我正在尝试运行一个运行Spark作业的气流示例。这是教程的链接https://www.projectpro.io/recipes/use-sparksubmitoperator-airflow-dag#mcetoc_1g2jkipvff
不幸的是，我得到了一个错误，我不明白。
在我的代码中，在dags文件夹中有以下3个文件:
sparkoperator_demo.py文件:
import airflow
from datetime import timedelta
from airflow import DAG
from airflow.providers.apache.spark.operators.spark_submit import SparkSubmitOperator
from airflow.utils.dates import days_ago
default_args = {
'owner': 'airflow',
#'start_date': airflow.utils.dates.days_ago(2),
# 'end_date': datetime(),
# 'depends_on_past': False,
# 'email': ['airflow@example.com'],
# 'email_on_failure': False,
#'email_on_retry': False,
# If a task fails, retry it once after waiting
# at least 5 minutes
#'retries': 1,
'retry_delay': timedelta(minutes=5),
}
dag_spark = DAG(
dag_id = "sparkoperator_demo",
default_args=args,
# schedule_interval='0 0 * * *',
schedule_interval='@once',
dagrun_timeout=timedelta(minutes=60),
description='use case of sparkoperator in airflow',
start_date = airflow.utils.dates.days_ago(1)
)
spark_submit_local = SparkSubmitOperator(
application ='/home/hduser/basicsparksubmit.py' ,
conn_id= 'spark_local',
task_id='spark_submit_task',
dag=dag_spark
)
if __name__ == "__main__":
dag_spark.cli()
sparksubmit_basic.py文件:
from pyspark import SparkContext
logFilepath = "count.txt"
sc = SparkContext("local", "first app")
logData = sc.textFile(logFilepath).cache()
numAs = logData.filter(lambda s: 'a' in s).count()
numBs = logData.filter(lambda s: 'b' in s).count()
print("Lines with a: %i, lines with b: %i" % (numAs, numBs))
count.txt文件:
test count world of this file
然后，我有这个docker-compose文件:
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#
# Basic Airflow cluster configuration for CeleryExecutor with Redis and PostgreSQL.
#
# WARNING: This configuration is for local development. Do not use it in a production deployment.
#
# This configuration supports basic configuration using environment variables or an .env file
# The following variables are supported:
#
# AIRFLOW_IMAGE_NAME - Docker image name used to run Airflow.
# Default: apache/airflow:2.5.0
# AIRFLOW_UID - User ID in Airflow containers
# Default: 50000
# Those configurations are useful mostly in case of standalone testing/running Airflow in test/try-out mode
#
# _AIRFLOW_WWW_USER_USERNAME - Username for the administrator account (if requested).
# Default: airflow
# _AIRFLOW_WWW_USER_PASSWORD - Password for the administrator account (if requested).
# Default: airflow
# _PIP_ADDITIONAL_REQUIREMENTS - Additional PIP requirements to add when starting all containers.
# Default: ''
#
# Feel free to modify this file to suit your needs.
---
version: '3'
x-airflow-common:
