我们有Airflow 1.10.3与Celery 4.1.1和Redis作为消息代理。
当我们打开Web服务器时，计划的DAG将无限期地进入运行状态，并且在Flower UI中看不到任何活动任务。
在日志(气流启动日志(中，我们得到以下错误：(发送Celery任务时出错：超时(
{"timestamp":"2020-11-11T09:45:58.326682", "hostname":"", "process":"scheduler", "name":"airflow.executors.celery_executor.CeleryExecutor", "level":"ERROR", "message":"Error sending Celery task:Timeout, PID: 16001nCelery Task ID: ('tutorial', 'print_date', datetime.datetime(2020, 11, 9, 0, 0, tzinfo=<Timezone [UTC]>), 1)nTraceback (most recent call last):n File "/usr/local/lib/python3.7/site-packages/kombu/utils/functional.py", line 42, in __call__n return self.__value__nAttributeError: 'ChannelPromise' object has no attribute '__value__'nnDuring handling of the above exception, another exception occurred:nnTraceback (most recent call last):n File "/usr/local/lib/python3.7/site-packages/kombu/transport/virtual/base.py", line 921, in create_channeln return self._avail_channels.pop()nIndexError: pop from empty listnnDuring handling of the above exception, another exception occurred:nnTraceback (most recent call last):n File "/usr/local/lib/python3.7/site-packages/airflow/executors/celery_executor.py", line 118, in send_task_to_executorn result = task.apply_async(args=[command], queue=queue)n File "/usr/local/lib/python3.7/site-packages/celery/app/task.py", line 535, in apply_asyncn **optionsn File "/usr/local/lib/python3.7/site-packages/celery/app/base.py", line 745, in send_taskn amqp.send_task_message(P, name, message, **options)n File "/usr/local/lib/python3.7/site-packages/celery/app/amqp.py", line 552, in send_task_messagen **propertiesn File "/usr/local/lib/python3.7/site-packages/kombu/messaging.py", line 181, in publishn exchange_name, declare,n File "/usr/local/lib/python3.7/site-packages/kombu/connection.py", line 518, in _ensuredn return fun(*args, **kwargs)n File "/usr/local/lib/python3.7/site-packages/kombu/messaging.py", line 187, in _publishn channel = self.channeln File "/usr/local/lib/python3.7/site-packages/kombu/messaging.py", line 209, in _get_channeln channel = self._channel = channel()n File "/usr/local/lib/python3.7/site-packages/kombu/utils/functional.py", line 44, in __call__n value = self.__value__ = self.__contract__()n File "/usr/local/lib/python3.7/site-packages/kombu/messaging.py", line 224, in <lambda>n channel = ChannelPromise(lambda: connection.default_channel)n File "/usr/local/lib/python3.7/site-packages/kombu/connection.py", line 866, in default_channeln self.ensure_connection(**conn_opts)n File "/usr/local/lib/python3.7/site-packages/kombu/connection.py", line 430, in ensure_connectionn callback, timeout=timeout)n File "/usr/local/lib/python3.7/site-packages/kombu/utils/functional.py", line 343, in retry_over_timen return fun(*args, **kwargs)n File "/usr/local/lib/python3.7/site-packages/kombu/connection.py", line 283, in connectn return self.connectionn File "/usr/local/lib/python3.7/site-packages/kombu/connection.py", line 837, in connectionn self._connection = self._establish_connection()n File "/usr/local/lib/python3.7/site-packages/kombu/connection.py", line 792, in _establish_connectionn conn = self.transport.establish_connection()n File "/usr/local/lib/python3.7/site-packages/kombu/transport/virtual/base.py", line 941, in establish_connectionn self._avail_channels.append(self.create_channel(self))n File "/usr/local/lib/python3.7/site-packages/kombu/transport/virtual/base.py", line 923, in create_channeln channel = self.Channel(connection)n File "/usr/local/lib/python3.7/site-packages/kombu/transport/redis.py", line 521, in __init__n self.client.ping()n File "/usr/local/lib/python3.7/site-packages/redis/client.py", line 1351, in pingn return self.execute_command('PING')n File "/usr/local/lib/python3.7/site-packages/redis/client.py", line 875, in execute_commandn conn = self.connection or pool.get_connection(command_name, **options)n File "/usr/local/lib/python3.7/site-packages/redis/connection.py", line 1185, in get_connectionn connection.connect()n File "/usr/local/lib/python3.7/site-packages/redis/connection.py", line 552, in connectn sock = self._connect()n File "/usr/local/lib/python3.7/site-packages/redis/connection.py", line 845, in _connectn sock = super(SSLConnection, self)._connect()n File "/usr/local/lib/python3.7/site-packages/redis/connection.py", line 579, in _connectn socket.SOCK_STREAM):n File "/usr/lib64/python3.7/socket.py", line 748, in getaddrinfon for res in _socket.getaddrinfo(host, port, family, type, proto, flags):n File "/usr/local/lib/python3.7/site-packages/airflow/utils/timeout.py", line 43, in handle_timeoutn raise AirflowTaskTimeout(self.error_message)nairflow.exceptions.AirflowTaskTimeout: Timeout, PID: 16001nn"}
配置文件
[core]
# The home folder for airflow, default is ~/airflow
airflow_home = /home/ec2-user/airflow
# The folder where your airflow pipelines live, most likely a
# subfolder in a code repository
# This path must be absolute
dags_folder = /home/ec2-user/airflow/dags
# The folder where airflow should store its log files
# This path must be absolute
base_log_folder = /var/log/airflow
# Logging level
logging_level = DEBUG
fab_logging_level = WARN
# Logging class
# Specify the class that will specify the logging configuration
# This class has to be on the python classpath
# logging_config_class = my.path.default_local_settings.LOGGING_CONFIG
logging_config_class = log_config.CUSTOM_LOGGING_CONFIG
# Log format
# we need to escape the curly braces by adding an additional curly brace
log_format = [%%(asctime)s] {%%(filename)s:%%(lineno)d} %%(levelname)s - %%(message)s
simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s
# Log filename format
# we need to escape the curly braces by adding an additional curly brace
log_filename_template = {{ ti.dag_id }}/{{ ti.task_id }}/{{ ts }}/{{ try_number }}.log
