark-submit --master spark:8080 --name arrow-spark spark-app.py
[2021-05-16 07:18:59,145] {spark_submit.py:526} INFO - JAVA_HOME is not set
[2021-05-16 07:18:59,156] {taskinstance.py:1482} ERROR - Task failed with exception
Traceback (most recent call last):
  File "/home/airflow/.local/lib/python3.6/site-packages/airflow/models/taskinstance.py", line 1138, in _run_raw_task
    self._prepare_and_execute_task_with_callbacks(context, task)
  File "/home/airflow/.local/lib/python3.6/site-packages/airflow/models/taskinstance.py", line 1311, in _prepare_and_execute_task_with_callbacks
    result = self._execute_task(context, task_copy)
  File "/home/airflow/.local/lib/python3.6/site-packages/airflow/models/taskinstance.py", line 1341, in _execute_task
    result = task_copy.execute(context=context)
  File "/home/airflow/.local/lib/python3.6/site-packages/airflow/providers/apache/spark/operators/spark_submit.py", line 183, in execute
    self._hook.submit(self._application)
  File "/home/airflow/.local/lib/python3.6/site-packages/airflow/providers/apache/spark/hooks/spark_submit.py", line 455, in submit
    self._mask_cmd(spark_submit_cmd), returncode
airflow.exceptions.AirflowException: Cannot execute: spark-submit --master spark:8080 --name arrow-spark spark-app.py. Error code is: 1.
[2021-05-16 07:18:59,159] {taskinstance.py:1532} INFO - Marking task as UP_FOR_RETRY. dag_id=timetable, task_id=spark-job, execution_date=20210516T071857, start_date=20210516T071858, end_date=20210516T071859
[2021-05-16 07:18:59,196] {local_task_job.py:146} INFO - Task exited with return code 1

作为错误提示信息 airflow.exceptions.AirflowException: Cannot execute: spark-submit --master spark:8080 --name arrow-spark spark-app.py. Error code is: 1. 不够精准，我不知道该如何继续。某种程度上似乎是 Spark 的连接没有正确初始化。因此我的问题是：

当 Spark 位于不同的 Docker 容器中时，如何使用 SparkSubmitOperator 触发 DAG？

我的设置：

daniel@Yoga:~/Projekte/db/airflow$ docker ps
CONTAINER ID   IMAGE                  COMMAND                  CREATED          STATUS                    PORTS                                                 NAMES
5e50523ee1ad   apache/airflow:2.0.2   "/usr/bin/dumb-init …"   23 minutes ago   Up 23 minutes             8080/tcp                                              airflowWorker
1da21c7545b3   apache/airflow:2.0.2   "/usr/bin/dumb-init …"   23 minutes ago   Up 23 minutes (healthy)   0.0.0.0:8081->8080/tcp, :::8081->8080/tcp             airflowWebserver
0fa61a4d0ce0   apache/airflow:2.0.2   "/usr/bin/dumb-init …"   23 minutes ago   Up 23 minutes (healthy)   0.0.0.0:5555->5555/tcp, :::5555->5555/tcp, 8080/tcp   airflowFlower
8a09bafae90a   apache/airflow:2.0.2   "/usr/bin/dumb-init …"   23 minutes ago   Up 23 minutes             8080/tcp                                              airflowScheduler
bbe5eb2111a7   postgres:13            "docker-entrypoint.s…"   23 minutes ago   Up 23 minutes (healthy)   5432/tcp                                              airflowPostgres
6b03d5411e52   redis:latest           "docker-entrypoint.s…"   23 minutes ago   Up 23 minutes (healthy)   0.0.0.0:6380->6379/tcp, :::6380->6379/tcp             airflowRedis
23457a819731   bitnami/spark:3        "/opt/bitnami/script…"   48 minutes ago   Up 48 minutes                                                                   spark_worker2
b2df035e216e   bitnami/spark:3        "/opt/bitnami/script…"   48 minutes ago   Up 48 minutes             0.0.0.0:8080->8080/tcp, :::8080->8080/tcp             spark
452d0cec2a0c   bitnami/spark:3        "/opt/bitnami/script…"   48 minutes ago   Up 48 minutes                                                                   spark_worker1

所有容器都在同一个Docker网络中，因此应该可以轻松通信。

我的DAG：

from datetime import datetime, timedelta

from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.providers.apache.spark.operators.spark_submit import SparkSubmitOperator

default_args = {
    'owner': 'dw',
    'start_date': datetime(2021, 5, 9),
    "retries": 3,
    "retry_delay": timedelta(minutes = 1)
}

dag = DAG('timetable', description = 'spark test', catchup = False, schedule_interval = "@hourly", default_args = default_args)

s1 = SparkSubmitOperator(
    task_id = "spark-job",
    application = "spark-app.py",
    conn_id = "spark_default",
    dag = dag
)

可以看到，conn_id 被设置为 spark_default。这个连接是在 Airflow 服务器下的 Admin/Connections 设置的。我已将其设置如下：

我想通过 Spark 执行的脚本非常简单（虚拟脚本），并且位于定义 DAG 的同一文件夹中：

from pyspark import SparkContext
sc = SparkContext("local", "First App")

rdd = sc.parallelize(range(10))

rdd.count()

有几个类似的帖子讨论了这个问题，但我在那里没有找到合适的解决方案：

• 从Airflow向外部Spark容器提交spark作业
• 使用SparkSubmitOperator无法执行spark作业
• 基于Docker的解决方案中的Apache Spark和Apache Airflow连接

- Requin

---

嗨@Requin，你是否使用--link spark将你的Airflow容器链接到Spark容器，因为你直接在HOST变量spark:8080中给出了容器名称。如果你不链接两个容器，它将无法解析。 - sai

考虑到您不确定错误的原因，您可能需要确保容器能够找到作业。我感觉SparkSubmitOperator无法找到您的文件。此外，请注意，在Python中，惯例是使用蛇形命名法，spark-app.py应该是spark_app.py。我不认为这是错误的原因，但在您不确定原因的情况下，我们不应排除任何可能性。 - Jorrick Sleijster

继续我的上一个评论。SparkSubmitOperator打开一个shell（终端），在其中运行spark-submit --master spark:8080 --name arrow-spark spark-app.py命令。您可能希望尝试从Airflow Docker镜像内部运行该命令以使其正常工作。 - Jorrick Sleijster

因此，我收到了“JAVA_HOME未设置”的反馈 - 这可能是整个事情无法正常工作的原因吗？如果我使用不存在的文件，也会出现这种情况。因此，在此时它甚至没有到达要执行的文件，而是在之前就停止了... 我也不确定它在哪里找不到Java：在airflow容器还是spark容器中？airflow容器似乎没有安装Java，而环境变量JAVA_HOME在spark容器中已经设置好了。 - Requin

我认为这就是问题所在。Airflow容器中具有spark-submit操作符的容器没有安装Java，也没有JAVA_HOME。 - floating_hammer

Answer 1

我最终通过在AirflowWorker容器中安装Java来使其运行，正如@floating_hammer建议的那样。

有关详细信息，请参见如何使用docker-compose.yaml在airflow容器中安装Java。

- Requin

---

太好了，听到这个消息很高兴！ :) - Jorrick Sleijster