dags_are_paused_at_creation = False AND is_paused_upon_creation param are not working

Here is my airflow.cfg
[core]
# The folder where your airflow pipelines live, most likely a
# subfolder in a code repository. This path must be absolute.
dags_folder = /home/user/airflow/dags

# Hostname by providing a path to a callable, which will resolve the hostname.
# The format is "package.function".
#
# For example, default value "airflow.utils.net.getfqdn" means that result from patched
# version of socket.getfqdn() - see https://github.com/python/cpython/issues/49254.
#
# No argument should be required in the function specified.
# If using IP address as hostname is preferred, use value ``airflow.utils.net.get_host_ip_address``
hostname_callable = airflow.utils.net.getfqdn

# A callable to check if a python file has airflow dags defined or not
# with argument as: `(file_path: str, zip_file: zipfile.ZipFile | None = None)`
# return True if it has dags otherwise False
# If this is not provided, Airflow uses its own heuristic rules.
might_contain_dag_callable = airflow.utils.file.might_contain_dag_via_default_heuristic

# 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
default_timezone = Asia/Shanghai

# The executor class that airflow should use. Choices include
# ``SequentialExecutor``, ``LocalExecutor``, ``CeleryExecutor``, ``DaskExecutor``,
# ``KubernetesExecutor``, ``CeleryKubernetesExecutor`` or the
# full import path to the class when using a custom executor.
#executor = SequentialExecutor
executor = LocalExecutor

# This defines the maximum number of task instances that can run concurrently per scheduler in
# Airflow, regardless of the worker count. Generally this value, multiplied by the number of
# schedulers in your cluster, is the maximum number of task instances with the running
# state in the metadata database.
parallelism = 32

# The maximum number of task instances allowed to run concurrently in each DAG. To calculate
# the number of tasks that is running concurrently for a DAG, add up the number of running
# tasks for all DAG runs of the DAG. This is configurable at the DAG level with ``max_active_tasks``,
# which is defaulted as ``max_active_tasks_per_dag``.
#
# An example scenario when this would be useful is when you want to stop a new dag with an early
# start date from stealing all the executor slots in a cluster.
max_active_tasks_per_dag = 16

# Are DAGs paused by default at creation
dags_are_paused_at_creation = False

# The maximum number of active DAG runs per DAG. The scheduler will not create more DAG runs
# if it reaches the limit. This is configurable at the DAG level with ``max_active_runs``,
# which is defaulted as ``max_active_runs_per_dag``.
max_active_runs_per_dag = 16

# The name of the method used in order to start Python processes via the multiprocessing module.
# This corresponds directly with the options available in the Python docs:
# https://docs.python.org/3/library/multiprocessing.html#multiprocessing.set_start_method.
# Must be one of the values returned by:
# https://docs.python.org/3/library/multiprocessing.html#multiprocessing.get_all_start_methods.
# Example: mp_start_method = fork
# mp_start_method =

# Whether to load the DAG 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
load_examples = False

# Path to the folder containing Airflow plugins
plugins_folder = /home/user/airflow/plugins

# Should tasks be executed via forking of the parent process ("False",
# the speedier option) or by spawning a new python process ("True" slow,
# but means plugin changes picked up by tasks straight away)
execute_tasks_new_python_interpreter = False

# Secret key to save connection passwords in the db
fernet_key =

# Whether to disable pickling dags
donot_pickle = True

# How long before timing out a python file import
dagbag_import_timeout = 30.0

# Should a traceback be shown in the UI for dagbag import errors,
# instead of just the exception message
dagbag_import_error_tracebacks = True

# If tracebacks are shown, how many entries from the traceback should be shown
dagbag_import_error_traceback_depth = 2

# How long before timing out a DagFileProcessor, which processes a dag file
dag_file_processor_timeout = 50

# The class to use for running task instances in a subprocess.
# Choices include StandardTaskRunner, CgroupTaskRunner or the full import path to the class
# when using a custom task runner.
task_runner = StandardTaskRunner

# 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

# Whether to enable pickling for xcom (note that this is insecure and allows for
# RCE exploits).
enable_xcom_pickling = False

# What classes can be imported during deserialization. This is a multi line value.
# The individual items will be parsed as regexp. Python built-in classes (like dict)
# are always allowed. Bare "." will be replaced so you can set airflow.* .
allowed_deserialization_classes = airflow\..*

# 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 dags backfill -c`` or
# ``airflow dags trigger -c``, the key-value pairs will override the existing ones in params.
dag_run_conf_overrides_params = True

