- html - 出于某种原因,IE8 对我的 Sass 文件中继承的 html5 CSS 不友好?
- JMeter 在响应断言中使用 span 标签的问题
- html - 在 :hover and :active? 上具有不同效果的 CSS 动画
- html - 相对于居中的 html 内容固定的 CSS 重复背景?
我正在创建气流图像,并在docker容器中运行气流。我已经将端口8080
从本地计算机端口转发到气流容器。构建镜像时,我正在使用ENTRYPOINT
脚本启动Web服务器。服务器运行正常,未显示任何错误,但是当我尝试使用浏览器从计算机访问ui时,得到了Internal Server Error
。关于这可能是什么问题的任何指示?
运行容器的命令:
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
最佳答案
问题解决了。 Flask版本中存在一个错误,根据this链接将其更新为0.12.4
即可解决此问题。
关于docker - 无法访问 Airflow UI,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/55270681/
我通过 spring ioc 编写了一些 Rest 应用程序。但我无法解决这个问题。这是我的异常(exception): org.springframework.beans.factory.BeanC
我对 TestNG、Spring 框架等完全陌生,我正在尝试使用注释 @Value通过 @Configuration 访问配置文件注释。 我在这里想要实现的目标是让控制台从配置文件中写出“hi”,通过
为此工作了几个小时。我完全被难住了。 这是 CS113 的实验室。 如果用户在程序(二进制计算器)结束时选择继续,我们需要使用 goto 语句来到达程序的顶部。 但是,我们还需要释放所有分配的内存。
我正在尝试使用 ffmpeg 库构建一个小的 C 程序。但是我什至无法使用 avformat_open_input() 打开音频文件设置检查错误代码的函数后,我得到以下输出: Error code:
使用 Spring Initializer 创建一个简单的 Spring boot。我只在可用选项下选择 DevTools。 创建项目后,无需对其进行任何更改,即可正常运行程序。 现在,当我尝试在项目
所以我只是在 Mac OS X 中通过 brew 安装了 qt。但是它无法链接它。当我尝试运行 brew link qt 或 brew link --overwrite qt 我得到以下信息: ton
我在提交和 pull 时遇到了问题:在提交的 IDE 中,我看到: warning not all local changes may be shown due to an error: unable
我跑 man gcc | grep "-L" 我明白了 Usage: grep [OPTION]... PATTERN [FILE]... Try `grep --help' for more inf
我有一段代码,旨在接收任何 URL 并将其从网络上撕下来。到目前为止,它运行良好,直到有人给了它这个 URL: http://www.aspensurgical.com/static/images/a
在过去的 5 个小时里,我一直在尝试在我的服务器上设置 WireGuard,但在完成所有设置后,我无法 ping IP 或解析域。 下面是服务器配置 [Interface] Address = 10.
我正在尝试在 GitLab 中 fork 我的一个私有(private)项目,但是当我按下 fork 按钮时,我会收到以下信息: No available namespaces to fork the
我这里遇到了一些问题。我是 node.js 和 Rest API 的新手,但我正在尝试自学。我制作了 REST API,使用 MongoDB 与我的数据库进行通信,我使用 Postman 来测试我的路
下面的代码在控制台中给出以下消息: Uncaught DOMException: Failed to execute 'appendChild' on 'Node': The new child el
我正在尝试调用一个新端点来显示数据,我意识到在上一组有效的数据中,它在数据周围用一对额外的“[]”括号进行控制台,我认为这就是问题是,而新端点不会以我使用数据的方式产生它! 这是 NgFor 失败的原
我正在尝试将我的 Symfony2 应用程序部署到我的 Azure Web 应用程序,但遇到了一些麻烦。 推送到远程时,我在终端中收到以下消息 remote: Updating branch 'mas
Minikube已启动并正在运行,没有任何错误,但是我无法 curl IP。我在这里遵循:https://docs.traefik.io/user-guide/kubernetes/,似乎没有提到关闭
每当我尝试docker组成任何项目时,都会出现以下错误。 我尝试过有和没有sudo 我在这台机器上只有这个问题。我可以在Mac和Amazon WorkSpace上运行相同的容器。 (myslabs)
我正在尝试 pip install stanza 并收到此消息: ERROR: No matching distribution found for torch>=1.3.0 (from stanza
DNS 解析看起来不错,但我无法 ping 我的服务。可能是什么原因? 来自集群中的另一个 Pod: $ ping backend PING backend.default.svc.cluster.l
我正在使用Hibernate 4 + Spring MVC 4当我开始 Apache Tomcat Server 8我收到此错误: Error creating bean with name 'wel
我是一名优秀的程序员,十分优秀!