log_processor_filename_template = {{ filename }}.log
# Hostname by providing a path to a callable, which will resolve the hostname
hostname_callable = socket:getfqdn
# Default timezone in case supplied date times are naive
# can be utc (default), system, or any IANA timezone string (e.g. Europe/Amsterdam)
default_timezone = utc
# Airflow can store logs remotely in AWS S3 or Google Cloud Storage. Users
# must supply a remote location URL (starting with either 's3://...' or
# 'gs://...') and an Airflow connection id that provides access to the storage
# location.
remote_base_log_folder =
remote_log_conn_id =
# Use server-side encryption for logs stored in S3
encrypt_s3_logs = False
# DEPRECATED option for remote log storage, use remote_base_log_folder instead!
s3_log_folder =
# The executor class that airflow should use. Choices include
# SequentialExecutor, LocalExecutor, CeleryExecutor
executor = CeleryExecutor
broker_url = redis://***************************************:6379/0
# The SqlAlchemy connection string to the metadata database.
# SqlAlchemy supports many different database engine, more information
# their website
sql_alchemy_conn = mysql://***************************************:3306/airflow
# The SqlAlchemy pool size is the maximum number of database connections
# in the pool.
sql_alchemy_pool_size = 5
# The SqlAlchemy pool recycle is the number of seconds a connection
# can be idle in the pool before it is invalidated. This config does
# not apply to sqlite.
sql_alchemy_pool_recycle = 2000
# The amount of parallelism as a setting to the executor. This defines
# the max number of task instances that should run simultaneously
# on this airflow installation
parallelism = 32
# The number of task instances allowed to run concurrently by the scheduler
dag_concurrency = 6
# Are DAGs paused by default at creation
dags_are_paused_at_creation = False
# When not using pools, tasks are run in the "default pool",
# whose size is guided by this config element
non_pooled_task_slot_count = 128
# The maximum number of active DAG runs per DAG
max_active_runs_per_dag = 16
# Whether to load the examples that ship with Airflow. It's good to
# get started, but you probably want to set this to False in a production
# environment
load_examples = False
# Where your Airflow plugins are stored
plugins_folder = /home/ec2-user/airflow/plugins
# Secret key to save connection passwords in the db
fernet_key =
# Whether to disable pickling dags
donot_pickle = False
# How long before timing out a python file import while filling the DagBag
dagbag_import_timeout = 30
# The class to use for running task instances in a subprocess
task_runner = BashTaskRunner
# If set, tasks without a `run_as_user` argument will be run with this user
# Can be used to de-elevate a sudo user running Airflow when executing tasks
default_impersonation =
# What security module to use (for example kerberos):
security =
# Turn unit test mode on (overwrites many configuration options with test
# values at runtime)
unit_test_mode = False
# full path of dag_processor_manager logfile
dag_processor_manager_log_location = /var/log/airflow/dag_processor_manager/dag_processor_manager.log
# Name of handler to read task instance logs.
# Default to use task handler.
task_log_reader = task
# Whether to enable pickling for xcom (note that this is insecure and allows for
# RCE exploits). This will be deprecated in Airflow 2.0 (be forced to False).
enable_xcom_pickling = True
# When a task is killed forcefully, this is the amount of time in seconds that
# it has to cleanup after it is sent a SIGTERM, before it is SIGKILLED
killed_task_cleanup_time = 60
# Whether to override params with dag_run.conf. If you pass some key-value pairs through `airflow backfill -c` or
# `airflow trigger_dag -c`, the key-value pairs will override the existing ones in params.
dag_run_conf_overrides_params = False
[cli]
# In what way should the cli access the API. The LocalClient will use the
# database directly, while the json_client will use the api running on the
# webserver
api_client = airflow.api.client.local_client
##endpoint_url = http://localhost:8080
endpoint_url = 10.136.119.91
[api]
# How to authenticate users of the API
#auth_backend = airflow.api.auth.backend.default
[lineage]
# what lineage backend to use
#backend =
[atlas]
sasl_enabled = False
host =
port = 21000
username =
password =
[operators]
# The default owner assigned to each new operator, unless
# provided explicitly or passed via `default_args`
default_owner = Airflow
default_cpus = 1
default_ram = 512
default_disk = 512
default_gpus = 0
[webserver]
