Airflow + Cluster + Celery + SQS - Airflow Worker：'Hub'对象没有属性'_current_http_client'

Question

我正在尝试群集我的Airflow设置，而我正在使用this article这样做。我只是将我的airflow.cfg文件配置为使用CeleryExecutor，我将sql_alchemy_conn指向我在同一主节点上运行的postgresql数据库，我已将broker_url设置为使用AWS SQS（我没有设置access_key_id或secret_key，因为它在EC2-Instance上运行它不需要那些），我也将celery_result_backend设置为我的postgresql服务器。我保存了我的新airflow.cfg更改，我运行了airflow initdb，然后我运行了airflow scheduler。

我访问了用户界面并打开了我的一个测试DAG，它进入了queued灰色状态，

为了更轻松地测试我的设置，我现在只在一台服务器上执行所有操作，一旦我使服务器工作，我将把其他节点添加到集群中。所以我开始使用airflow worker启动一个气流工作者，我得到一个错误，说我没有安装pycurl。所以我使用

安装pycurl

sudo yum install libcurl-devel
export PYCURL_SSL_LIBRARY=openssl
       note: other options include [nss|openssl|ssl|gnutls]
sudo pip-3.6 install pycurl
sudo yum install -y openssl-devel

然后我重新命令airflow worker，我收到了这个错误，

[ec2-user@ip-10-185-150-32 ~]$ airflow worker
[2018-06-08 15:36:28,653] {configuration.py:206} WARNING - section/key [celery/celery_ssl_active] not found in config
[2018-06-08 15:36:28,653] {default_celery.py:41} WARNING - Celery Executor will run without SSL
[2018-06-08 15:36:28,654] {__init__.py:45} INFO - Using executor CeleryExecutor

 -------------- celery@ip-10-185-150-32 v4.1.1 (latentcall)
---- **** -----
--- * ***  * -- Linux-4.9.76-3.78.amzn1.x86_64-x86_64-with-glibc2.3.4 2018-06-08 15:36:28
-- * - **** ---
- ** ---------- [config]
- ** ---------- .> app:         airflow.executors.celery_executor:0x7f1d528c39b0
- ** ---------- .> transport:   sqs://localhost//
- ** ---------- .> results:     postgresql://postgres:**@localhost/datalake_airflow_cluster_v1_master1_database_1
- *** --- * --- .> concurrency: 16 (prefork)
-- ******* ---- .> task events: OFF (enable -E to monitor tasks in this worker)
--- ***** -----
 -------------- [queues]
                .> default          exchange=default(direct) key=default