# If enabled, Airflow will only scan files containing both ``DAG`` and ``airflow`` (case-insensitive).
dag_discovery_safe_mode = True

# The pattern syntax used in the ".airflowignore" files in the DAG directories. Valid values are
# ``regexp`` or ``glob``.
dag_ignore_file_syntax = regexp

# The number of retries each task is going to have by default. Can be overridden at dag or task level.
default_task_retries = 0

# The number of seconds each task is going to wait by default between retries. Can be overridden at
# dag or task level.
default_task_retry_delay = 300

# The maximum delay (in seconds) each task is going to wait by default between retries.
# This is a global setting and cannot be overridden at task or DAG level.
max_task_retry_delay = 86400

# The weighting method used for the effective total priority weight of the task
default_task_weight_rule = downstream

# The default task execution_timeout value for the operators. Expected an integer value to
# be passed into timedelta as seconds. If not specified, then the value is considered as None,
# meaning that the operators are never timed out by default.
default_task_execution_timeout =

# Updating serialized DAG can not be faster than a minimum interval to reduce database write rate.
min_serialized_dag_update_interval = 30

# If True, serialized DAGs are compressed before writing to DB.
# Note: this will disable the DAG dependencies view
compress_serialized_dags = False

# Fetching serialized DAG can not be faster than a minimum interval to reduce database
# read rate. This config controls when your DAGs are updated in the Webserver
min_serialized_dag_fetch_interval = 10

# Maximum number of Rendered Task Instance Fields (Template Fields) per task to store
# in the Database.
# All the template_fields for each of Task Instance are stored in the Database.
# Keeping this number small may cause an error when you try to view ``Rendered`` tab in
# TaskInstance view for older tasks.
max_num_rendered_ti_fields_per_task = 30

# On each dagrun check against defined SLAs
check_slas = True

# Path to custom XCom class that will be used to store and resolve operators results
# Example: xcom_backend = path.to.CustomXCom
xcom_backend = airflow.models.xcom.BaseXCom

# By default Airflow plugins are lazily-loaded (only loaded when required). Set it to ``False``,
# if you want to load plugins whenever 'airflow' is invoked via cli or loaded from module.
#lazy_load_plugins = True
lazy_load_plugins = False

# By default Airflow providers are lazily-discovered (discovery and imports happen only when required).
# Set it to False, if you want to discover providers whenever 'airflow' is invoked via cli or
# loaded from module.
lazy_discover_providers = True

# Hide sensitive Variables or Connection extra json keys from UI and task logs when set to True
#
# (Connection passwords are always hidden in logs)
hide_sensitive_var_conn_fields = True

# A comma-separated list of extra sensitive keywords to look for in variables names or connection's
# extra JSON.
sensitive_var_conn_names =

# Task Slot counts for ``default_pool``. This setting would not have any effect in an existing
# deployment where the ``default_pool`` is already created. For existing deployments, users can
# change the number of slots using Webserver, API or the CLI
default_pool_task_slot_count = 128

# The maximum list/dict length an XCom can push to trigger task mapping. If the pushed list/dict has a
# length exceeding this value, the task pushing the XCom will be failed automatically to prevent the
# mapped tasks from clogging the scheduler.
max_map_length = 1024

# The default umask to use for process when run in daemon mode (scheduler, worker,  etc.)
#
# This controls the file-creation mode mask which determines the initial value of file permission bits
# for newly created files.
#
# This value is treated as an octal-integer.
daemon_umask = 0o077

# Class to use as dataset manager.
# Example: dataset_manager_class = airflow.datasets.manager.DatasetManager
# dataset_manager_class =

# Kwargs to supply to dataset manager.
# Example: dataset_manager_kwargs = {{"some_param": "some_value"}}
# dataset_manager_kwargs =

[database]
# The SqlAlchemy connection string to the metadata database.
# SqlAlchemy supports many different database engines.
# More information here:
# http://airflow.apache.org/docs/apache-airflow/stable/howto/set-up-database.html#database-uri
sql_alchemy_conn = mysql+mysqldb://airflow_user:airflow_pass@localhost:3306/airflow_db 

# Extra engine specific keyword args passed to SQLAlchemy's create_engine, as a JSON-encoded value
# Example: sql_alchemy_engine_args = {{"arg1": True}}
# sql_alchemy_engine_args =