# The base url of your website as airflow cannot guess what domain or
# cname you are using. This is used in automated emails that
# airflow sends to point links to the right web server
#base_url =
# The ip specified when starting the web server
web_server_host = 0.0.0.0
# The port on which to run the web server
web_server_port = 8080
# Paths to the SSL certificate and key for the web server. When both are
# provided SSL will be enabled. This does not change the web server port.
web_server_ssl_cert = /etc/ssl/certs/airflow-selfsigned.crt
web_server_ssl_key = /etc/ssl/private/airflow-selfsigned.key
# Number of seconds the webserver waits before killing gunicorn master that doesn't respond
web_server_master_timeout = 1200
# Number of seconds the gunicorn webserver waits before timing out on a worker
web_server_worker_timeout = 1200
# Number of workers to refresh at a time. When set to 0, worker refresh is
# disabled. When nonzero, airflow periodically refreshes webserver workers by
# bringing up new ones and killing old ones.
worker_refresh_batch_size = 1
# Number of seconds to wait before refreshing a batch of workers.
worker_refresh_interval = 30
# Secret key used to run your flask app
secret_key = temporary_key
# Number of workers to run the Gunicorn web server
workers = 4
# The worker class gunicorn should use. Choices include
# sync (default), eventlet, gevent
worker_class = sync
# Log files for the gunicorn webserver. '-' means log to stderr.
access_logfile = /var/log/airflow/gunicorn-access.log
error_logfile = /var/log/airflow/gunicorn-error.log
# Expose the configuration file in the web server
expose_config = False
# Set to true to turn on authentication:
# http://pythonhosted.org/airflow/security.html#web-authentication
authenticate = True
auth_backend = airflow.contrib.auth.backends.password_auth
# Filter the list of dags by owner name (requires authentication to be enabled)
filter_by_owner = False
# Filtering mode. Choices include user (default) and ldapgroup.
# Ldap group filtering requires using the ldap backend
#
# Note that the ldap server needs the "memberOf" overlay to be set up
# in order to user the ldapgroup mode.
owner_mode = user
# Default DAG orientation. Valid values are:
# LR (Left->Right), TB (Top->Bottom), RL (Right->Left), BT (Bottom->Top)
dag_orientation = LR
# Puts the webserver in demonstration mode; blurs the names of Operators for
# privacy.
demo_mode = False
# The amount of time (in secs) webserver will wait for initial handshake
# while fetching logs from other worker machine
log_fetch_timeout_sec = 5
# By default, the webserver shows paused DAGs. Flip this to hide paused
# DAGs by default
hide_paused_dags_by_default = False
# Consistent page size across all listing views in the UI
page_size = 100
# Use FAB-based webserver with RBAC feature
rbac = True
# Define the color of navigation bar
navbar_color = #007A87
# Default dagrun to show in UI
default_dag_run_display_number = 25
[email]
email_backend = airflow.utils.email.send_email_smtp
[smtp]
# If you want airflow to send emails on retries, failure, and you want to use
# the airflow.utils.email.send_email_smtp function, you have to configure an
# smtp server here
smtp_host = localhost
smtp_starttls = True
smtp_ssl = False
smtp_port = 25
smtp_mail_from =
[celery]
# This section only applies if you are using the CeleryExecutor in
# [core] section above
# The app name that will be used by celery
celery_app_name = airflow.executors.celery_executor
# The concurrency that will be used when starting workers with the
# "airflow worker" command. This defines the number of task instances that
# a worker will take, so size up your workers based on the resources on
# your worker box and the nature of your tasks
worker_concurrency = 16
# When you start an airflow worker, airflow starts a tiny web server
# subprocess to serve the workers local log files to the airflow main
# web server, who then builds pages and sends them to users. This defines
# the port on which the logs are served. It needs to be unused, and open
# visible from the main web server to connect into the workers.
worker_log_server_port = 8793
# The Celery broker URL. Celery supports RabbitMQ, Redis and experimentally
# a sqlalchemy database. Refer to the Celery documentation for more
# information.
broker_url = redis://***************************************:6379/0
celery_result_backend = db+mysql://***************************************:3306/airflow
# Another key Celery setting
result_backend = db+mysql://***************************************:3306/airflow
# Celery Flower is a sweet UI for Celery. Airflow has a shortcut to start
# it `airflow flower`. This defines the IP that Celery Flower runs on
flower_host = 0.0.0.0
# This defines the port that Celery Flower runs on
flower_port = 8443
# Default queue that tasks get assigned to and that worker listen on.
default_queue = default
# Import path for celery configuration options
celery_config_options = airflow.config_templates.default_celery.DEFAULT_CELERY_CONFIG
# In case of using SSL
ssl_active = True
ssl_key = /etc/ssl/private/airflow-selfsigned.key
ssl_cert = /etc/ssl/certs/airflow-selfsigned.crt
ssl_cacert =
[celery_broker_transport_options]