&airflow-common
# In order to add custom dependencies or upgrade provider packages you can use your extended image.
# Comment the image line, place your Dockerfile in the directory where you placed the docker-compose.yaml
# and uncomment the "build" line below, Then run `docker-compose build` to build the images.
image: ${AIRFLOW_IMAGE_NAME:-apache/airflow:2.5.0}
# build: .
environment:
&airflow-common-env
AIRFLOW__CORE__EXECUTOR: CeleryExecutor
AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow
# For backward compatibility, with Airflow <2.3
AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow
AIRFLOW__CELERY__RESULT_BACKEND: db+postgresql://airflow:airflow@postgres/airflow
AIRFLOW__CELERY__BROKER_URL: redis://:@redis:6379/0
AIRFLOW__CORE__FERNET_KEY: ''
AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION: 'true'
AIRFLOW__CORE__LOAD_EXAMPLES: 'true'
AIRFLOW__API__AUTH_BACKENDS: 'airflow.api.auth.backend.basic_auth'
_PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:- apache-airflow-providers-apache-spark apache-airflow-providers-cncf-kubernetes}
volumes:
- ./dags:/opt/airflow/dags
- ./logs:/opt/airflow/logs
- ./plugins:/opt/airflow/plugins
user: "${AIRFLOW_UID:-50000}:0"
depends_on:
&airflow-common-depends-on
redis:
condition: service_healthy
postgres:
condition: service_healthy
services:
postgres:
image: postgres:13
environment:
POSTGRES_USER: airflow
POSTGRES_PASSWORD: airflow
POSTGRES_DB: airflow
volumes:
- postgres-db-volume:/var/lib/postgresql/data
healthcheck:
test: ["CMD", "pg_isready", "-U", "airflow"]
interval: 5s
retries: 5
restart: always
redis:
image: redis:latest
expose:
- 6379
healthcheck:
test: ["CMD", "redis-cli", "ping"]
interval: 5s
timeout: 30s
retries: 50
restart: always
airflow-webserver:
<<: *airflow-common
command: webserver
ports:
- 8080:8080
healthcheck:
test: ["CMD", "curl", "--fail", "http://localhost:8080/health"]
interval: 10s
timeout: 10s
retries: 5
restart: always
depends_on:
<<: *airflow-common-depends-on
airflow-init:
condition: service_completed_successfully
airflow-scheduler:
<<: *airflow-common
command: scheduler
healthcheck:
test: ["CMD-SHELL", 'airflow jobs check --job-type SchedulerJob --hostname "$${HOSTNAME}"']
interval: 10s
timeout: 10s
retries: 5
restart: always
depends_on:
<<: *airflow-common-depends-on
airflow-init:
condition: service_completed_successfully
airflow-worker:
<<: *airflow-common
command: celery worker
healthcheck:
test:
- "CMD-SHELL"
- 'celery --app airflow.executors.celery_executor.app inspect ping -d "celery@$${HOSTNAME}"'
interval: 10s
timeout: 10s
retries: 5
environment:
<<: *airflow-common-env
# Required to handle warm shutdown of the celery workers properly
# See https://airflow.apache.org/docs/docker-stack/entrypoint.html#signal-propagation
DUMB_INIT_SETSID: "0"
restart: always
depends_on:
<<: *airflow-common-depends-on
airflow-init:
condition: service_completed_successfully
airflow-triggerer:
<<: *airflow-common
command: triggerer
healthcheck:
test: ["CMD-SHELL", 'airflow jobs check --job-type TriggererJob --hostname "$${HOSTNAME}"']
interval: 10s
timeout: 10s
retries: 5
restart: always
depends_on:
<<: *airflow-common-depends-on
airflow-init:
condition: service_completed_successfully
airflow-init:
<<: *airflow-common
entrypoint: /bin/bash
# yamllint disable rule:line-length
command:
- -c
- |
function ver() {
printf "%04d%04d%04d%04d" $${1//./ }
}
airflow_version=$$(AIRFLOW__LOGGING__LOGGING_LEVEL=INFO && gosu airflow airflow version)
airflow_version_comparable=$$(ver $${airflow_version})
min_airflow_version=2.2.0
min_airflow_version_comparable=$$(ver $${min_airflow_version})
if (( airflow_version_comparable < min_airflow_version_comparable )); then
echo
echo -e " 33[1;31mERROR!!!: Too old Airflow version $${airflow_version}!e[0m"
echo "The minimum Airflow version supported: $${min_airflow_version}. Only use this or higher!"
echo
exit 1
fi
if [[ -z "${AIRFLOW_UID}" ]]; then
echo
echo -e " 33[1;33mWARNING!!!: AIRFLOW_UID not set!e[0m"
echo "If you are on Linux, you SHOULD follow the instructions below to set "
echo "AIRFLOW_UID environment variable, otherwise files will be owned by root."
