無法訪問氣流 UI

Question

我正在創建一個氣流圖像並在 docker 容器中運行氣流。 我已將端口8080從本地機器端口轉發到氣流容器。 構建圖像時，我正在ENTRYPOINT腳本中啟動網絡服務器。 服務器運行良好，沒有顯示錯誤，但是當我嘗試使用瀏覽器從我的機器訪問 ui 時，我收到了Internal Server Error 。 關於這里可能存在什么問題的任何指示？

運行容器的命令：

docker run -p 8080:8080 --name airflow 5dd318a99d75

airflow.cfg

[core]
# The home folder for airflow, default is ~/airflow
airflow_home = /home/airflow

# The folder where your airflow pipelines live, most likely a
# subfolder in a code repository
dags_folder = /mnt/airflow/dags

# The folder where airflow should store its log files. This location
base_log_folder = /mnt/logs/airflow/logs

# 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_logging =
remote_log_conn_id =
remote_base_log_folder =

# Use server-side encryption for logs stored in S3
encrypt_s3_logs = False

# Logging level
logging_level = INFO
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 =

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

# Hostname by providing a path to a callable, which will resolve the hostname
hostname_callable = socket:getfqdn

# The SqlAlchemy connection string to the metadata database.
# SqlAlchemy supports many different database engine, more information
# their website
#sql_alchemy_conn = mysql://<user>:<pwd>@<host>:<port>/<db_name>
<mysql_db_connection>

# 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 = 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/airflow/plugins

# Secret key to save connection passwords in the db
# It will be loaded from the yaml file - AIRFLOW__CORE__FERNET_KEY
fernet_key = 46BKJoQYlPPOexq0OhDZnIlNepKFf87WFwLbfzqDDho=

# 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 = StandardTaskRunner

[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

# If you set web_server_url_prefix, do NOT forget to append it here, ex:
# endpoint_url = http://localhost:8080/myroot
# So api will look like: http://localhost:8080/myroot/api/experimental/...
endpoint_url = http://localhost:8080

[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


[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 webserver waits before killing gunicorn master that doesn't respond
web_server_master_timeout = 1200

# The time 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
# It will be loaded from the yaml file - AIRFLOW__WEBSERVER__FLASK_SECRET_KEY
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

# 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

# Define the color of navigation bar
navbar_color = #007A87

# Default dagrun to show in UI
default_dag_run_display_number = 25

# Enable werkzeug `ProxyFix` middleware
enable_proxy_fix = False

# Set secure flag on session cookie
cookie_secure = False

# Set samesite policy on session cookie
cookie_samesite =

# Set to true to turn on authentication:
# http://pythonhosted.org/airflow/installation.html#web-authentication
;authenticate = False
;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

[email]
email_backend = airflow.utils.email.send_email_smtp

[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 = 40

# 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

# Another key Celery setting
celery_result_backend = db+mysql://airflow:airflow@localhost:3306/airflow

# Celery Flower is a sweet UI for Celery. Airflow has a shortcut to start
# it `airflow flower`. 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

[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

# 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. However airflow will never
# use more threads than the amount of cpu cores available.
max_threads = 2

[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

Answer 1

問題解決了。 0.12.4版本中存在一個錯誤，根據此鏈接將其更新到0.12.4解決了該問題。