# The encoding for the databases
sql_engine_encoding = utf-8

# Collation for ``dag_id``, ``task_id``, ``key``, ``external_executor_id`` columns
# in case they have different encoding.
# By default this collation is the same as the database collation, however for ``mysql`` and ``mariadb``
# the default is ``utf8mb3_bin`` so that the index sizes of our index keys will not exceed
# the maximum size of allowed index when collation is set to ``utf8mb4`` variant
# (see https://github.com/apache/airflow/pull/17603#issuecomment-901121618).
# sql_engine_collation_for_ids =

# If SqlAlchemy should pool database connections.
sql_alchemy_pool_enabled = True

# The SqlAlchemy pool size is the maximum number of database connections
# in the pool. 0 indicates no limit.
sql_alchemy_pool_size = 5

# The maximum overflow size of the pool.
# When the number of checked-out connections reaches the size set in pool_size,
# additional connections will be returned up to this limit.
# When those additional connections are returned to the pool, they are disconnected and discarded.
# It follows then that the total number of simultaneous connections the pool will allow
# is pool_size + max_overflow,
# and the total number of "sleeping" connections the pool will allow is pool_size.
# max_overflow can be set to ``-1`` to indicate no overflow limit;
# no limit will be placed on the total number of concurrent connections. Defaults to ``10``.
sql_alchemy_max_overflow = 10

# 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. If the number of DB connections is ever exceeded,
# a lower config value will allow the system to recover faster.
sql_alchemy_pool_recycle = 1800

# Check connection at the start of each connection pool checkout.
# Typically, this is a simple statement like "SELECT 1".
# More information here:
# https://docs.sqlalchemy.org/en/14/core/pooling.html#disconnect-handling-pessimistic
sql_alchemy_pool_pre_ping = True

# The schema to use for the metadata database.
# SqlAlchemy supports databases with the concept of multiple schemas.
sql_alchemy_schema =

# Import path for connect args in SqlAlchemy. Defaults to an empty dict.
# This is useful when you want to configure db engine args that SqlAlchemy won't parse
# in connection string.
# See https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine.params.connect_args
# sql_alchemy_connect_args =

# Whether to load the default connections that ship with Airflow. It's good to
# get started, but you probably want to set this to ``False`` in a production
# environment
load_default_connections = True

# Number of times the code should be retried in case of DB Operational Errors.
# Not all transactions will be retried as it can cause undesired state.
# Currently it is only used in ``DagFileProcessor.process_file`` to retry ``dagbag.sync_to_db``.
max_db_retries = 3

# Whether to run alembic migrations during Airflow start up. Sometimes this operation can be expensive,
# and the users can assert the correct version through other means (e.g. through a Helm chart).
# Accepts "True" or "False".
check_migrations = True

[logging]
# The folder where airflow should store its log files.
# This path must be absolute.
# There are a few existing configurations that assume this is set to the default.
# If you choose to override this you may need to update the dag_processor_manager_log_location and
# dag_processor_manager_log_location settings as well.
base_log_folder = /home/user/airflow/logs

# Airflow can store logs remotely in AWS S3, Google Cloud Storage or Elastic Search.
# Set this to True if you want to enable remote logging.
remote_logging = False

# Users must supply an Airflow connection id that provides access to the storage
# location. Depending on your remote logging service, this may only be used for
# reading logs, not writing them.
remote_log_conn_id =

# Whether the local log files for GCS, S3, WASB and OSS remote logging should be deleted after
# they are uploaded to the remote location.
delete_local_logs = False

# Path to Google Credential JSON file. If omitted, authorization based on `the Application Default
# Credentials
# <https://cloud.google.com/docs/authentication/production#finding_credentials_automatically>`__ will
# be used.
google_key_path =

# Storage bucket URL for remote logging
# S3 buckets should start with "s3://"
# Cloudwatch log groups should start with "cloudwatch://"
# GCS buckets should start with "gs://"
# WASB buckets should start with "wasb" just to help Airflow select correct handler
# Stackdriver logs should start with "stackdriver://"
remote_base_log_folder =

# The remote_task_handler_kwargs param is loaded into a dictionary and passed to __init__ of remote
# task handler and it overrides the values provided by Airflow config. For example if you set
# `delete_local_logs=False` and you provide ``{{"delete_local_copy": true}}``, then the local
# log files will be deleted after they are uploaded to remote location.
# Example: remote_task_handler_kwargs = {{"delete_local_copy": true}}
remote_task_handler_kwargs =