echo "For other operating systems you can get rid of the warning with manually created .env file:"
echo " See: https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#setting-the-right-airflow-user"
echo
fi
one_meg=1048576
mem_available=$$(($$(getconf _PHYS_PAGES) * $$(getconf PAGE_SIZE) / one_meg))
cpus_available=$$(grep -cE 'cpu[0-9]+' /proc/stat)
disk_available=$$(df / | tail -1 | awk '{print $$4}')
warning_resources="false"
if (( mem_available < 4000 )) ; then
echo
echo -e " 33[1;33mWARNING!!!: Not enough memory available for Docker.e[0m"
echo "At least 4GB of memory required. You have $$(numfmt --to iec $$((mem_available * one_meg)))"
echo
warning_resources="true"
fi
if (( cpus_available < 2 )); then
echo
echo -e " 33[1;33mWARNING!!!: Not enough CPUS available for Docker.e[0m"
echo "At least 2 CPUs recommended. You have $${cpus_available}"
echo
warning_resources="true"
fi
if (( disk_available < one_meg * 10 )); then
echo
echo -e " 33[1;33mWARNING!!!: Not enough Disk space available for Docker.e[0m"
echo "At least 10 GBs recommended. You have $$(numfmt --to iec $$((disk_available * 1024 )))"
echo
warning_resources="true"
fi
if [[ $${warning_resources} == "true" ]]; then
echo
echo -e " 33[1;33mWARNING!!!: You have not enough resources to run Airflow (see above)!e[0m"
echo "Please follow the instructions to increase amount of resources available:"
echo " https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#before-you-begin"
echo
fi
mkdir -p /sources/logs /sources/dags /sources/plugins
chown -R "${AIRFLOW_UID}:0" /sources/{logs,dags,plugins}
exec /entrypoint airflow version
# yamllint enable rule:line-length
environment:
<<: *airflow-common-env
_AIRFLOW_DB_UPGRADE: 'true'
_AIRFLOW_WWW_USER_CREATE: 'true'
_AIRFLOW_WWW_USER_USERNAME: ${_AIRFLOW_WWW_USER_USERNAME:-airflow}
_AIRFLOW_WWW_USER_PASSWORD: ${_AIRFLOW_WWW_USER_PASSWORD:-airflow}
_PIP_ADDITIONAL_REQUIREMENTS: ''
user: "0:0"
volumes:
- .:/sources
airflow-cli:
<<: *airflow-common
profiles:
- debug
environment:
<<: *airflow-common-env
CONNECTION_CHECK_MAX_COUNT: "0"
# Workaround for entrypoint issue. See: https://github.com/apache/airflow/issues/16252
command:
- bash
- -c
- airflow
# You can enable flower by adding "--profile flower" option e.g. docker-compose --profile flower up
# or by explicitly targeted on the command line e.g. docker-compose up flower.
# See: https://docs.docker.com/compose/profiles/
flower:
<<: *airflow-common
command: celery flower
profiles:
- flower
ports:
- 5555:5555
healthcheck:
test: ["CMD", "curl", "--fail", "http://localhost:5555/"]
interval: 10s
timeout: 10s
retries: 5
restart: always
depends_on:
<<: *airflow-common-depends-on
airflow-init:
condition: service_completed_successfully
volumes:
postgres-db-volume:
我的问题是，当我去到localhost:8080我得到这个错误消息:
Broken DAG: [/opt/airflow/dags/sparkoperator_demo.py] Traceback (most recent call last):
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/opt/airflow/dags/sparkoperator_demo.py", line 24, in <module>
default_args=args,
NameError: name 'args' is not defined
我还注意到在logs文件夹中我得到了以下日志:
[2023-01-02T13:03:40.715+0000] {processor.py:153} INFO - Started process (PID=75) to work on /opt/airflow/dags/sparkoperator_demo.py
[2023-01-02T13:03:40.760+0000] {processor.py:743} INFO - Processing file /opt/airflow/dags/sparkoperator_demo.py for tasks to queue
[2023-01-02T13:03:40.778+0000] {logging_mixin.py:137} INFO - [2023-01-02T13:03:40.777+0000] {dagbag.py:538} INFO - Filling up the DagBag from /opt/airflow/dags/sparkoperator_demo.py
[2023-01-02T13:03:40.976+0000] {logging_mixin.py:137} INFO - [2023-01-02T13:03:40.937+0000] {dagbag.py:343} ERROR - Failed to import: /opt/airflow/dags/sparkoperator_demo.py
Traceback (most recent call last):
File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/dagbag.py", line 339, in parse
loader.exec_module(new_module)
File "<frozen importlib._bootstrap_external>", line 728, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/opt/airflow/dags/sparkoperator_demo.py", line 24, in <module>
default_args=args,
NameError: name 'args' is not defined
[2023-01-02T13:03:40.984+0000] {processor.py:755} WARNING - No viable dags retrieved from /opt/airflow/dags/sparkoperator_demo.py
[2023-01-02T13:03:41.357+0000] {processor.py:175} INFO - Processing /opt/airflow/dags/sparkoperator_demo.py took 0.690 seconds
你能帮我一下吗?
实际上变量args没有定义，只需将其替换为default_args，这是您的参数字典的名称:
dag_spark = DAG(
dag_id = "sparkoperator_demo",
default_args=default_args,
# schedule_interval='0 0 * * *',
schedule_interval='@once',
dagrun_timeout=timedelta(minutes=60),
description='use case of sparkoperator in airflow',
start_date = airflow.utils.dates.days_ago(1)
)