# This section is for specifying options which can be passed to the
# underlying celery broker transport. See:
# http://docs.celeryproject.org/en/latest/userguide/configuration.html#std:setting-broker_transport_options
# The visibility timeout defines the number of seconds to wait for the worker
# to acknowledge the task before the message is redelivered to another worker.
# Make sure to increase the visibility timeout to match the time of the longest
# ETA you're planning to use.
#
# visibility_timeout is only supported for Redis and SQS celery brokers.
# See:
# http://docs.celeryproject.org/en/master/userguide/configuration.html#std:setting-broker_transport_options
#
#visibility_timeout = 21600
[scheduler]
# Task instances listen for external kill signal (when you clear tasks
# from the CLI or the UI), this defines the frequency at which they should
# listen (in seconds).
job_heartbeat_sec = 5
# The scheduler constantly tries to trigger new tasks (look at the
# scheduler section in the docs for more information). This defines
# how often the scheduler should run (in seconds).
scheduler_heartbeat_sec = 5
# after how much time should the scheduler terminate in seconds
# -1 indicates to run continuously (see also num_runs)
run_duration = -1
# after how much time a new DAGs should be picked up from the filesystem
min_file_process_interval = 0
# How many seconds to wait between file-parsing loops to prevent the logs from being spammed.
min_file_parsing_loop_time = 1
dag_dir_list_interval = 300
# How often should stats be printed to the logs
print_stats_interval = 30
child_process_log_directory = /var/log/airflow/scheduler
# Local task jobs periodically heartbeat to the DB. If the job has
# not heartbeat in this many seconds, the scheduler will mark the
# associated task instance as failed and will re-schedule the task.
scheduler_zombie_task_threshold = 300
# Turn off scheduler catchup by setting this to False.
# Default behavior is unchanged and
# Command Line Backfills still work, but the scheduler
# will not do scheduler catchup if this is False,
# however it can be set on a per DAG basis in the
# DAG definition (catchup)
catchup_by_default = True
# This changes the batch size of queries in the scheduling main loop.
# If this is too high, SQL query performance may be impacted by one
# or more of the following:
# - reversion to full table scan
# - complexity of query predicate
# - excessive locking
#
# Additionally, you may hit the maximum allowable query length for your db.
#
# Set this to 0 for no limit (not advised)
max_tis_per_query = 512
# Statsd (https://github.com/etsy/statsd) integration settings
statsd_on = True
statsd_host = localhost
statsd_port = 8125
statsd_prefix = airflow
# The scheduler can run multiple threads in parallel to schedule dags.
# This defines how many threads will run. However airflow will never
# use more threads than the amount of cpu cores available.
max_threads = 4
authenticate = False
[mesos]
# Mesos master address which MesosExecutor will connect to.
master = localhost:5050
# The framework name which Airflow scheduler will register itself as on mesos
framework_name = Airflow
# Number of cpu cores required for running one task instance using
# 'airflow run <dag_id> <task_id> <execution_date> --local -p <pickle_id>'
# command on a mesos slave
task_cpu = 1
# Memory in MB required for running one task instance using
# 'airflow run <dag_id> <task_id> <execution_date> --local -p <pickle_id>'
# command on a mesos slave
task_memory = 256
# Enable framework checkpointing for mesos
# See http://mesos.apache.org/documentation/latest/slave-recovery/
checkpoint = False
# Failover timeout in milliseconds.
# When checkpointing is enabled and this option is set, Mesos waits
# until the configured timeout for
# the MesosExecutor framework to re-register after a failover. Mesos
# shuts down running tasks if the
# MesosExecutor framework fails to re-register within this timeframe.
# failover_timeout = 604800
# Enable framework authentication for mesos
# See http://mesos.apache.org/documentation/latest/configuration/
authenticate = False
# Mesos credentials, if authentication is enabled
# default_principal = admin
# default_secret = admin
[admin]
# UI to hide sensitive variable fields when set to True
hide_sensitive_variable_fields = True
你能帮吗
1-通常您的芹菜错误日志会出现在您的调度程序日志中，因此最好在那里进行检查。如果您没有将其作为后台进程或后台进程运行，您可以在终端中详细了解问题的具体原因。
2-正如我快速查看的那样(您的配置(，它看起来非常像默认的配置文件或与在线安装指南几乎匹配的配置===>所以肯定没有什么大问题。
3-你的错误尚不清楚，但我相信，如果你刚刚发货，这是一个突然的错误，在那之后，你很有可能会面临你的经纪人许可错误或任何相关错误，因为芹菜会与之沟通。所以我会为一个常见问题提供一个通用的解决方案，希望它能帮助社区，因为你已经远远超过了3个月前的问题：(
rabbitmqctl set_permissions -p /myvhost guest ".*" ".*" ".*"
guest=您的用户(您作为代理(在本例中为RabbitMQ(用户提供的内容(
/myvhost=对你来说，它可能只是斜线或/
祝你好运。