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

# Logging level.
#
# Supported values: ``CRITICAL``, ``ERROR``, ``WARNING``, ``INFO``, ``DEBUG``.
logging_level = INFO

# Logging level for celery. If not set, it uses the value of logging_level
#
# Supported values: ``CRITICAL``, ``ERROR``, ``WARNING``, ``INFO``, ``DEBUG``.
celery_logging_level =

# Logging level for Flask-appbuilder UI.
#
# Supported values: ``CRITICAL``, ``ERROR``, ``WARNING``, ``INFO``, ``DEBUG``.
fab_logging_level = WARNING

# Logging class
# Specify the class that will specify the logging configuration
# This class has to be on the python classpath
# Example: logging_config_class = my.path.default_local_settings.LOGGING_CONFIG
logging_config_class =

# Flag to enable/disable Colored logs in Console
# Colour the logs when the controlling terminal is a TTY.
colored_console_log = True

# Log format for when Colored logs is enabled
colored_log_format = [%%(blue)s%%(asctime)s%%(reset)s] {{%%(blue)s%%(filename)s:%%(reset)s%%(lineno)d}} %%(log_color)s%%(levelname)s%%(reset)s - %%(log_color)s%%(message)s%%(reset)s
colored_formatter_class = airflow.utils.log.colored_log.CustomTTYColoredFormatter

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

# Where to send dag parser logs. If "file", logs are sent to log files defined by child_process_log_directory.
dag_processor_log_target = file

# Format of Dag Processor Log line
dag_processor_log_format = [%%(asctime)s] [SOURCE:DAG_PROCESSOR] {{%%(filename)s:%%(lineno)d}} %%(levelname)s - %%(message)s
log_formatter_class = airflow.utils.log.timezone_aware.TimezoneAware

# An import path to a function to add adaptations of each secret added with
# `airflow.utils.log.secrets_masker.mask_secret` to be masked in log messages. The given function
# is expected to require a single parameter: the secret to be adapted. It may return a
# single adaptation of the secret or an iterable of adaptations to each be masked as secrets.
# The original secret will be masked as well as any adaptations returned.
# Example: secret_mask_adapter = urllib.parse.quote
secret_mask_adapter =

# Specify prefix pattern like mentioned below with stream handler TaskHandlerWithCustomFormatter
# Example: task_log_prefix_template = {{ti.dag_id}}-{{ti.task_id}}-{{execution_date}}-{{try_number}}
task_log_prefix_template =

# Formatting for how airflow generates file names/paths for each task run.
#log_filename_template = dag_id={{{{ ti.dag_id }}}}/run_id={{{{ ti.run_id }}}}/task_id={{{{ ti.task_id }}}}/{{%% if ti.map_index >= 0 %%}}map_index={{{{ ti.map_index }}}}/{{%% endif %%}}attempt={{{{ try_number }}}}.log
log_filename_template = dag_id={{ ti.dag_id }}/run_id={{ ti.run_id }}/task_id={{ ti.task_id }}/{%% if ti.map_index >= 0 %%}map_index={{ ti.map_index }}/{%% endif %%}attempt={{ try_number }}.log 

# Formatting for how airflow generates file names for log
#log_processor_filename_template = {{{{ filename }}}}.log
log_processor_filename_template = {{ filename }}.log

# Full path of dag_processor_manager logfile.
dag_processor_manager_log_location = /home/user/airflow/logs/dag_processor_manager/dag_processor_manager.log

# Name of handler to read task instance logs.
# Defaults to use ``task`` handler.
task_log_reader = task

# A comma\-separated list of third-party logger names that will be configured to print messages to
# consoles\.
# Example: extra_logger_names = connexion,sqlalchemy
extra_logger_names =

# 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

# Port to serve logs from for triggerer.  See worker_log_server_port description
# for more info.
trigger_log_server_port = 8794

# We must parse timestamps to interleave logs between trigger and task.  To do so,
# we need to parse timestamps in log files. In case your log format is non-standard,
# you may provide import path to callable which takes a string log line and returns
# the timestamp (datetime.datetime compatible).
# Example: interleave_timestamp_parser = path.to.my_func
# interleave_timestamp_parser =

# Permissions in the form or of octal string as understood by chmod. The permissions are important
# when you use impersonation, when logs are written by a different user than airflow. The most secure
# way of configuring it in this case is to add both users to the same group and make it the default
# group of both users. Group-writeable logs are default in airflow, but you might decide that you are
# OK with having the logs other-writeable, in which case you should set it to `0o777`. You might
# decide to add more security if you do not use impersonation and change it to `0o755` to make it
# only owner-writeable. You can also make it just readable only for owner by changing it to `0o700` if
# all the access (read/write) for your logs happens from the same user.
# Example: file_task_handler_new_folder_permissions = 0o775
file_task_handler_new_folder_permissions = 0o775