[2018-06-08 15:36:29,650] {configuration.py:206} WARNING - section/key [celery/celery_ssl_active] not found in config
[2018-06-08 15:36:29,650] {default_celery.py:41} WARNING - Celery Executor will run without SSL
[2018-06-08 15:36:29,651] {__init__.py:45} INFO - Using executor CeleryExecutor
Starting flask
[2018-06-08 15:36:29,774] {_internal.py:88} INFO -  * Running on http://0.0.0.0:8793/ (Press CTRL+C to quit)
[2018-06-08 15:36:29,864: CRITICAL/MainProcess] Unrecoverable error: ImportError('The curl client requires the pycurl library.',)
Traceback (most recent call last):
  File "/usr/local/lib/python3.6/site-packages/kombu/asynchronous/http/__init__.py", line 20, in get_client
    return hub._current_http_client
AttributeError: 'Hub' object has no attribute '_current_http_client'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.6/site-packages/celery/worker/worker.py", line 205, in start
    self.blueprint.start(self)
  File "/usr/local/lib/python3.6/site-packages/celery/bootsteps.py", line 119, in start
    step.start(parent)
  File "/usr/local/lib/python3.6/site-packages/celery/bootsteps.py", line 370, in start
    return self.obj.start()
  File "/usr/local/lib/python3.6/site-packages/celery/worker/consumer/consumer.py", line 316, in start
    blueprint.start(self)
  File "/usr/local/lib/python3.6/site-packages/celery/bootsteps.py", line 119, in start
    step.start(parent)
  File "/usr/local/lib/python3.6/site-packages/celery/worker/consumer/consumer.py", line 592, in start
    c.loop(*c.loop_args())
  File "/usr/local/lib/python3.6/site-packages/celery/worker/loops.py", line 91, in asynloop
    next(loop)
  File "/usr/local/lib/python3.6/site-packages/kombu/asynchronous/hub.py", line 291, in create_loop
    item()
  File "/usr/local/lib/python3.6/site-packages/vine/promises.py", line 143, in __call__
    return self.throw()
  File "/usr/local/lib/python3.6/site-packages/vine/promises.py", line 140, in __call__
    retval = fun(*final_args, **final_kwargs)
  File "/usr/local/lib/python3.6/site-packages/kombu/transport/SQS.py", line 316, in _schedule_queue
    queue, callback=promise(self._loop1, (queue,)),
  File "/usr/local/lib/python3.6/site-packages/kombu/transport/SQS.py", line 332, in _get_bulk_async
    return self._get_async(queue, maxcount, callback=callback)
  File "/usr/local/lib/python3.6/site-packages/kombu/transport/SQS.py", line 342, in _get_async
    qname, count=count, connection=self.asynsqs,
  File "/usr/local/lib/python3.6/site-packages/kombu/transport/SQS.py", line 436, in asynsqs
    region=self.region
  File "/usr/local/lib/python3.6/site-packages/kombu/asynchronous/aws/sqs/connection.py", line 27, in __init__
    **kwargs
  File "/usr/local/lib/python3.6/site-packages/kombu/asynchronous/aws/connection.py", line 178, in __init__
    **http_client_params)
  File "/usr/local/lib/python3.6/site-packages/kombu/asynchronous/aws/connection.py", line 151, in __init__
    self._httpclient = http_client or get_client()
  File "/usr/local/lib/python3.6/site-packages/kombu/asynchronous/http/__init__.py", line 22, in get_client
    client = hub._current_http_client = Client(hub, **kwargs)
  File "/usr/local/lib/python3.6/site-packages/kombu/asynchronous/http/__init__.py", line 13, in Client
    return CurlClient(hub, **kwargs)
  File "/usr/local/lib/python3.6/site-packages/kombu/asynchronous/http/curl.py", line 43, in __init__
    raise ImportError('The curl client requires the pycurl library.')
ImportError: The curl client requires the pycurl library.

我想知道的一件事是，我是否必须在某处指定AWS SQS队列名称？我没有做任何配置，我告诉Airflow / Celery在SQS中使用什么队列。它会自动创建队列吗？如果是这样，就没有从我在AWS管理控制台上看到的队列创建的队列，所以我想知道这是否是问题。我是否必须创建一个SQS队列，然后将其放在某个配置文件中？

这是我的Airflow.cfg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 = /home/ec2-user/airflow/logs

# Airflow can store logs remotely in AWS S3 or Google Cloud Storage. Users
# must supply an Airflow connection id that provides access to the storage
# location.
remote_log_conn_id =
encrypt_s3_logs = False

# Logging level
logging_level = INFO

# 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 format
log_format = [%%(asctime)s] {%%(filename)s:%%(lineno)d} %%(levelname)s - %%(message)s
simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s

# The executor class that airflow should use. Choices include
# SequentialExecutor, LocalExecutor, CeleryExecutor, DaskExecutor
#executor = SequentialExecutor
executor = CeleryExecutor

# The SqlAlchemy connection string to the metadata database.
# SqlAlchemy supports many different database engine, more information
# their website
#sql_alchemy_conn = sqlite:////home/ec2-user/airflow/airflow.db
sql_alchemy_conn = postgresql+psycopg2://postgres:$password@localhost/datalake_airflow_cluster_v1_master1_database_1

