由于我之前的帖子，我刚刚痛苦地安装了 Airflow 1.10。我们有一个正在运行的 ec2 实例，我们的队列是 AWS Elastic Cache Redis，我们的元数据库是 AWS RDS for PostgreSQL。当我们使用气流版本1.9时，Airflow可以很好地使用此设置。但是当我们启动调度程序时，我们在 Airflow 版本 1.10 上遇到了问题。
[2018-08-15 16:29:14,015] {jobs.py:385} INFO - Started process (PID=15778) to work on /home/ec2-user/airflow/dags/myDag.py
[2018-08-15 16:29:14,055] {jobs.py:1782} INFO - Processing file /home/ec2-user/airflow/dags/myDag.py for tasks to queue
[2018-08-15 16:29:14,055] {logging_mixin.py:95} INFO - [2018-08-15 16:29:14,055] {models.py:258} INFO - Filling up the DagBag from /home/ec2-user/airflow/dags/myDag.py
[2018-08-15 16:29:20,417] {jobs.py:396} ERROR - Got an exception! Propagating...
Traceback (most recent call last):
File "<frozen importlib._bootstrap>", line 665, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 674, in exec_module
File "<frozen importlib._bootstrap_external>", line 779, in get_code
File "<frozen importlib._bootstrap_external>", line 487, in _compile_bytecode
File "/usr/local/lib/python3.6/site-packages/airflow/bin/cli.py", line 85, in sigint_handler
sys.exit(0)
SystemExit: 0
Traceback (most recent call last):
File "/usr/local/lib/python3.6/site-packages/celery/backends/database/__init__.py", line 53, in _inner
return fun(*args, **kwargs)
File "/usr/local/lib/python3.6/site-packages/celery/backends/database/__init__.py", line 122, in _get_task_meta_for
session = self.ResultSession()
File "/usr/local/lib/python3.6/site-packages/celery/backends/database/__init__.py", line 99, in ResultSession
**self.engine_options)
File "/usr/local/lib/python3.6/site-packages/celery/backends/database/session.py", line 60, in session_factory
self.prepare_models(engine)
File "/usr/local/lib/python3.6/site-packages/celery/backends/database/session.py", line 55, in prepare_models
ResultModelBase.metadata.create_all(engine)
File "/usr/local/lib64/python3.6/site-packages/sqlalchemy/sql/schema.py", line 3949, in create_all
tables=tables)
File "/usr/local/lib64/python3.6/site-packages/sqlalchemy/engine/base.py", line 1928, in _run_visitor
with self._optional_conn_ctx_manager(connection) as conn:
File "/usr/lib64/python3.6/contextlib.py", line 81, in __enter__
return next(self.gen)
File "/usr/local/lib64/python3.6/site-packages/sqlalchemy/engine/base.py", line 1921, in _optional_conn_ctx_manager
with self.contextual_connect() as conn:
File "/usr/local/lib64/python3.6/site-packages/sqlalchemy/engine/base.py", line 2112, in contextual_connect
self._wrap_pool_connect(self.pool.connect, None),
File "/usr/local/lib64/python3.6/site-packages/sqlalchemy/engine/base.py", line 2151, in _wrap_pool_connect
e, dialect, self)
File "/usr/local/lib64/python3.6/site-packages/sqlalchemy/engine/base.py", line 1465, in _handle_dbapi_exception_noconnection
exc_info
File "/usr/local/lib64/python3.6/site-packages/sqlalchemy/util/compat.py", line 203, in raise_from_cause
reraise(type(exception), exception, tb=exc_tb, cause=cause)
File "/usr/local/lib64/python3.6/site-packages/sqlalchemy/util/compat.py", line 186, in reraise
raise value.with_traceback(tb)
File "/usr/local/lib64/python3.6/site-packages/sqlalchemy/engine/base.py", line 2147, in _wrap_pool_connect
return fn()
File "/usr/local/lib64/python3.6/site-packages/sqlalchemy/pool.py", line 387, in connect
return _ConnectionFairy._checkout(self)
File "/usr/local/lib64/python3.6/site-packages/sqlalchemy/pool.py", line 768, in _checkout
fairy = _ConnectionRecord.checkout(pool)
File "/usr/local/lib64/python3.6/site-packages/sqlalchemy/pool.py", line 516, in checkout
rec = pool._do_get()
File "/usr/local/lib64/python3.6/site-packages/sqlalchemy/pool.py", line 1231, in _do_get
return self._create_connection()