# Permissions in the form or of octal string as understood by chmod. The permissions are important
# when you use impersonation, when logs are written by a different user than airflow. The most secure
# way of configuring it in this case is to add both users to the same group and make it the default
# group of both users. Group-writeable logs are default in airflow, but you might decide that you are
# OK with having the logs other-writeable, in which case you should set it to `0o666`. You might
# decide to add more security if you do not use impersonation and change it to `0o644` to make it
# only owner-writeable. You can also make it just readable only for owner by changing it to `0o600` if
# all the access (read/write) for your logs happens from the same user.
# Example: file_task_handler_new_file_permissions = 0o664
file_task_handler_new_file_permissions = 0o664

[metrics]

# StatsD (https://github.com/etsy/statsd) integration settings.
# If you want to avoid emitting all the available metrics, you can configure an
# allow list of prefixes (comma separated) to send only the metrics that start
# with the elements of the list (e.g: "scheduler,executor,dagrun")
metrics_allow_list =

# If you want to avoid emitting all the available metrics, you can configure a
# block list of prefixes (comma separated) to filter out metrics that start with
# the elements of the list (e.g: "scheduler,executor,dagrun").
# If metrics_allow_list and metrics_block_list are both configured, metrics_block_list is ignored.
metrics_block_list =

# Enables sending metrics to StatsD.
statsd_on = False
statsd_host = localhost
statsd_port = 8125
statsd_prefix = airflow

# A function that validate the StatsD stat name, apply changes to the stat name if necessary and return
# the transformed stat name.
#
# The function should have the following signature:
# def func_name(stat_name: str) -> str:
stat_name_handler =

# To enable datadog integration to send airflow metrics.
statsd_datadog_enabled = False

# List of datadog tags attached to all metrics(e.g: key1:value1,key2:value2)
statsd_datadog_tags =

# Set to False to disable metadata tags for some of the emitted metrics
statsd_datadog_metrics_tags = True

# If you want to utilise your own custom StatsD client set the relevant
# module path below.
# Note: The module path must exist on your PYTHONPATH for Airflow to pick it up
# statsd_custom_client_path =

# If you want to avoid sending all the available metrics tags to StatsD,
# you can configure a block list of prefixes (comma separated) to filter out metric tags
# that start with the elements of the list (e.g: "job_id,run_id")
# Example: statsd_disabled_tags = job_id,run_id,dag_id,task_id
statsd_disabled_tags = job_id,run_id

# To enable sending Airflow metrics with StatsD-Influxdb tagging convention.
statsd_influxdb_enabled = False

# Enables sending metrics to OpenTelemetry.
otel_on = False
otel_host = localhost
otel_port = 8889
otel_prefix = airflow
otel_interval_milliseconds = 60000

[secrets]
# Full class name of secrets backend to enable (will precede env vars and metastore in search path)
# Example: backend = airflow.providers.amazon.aws.secrets.systems_manager.SystemsManagerParameterStoreBackend
backend =

# The backend_kwargs param is loaded into a dictionary and passed to __init__ of secrets backend class.
# See documentation for the secrets backend you are using. JSON is expected.
# Example for AWS Systems Manager ParameterStore:
# ``{{"connections_prefix": "/airflow/connections", "profile_name": "default"}}``
backend_kwargs =

[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://192.168.0.166:8080

[debug]
# Used only with ``DebugExecutor``. If set to ``True`` DAG will fail with first
# failed task. Helpful for debugging purposes.
fail_fast = False

[api]
# Enables the deprecated experimental API. Please note that these APIs do not have access control.
# The authenticated user has full access.
#
# .. warning::
#
#   This `Experimental REST API <https://airflow.readthedocs.io/en/latest/rest-api-ref.html>`__ is
#   deprecated since version 2.0. Please consider using
#   `the Stable REST API <https://airflow.readthedocs.io/en/latest/stable-rest-api-ref.html>`__.
#   For more information on migration, see
#   `RELEASE_NOTES.rst <https://github.com/apache/airflow/blob/main/RELEASE_NOTES.rst>`_
enable_experimental_api = False

# Comma separated list of auth backends to authenticate users of the API. See
# https://airflow.apache.org/docs/apache-airflow/stable/security/api.html for possible values.
# ("airflow.api.auth.backend.default" allows all requests for historic reasons)
auth_backends = airflow.api.auth.backend.session