# 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 = 3600

# 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 = 16

# Are DAGs paused by default at creation
dags_are_paused_at_creation = True

# 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 = True

# Where your Airflow plugins are stored
plugins_folder = /home/ec2-user/airflow/plugins

# Secret key to save connection passwords in the db
fernet_key = ibwZ5uSASmZGphBmwdJ4BIhd1-5WZXMTTgMF9u1_dGM=

# 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

# Name of handler to read task instance logs.
# Default to use file task handler.
task_log_reader = file.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

[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

[api]
# How to authenticate users of the API
auth_backend = airflow.api.auth.backend.default

[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 = http://localhost:8080

# 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 =
web_server_ssl_key =

# Number of seconds the gunicorn webserver waits before timing out on a worker
web_server_worker_timeout = 120

# 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 = -
error_logfile = -

# 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 = False

# 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 view.  Valid values are:
# tree, graph, duration, gantt, landing_times
dag_default_view = tree

# 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

[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
# Uncomment and set the user/pass settings if you want to use SMTP AUTH
# smtp_user = airflow
# smtp_password = airflow
smtp_port = 25
smtp_mail_from = airflow@example.com


[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
celeryd_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 = sqla+mysql://airflow:airflow@localhost:3306/airflow
broker_url = sqs://

# Another key Celery setting
#celery_result_backend = db+mysql://airflow:airflow@localhost:3306/airflow
celery_result_backend = db+postgresql://postgres:$password@localhost/datalake_airflow_cluster_v1_master1_database_1

# 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 = 5555

# 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

[dask]
# This section only applies if you are using the DaskExecutor in
# [core] section above

# The IP address and port of the Dask cluster's scheduler.
cluster_address = 127.0.0.1:8786


[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

dag_dir_list_interval = 300

# How often should stats be printed to the logs
print_stats_interval = 30

child_process_log_directory = /home/ec2-user/airflow/logs/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.
# This depends on query length limits and how long you are willing to hold locks.
# 0 for no limit
max_tis_per_query = 0

# Statsd (https://github.com/etsy/statsd) integration settings
statsd_on = False
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.
max_threads = 2

authenticate = False

[ldap]
# set this to ldaps://<your.ldap.server>:<port>
uri = 
user_filter = objectClass=*
user_name_attr = uid
group_member_attr = memberOf
superuser_filter = 
data_profiler_filter = 
bind_user = cn=Manager,dc=example,dc=com
bind_password = insecure
basedn = dc=example,dc=com
cacert = /etc/ca/ldap_ca.crt
search_scope = LEVEL

[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


[kerberos]
ccache = /tmp/airflow_krb5_ccache
# gets augmented with fqdn
principal = airflow
reinit_frequency = 3600
kinit_path = kinit
keytab = airflow.keytab


[github_enterprise]
api_rev = v3


[admin]
# UI to hide sensitive variable fields when set to True
hide_sensitive_variable_fields = True

更新

我刚注意到airflow.cfg中有一个配置字段提供队列名称，所以我用我的SQS队列名称设置default_queue = myQueueName-SQS.fifo，然后运行airflow initdb但我仍然得到同样的错误。

更新2：

我刚刚注意到，当我运行airflow worker命令transport: sqs://localhost//时Celery正在显示这个，所以我认为我需要修改Celery配置文件以指向我的SQS位置。

更新

当我读到RabbitMQ error here时，我已经浪费了两天半的时间来尝试排队与Celery和Airflow（this article以及此处的SQS错误）一起工作，这说明Airbnb（ Airflow的创造者正在使用Celery和Redis作为他们的队列。所以我尝试了它，它实际上花了我三分钟做它并且它完美地工作....我所做的只是使用sudo yum install redis下载Redis然后bam我安装了Redis。我started Redis使用redis-server。然后我将airflow.cfg broker_url字段更改为broker_url = redis://，运行airflow initdb，重新启动调度程序airflow scheduler，然后启动了一个工作程序airflow worker和BAM我的DAG开始运行Redis队列和CeleryExecutor。 HALLELUJAH只是使用Redis作为你的队列....