File "/usr/local/lib64/python3.6/site-packages/sqlalchemy/pool.py", line 333, in _create_connection
return _ConnectionRecord(self)
File "/usr/local/lib64/python3.6/site-packages/sqlalchemy/pool.py", line 461, in __init__
self.__connect(first_connect_check=True)
File "/usr/local/lib64/python3.6/site-packages/sqlalchemy/pool.py", line 651, in __connect
connection = pool._invoke_creator(self)
File "/usr/local/lib64/python3.6/site-packages/sqlalchemy/engine/strategies.py", line 105, in connect
return dialect.connect(*cargs, **cparams)
File "/usr/local/lib64/python3.6/site-packages/sqlalchemy/engine/default.py", line 393, in connect
return self.dbapi.connect(*cargs, **cparams)
File "/usr/local/lib64/python3.6/site-packages/MySQLdb/__init__.py", line 85, in Connect
return Connection(*args, **kwargs)
File "/usr/local/lib64/python3.6/site-packages/MySQLdb/connections.py", line 204, in __init__
super(Connection, self).__init__(*args, **kwargs2)
sqlalchemy.exc.OperationalError: (_mysql_exceptions.OperationalError) (2002, "Can't connect to local MySQL server through socket '/var/lib/mysql/mysql.sock' (2)")
请注意，网络服务器(airflow webserver(和worker(airflow worker(启动正常，没有错误。调度程序(airflow scheduler(甚至可以正常启动，如果我们在dags文件夹中没有任何DAG。但是一旦我们将任何DAG添加到dags文件夹中并重新启动调度程序，就会出现此错误。
我们已经尝试从这篇文章和这篇文章中安装一百万个不同的 python 模块，但似乎没有任何效果。
这是我们的完整airflow.cfg文件：
[core]
# The home folder for airflow, default is ~/airflow
airflow_home = $AIRFLOW_HOME
hostname_callable=10.185.143.177:10.185.143.177
# The folder where your airflow pipelines live, most likely a
# subfolder in a code repository
# This path must be absolute
dags_folder = $AIRFLOW_HOME/dags
# The folder where airflow should store its log files
# This path must be absolute
base_log_folder = $AIRFLOW_HOME/log
# 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:////var/lib/airflow/airflow.db
sql_alchemy_conn = postgresql+psycopg2://foobar:password@airflowdb.us-east-1.rds.amazonaws.com:5432/blah
# 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
dag_concurrency = 32
# 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
max_active_runs_per_dag = 32
# 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 = /var/lib/airflow/plugins
# Secret key to save connection passwords in the db
fernet_key = thisLooksSensitive!
# 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
authenticate = True
auth_backend = airflow.contrib.auth.backends.ldap_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 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
celeryd_concurrency = 32
# 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.us-east-1.amazonaws.com/235324643256/-airflow-master-queue.fifo
broker_url = redis://asdfg.asdf.0001.use1.cache.amazonaws.com
# Another key Celery setting
#celery_result_backend = db+mysql://airflow:airflow@localhost:3306/airflow
celery_result_backend = db+postgresql://foobar:password@airflowdb.us-east-1.rds.amazonaws.com:5432/blah
# 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
default_queue = foobar.fifo
# 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 = $AIRFLOW_HOME/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]
# secret...
[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]
# secret...
[github_enterprise]
api_rev = v3
[admin]
# UI to hide sensitive variable fields when set to True
hide_sensitive_variable_fields = True
更新：
我们使用的是Python 3.6，并且不断看到MySqlPython包仅适用于Python 2.x。我不确定这是否是问题所在。这是否意味着Airflow 1.10仅适用于Python 2.x？但是我们已经安装了显然适用于Python 3.x的mysqlclient包？这非常令人困惑正在发生的事情。我们甚至不明白为什么Airflow需要MySQL，如果我们使用PostgreSQL。
为了使气流的配置与芹菜兼容，一些属性 已重命名。
celeryd_concurrency -> worker_concurrency
celery_result_backend -> result_backend
celery_ssl_active -> ssl_active
celery_ssl_cert -> ssl_cert
celery_ssl_key -> ssl_key
产生与 Celery 4 相同的配置参数，具有更高的透明度。
更多更新请参阅此处。气流更新