# Used to set the maximum page limit for API requests. If limit passed as param
# is greater than maximum page limit, it will be ignored and maximum page limit value
# will be set as the limit
#maximum_page_limit = 100
maximum_page_limit = 100

# Used to set the default page limit when limit param is zero or not provided in API
# requests. Otherwise if positive integer is passed in the API requests as limit, the
# smallest number of user given limit or maximum page limit is taken as limit.
fallback_page_limit = 100

# The intended audience for JWT token credentials used for authorization. This value must match on the client and server sides. If empty, audience will not be tested.
# Example: google_oauth2_audience = project-id-random-value.apps.googleusercontent.com
google_oauth2_audience =

# Path to Google Cloud Service Account key file (JSON). If omitted, authorization based on
# `the Application Default Credentials
# <https://cloud.google.com/docs/authentication/production#finding_credentials_automatically>`__ will
# be used.
# Example: google_key_path = /files/service-account-json
google_key_path =

# Used in response to a preflight request to indicate which HTTP
# headers can be used when making the actual request. This header is
# the server side response to the browser's
# Access-Control-Request-Headers header.
access_control_allow_headers =

# Specifies the method or methods allowed when accessing the resource.
access_control_allow_methods =

# Indicates whether the response can be shared with requesting code from the given origins.
# Separate URLs with space.
access_control_allow_origins =

[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

# Default queue that tasks get assigned to and that worker listen on.
default_queue = default

# Is allowed to pass additional/unused arguments (args, kwargs) to the BaseOperator operator.
# If set to False, an exception will be thrown, otherwise only the console message will be displayed.
allow_illegal_arguments = False

[hive]
# Default mapreduce queue for HiveOperator tasks
default_hive_mapred_queue =

# Template for mapred_job_name in HiveOperator, supports the following named parameters
# hostname, dag_id, task_id, execution_date
# mapred_job_name_template =

[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://192.168.0.166:8080

# Default timezone to display all dates in the UI, can be UTC, system, or
# any IANA timezone string (e.g. Europe/Amsterdam). If left empty the
# default value of core/default_timezone will be used
# Example: default_ui_timezone = America/New_York
default_ui_timezone = UTC

# 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 =

# 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_key =

# The type of backend used to store web session data, can be 'database' or 'securecookie'
# Example: session_backend = securecookie
session_backend = database

# Number of seconds the webserver waits before killing gunicorn master that doesn't respond
web_server_master_timeout = 120

# 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 = 6000

# If set to True, Airflow will track files in plugins_folder directory. When it detects changes,
# then reload the gunicorn.
reload_on_plugin_change = False

# Secret key used to run your flask app. It should be as random as possible. However, when running
# more than 1 instances of webserver, make sure all of them use the same ``secret_key`` otherwise
# one of them will error with "CSRF session token is missing".
# The webserver key is also used to authorize requests to Celery workers when logs are retrieved.
# The token generated using the secret key has a short expiry time though - make sure that time on
# ALL the machines that you run airflow components on is synchronized (for example using ntpd)
# otherwise you might get "forbidden" errors when the logs are accessed.
secret_key = 84AsPHLvtFO5KhByzX4IrQ==

# Number of workers to run the Gunicorn web server
workers = 4

# The worker class gunicorn should use. Choices include
# sync (default), eventlet, gevent. Note when using gevent you might also want to set the
# "_AIRFLOW_PATCH_GEVENT" environment variable to "1" to make sure gevent patching is done as
# early as possible.
worker_class = sync

# Log files for the gunicorn webserver. '-' means log to stderr.
access_logfile = -

# Log files for the gunicorn webserver. '-' means log to stderr.
error_logfile = -

# Access log format for gunicorn webserver.
# default format is %%(h)s %%(l)s %%(u)s %%(t)s "%%(r)s" %%(s)s %%(b)s "%%(f)s" "%%(a)s"
# documentation - https://docs.gunicorn.org/en/stable/settings.html#access-log-format
access_logformat =

# Expose the configuration file in the web server. Set to "non-sensitive-only" to show all values
# except those that have security implications. "True" shows all values. "False" hides the
# configuration completely.
expose_config = False

# Expose hostname in the web server
expose_hostname = False

# Expose stacktrace in the web server
expose_stacktrace = False

# Default DAG view. Valid values are: ``grid``, ``graph``, ``duration``, ``gantt``, ``landing_times``
dag_default_view = grid

# Default DAG orientation. Valid values are:
# ``LR`` (Left->Right), ``TB`` (Top->Bottom), ``RL`` (Right->Left), ``BT`` (Bottom->Top)
dag_orientation = LR

# The amount of time (in secs) webserver will wait for initial handshake
# while fetching logs from other worker machine
log_fetch_timeout_sec = 5

# Time interval (in secs) to wait before next log fetching.
log_fetch_delay_sec = 2

# Distance away from page bottom to enable auto tailing.
log_auto_tailing_offset = 30

# Animation speed for auto tailing log display.
log_animation_speed = 1000

# 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
page_size = 50

# Define the color of navigation bar
navbar_color = #fff

# Default dagrun to show in UI
default_dag_run_display_number = 25

# Enable werkzeug ``ProxyFix`` middleware for reverse proxy
enable_proxy_fix = False

# Number of values to trust for ``X-Forwarded-For``.
# More info: https://werkzeug.palletsprojects.com/en/0.16.x/middleware/proxy_fix/
proxy_fix_x_for = 1

# Number of values to trust for ``X-Forwarded-Proto``
proxy_fix_x_proto = 1

# Number of values to trust for ``X-Forwarded-Host``
proxy_fix_x_host = 1

# Number of values to trust for ``X-Forwarded-Port``
proxy_fix_x_port = 1

# Number of values to trust for ``X-Forwarded-Prefix``
proxy_fix_x_prefix = 1

# Set secure flag on session cookie
cookie_secure = False

# Set samesite policy on session cookie
cookie_samesite = Lax

# Default setting for wrap toggle on DAG code and TI log views.
default_wrap = False

# Allow the UI to be rendered in a frame
x_frame_enabled = True

# Send anonymous user activity to your analytics tool
# choose from google_analytics, segment, or metarouter
# analytics_tool =

# Unique ID of your account in the analytics tool
# analytics_id =

# 'Recent Tasks' stats will show for old DagRuns if set
show_recent_stats_for_completed_runs = True

# Update FAB permissions and sync security manager roles
# on webserver startup
update_fab_perms = True

# The UI cookie lifetime in minutes. User will be logged out from UI after
# ``session_lifetime_minutes`` of non-activity
session_lifetime_minutes = 43200

# Sets a custom page title for the DAGs overview page and site title for all pages
instance_name = 任务列表

# Whether the custom page title for the DAGs overview page contains any Markup language
#instance_name_has_markup = False
instance_name_has_markup = True

# How frequently, in seconds, the DAG data will auto-refresh in graph or grid view
# when auto-refresh is turned on
auto_refresh_interval = 3

# Boolean for displaying warning for publicly viewable deployment
warn_deployment_exposure = True

# Comma separated string of view events to exclude from dag audit view.
# All other events will be added minus the ones passed here.
# The audit logs in the db will not be affected by this parameter.
audit_view_excluded_events = gantt,landing_times,tries,duration,calendar,graph,grid,tree,tree_data

# Comma separated string of view events to include in dag audit view.
# If passed, only these events will populate the dag audit view.
# The audit logs in the db will not be affected by this parameter.
# Example: audit_view_included_events = dagrun_cleared,failed
# audit_view_included_events =

# Boolean for running SwaggerUI in the webserver.
enable_swagger_ui = True

# Boolean for enabling rate limiting on authentication endpoints.
auth_rate_limited = True

# Rate limit for authentication endpoints.
auth_rate_limit = 5 per 40 second

[email]

# Configuration email backend and whether to
# send email alerts on retry or failure
# Email backend to use
email_backend = airflow.utils.email.send_email_smtp

# Email connection to use
email_conn_id = smtp_default

# Whether email alerts should be sent when a task is retried
default_email_on_retry = True

# Whether email alerts should be sent when a task failed
default_email_on_failure = True

# File that will be used as the template for Email subject (which will be rendered using Jinja2).
# If not set, Airflow uses a base template.
# Example: subject_template = /path/to/my_subject_template_file
# subject_template =

# File that will be used as the template for Email content (which will be rendered using Jinja2).
# If not set, Airflow uses a base template.
# Example: html_content_template = /path/to/my_html_content_template_file
# html_content_template =

# Email address that will be used as sender address.
# It can either be raw email or the complete address in a format ``Sender Name <sender@email.com>``
# Example: from_email = Airflow <airflow@example.com>
# from_email =

[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
# Example: smtp_user = airflow
# smtp_user =
# Example: smtp_password = airflow
# smtp_password =
smtp_port = 25
smtp_mail_from = airflow@example.com
smtp_timeout = 30
smtp_retry_limit = 5

[sentry]

# Sentry (https://docs.sentry.io) integration. Here you can supply
# additional configuration options based on the Python platform. See:
# https://docs.sentry.io/error-reporting/configuration/?platform=python.
# Unsupported options: ``integrations``, ``in_app_include``, ``in_app_exclude``,
# ``ignore_errors``, ``before_breadcrumb``, ``transport``.
# Enable error reporting to Sentry
sentry_on = false
sentry_dsn =

# Dotted path to a before_send function that the sentry SDK should be configured to use.
# before_send =

[local_kubernetes_executor]

# This section only applies if you are using the ``LocalKubernetesExecutor`` in
# ``[core]`` section above
# Define when to send a task to ``KubernetesExecutor`` when using ``LocalKubernetesExecutor``.
# When the queue of a task is the value of ``kubernetes_queue`` (default ``kubernetes``),
# the task is executed via ``KubernetesExecutor``,
# otherwise via ``LocalExecutor``
kubernetes_queue = kubernetes

[celery_kubernetes_executor]

# This section only applies if you are using the ``CeleryKubernetesExecutor`` in
# ``[core]`` section above
# Define when to send a task to ``KubernetesExecutor`` when using ``CeleryKubernetesExecutor``.
# When the queue of a task is the value of ``kubernetes_queue`` (default ``kubernetes``),
# the task is executed via ``KubernetesExecutor``,
# otherwise via ``CeleryExecutor``
kubernetes_queue = kubernetes

[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 celery 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

# The maximum and minimum concurrency that will be used when starting workers with the
# ``airflow celery worker`` command (always keep minimum processes, but grow
# to maximum if necessary). Note the value should be max_concurrency,min_concurrency
# Pick these numbers based on resources on worker box and the nature of the task.
# If autoscale option is available, worker_concurrency will be ignored.
# http://docs.celeryproject.org/en/latest/reference/celery.bin.worker.html#cmdoption-celery-worker-autoscale
# Example: worker_autoscale = 16,12
# worker_autoscale =

# Used to increase the number of tasks that a worker prefetches which can improve performance.
# The number of processes multiplied by worker_prefetch_multiplier is the number of tasks
# that are prefetched by a worker. A value greater than 1 can result in tasks being unnecessarily
# blocked if there are multiple workers and one worker prefetches tasks that sit behind long
# running tasks while another worker has unutilized processes that are unable to process the already
# claimed blocked tasks.
# https://docs.celeryproject.org/en/stable/userguide/optimizing.html#prefetch-limits
worker_prefetch_multiplier = 1

# Specify if remote control of the workers is enabled.
# In some cases when the broker does not support remote control, Celery creates lots of
# ``.*reply-celery-pidbox`` queues. You can prevent this by setting this to false.
# However, with this disabled Flower won't work.
# https://docs.celeryq.dev/en/stable/getting-started/backends-and-brokers/index.html#broker-overview
worker_enable_remote_control = true

# The Celery broker URL. Celery supports RabbitMQ, Redis and experimentally
# a sqlalchemy database. Refer to the Celery documentation for more information.
broker_url = redis://redis:6379/0

# The Celery result_backend. When a job finishes, it needs to update the
# metadata of the job. Therefore it will post a message on a message bus,
# or insert it into a database (depending of the backend)
# This status is used by the scheduler to update the state of the task
# The use of a database is highly recommended
# When not specified, sql_alchemy_conn with a db+ scheme prefix will be used
# http://docs.celeryproject.org/en/latest/userguide/configuration.html#task-result-backend-settings
# Example: result_backend = db+postgresql://postgres:airflow@postgres/airflow
# result_backend =

# Celery Flower is a sweet UI for Celery. Airflow has a shortcut to start
# it ``airflow celery flower``. This defines the IP that Celery Flower runs on
flower_host = 0.0.0.0

# The root URL for Flower
# Example: flower_url_prefix = /flower
flower_url_prefix =

# This defines the port that Celery Flower runs on
flower_port = 5555

# Securing Flower with Basic Authentication
# Accepts user:password pairs separated by a comma
# Example: flower_basic_auth = user1:password1,user2:password2
flower_basic_auth =

# How many processes CeleryExecutor uses to sync task state.
# 0 means to use max(1, number of cores - 1) processes.
sync_parallelism = 0

# Import path for celery configuration options
celery_config_options = airflow.config_templates.default_celery.DEFAULT_CELERY_CONFIG
ssl_active = False
ssl_key =
ssl_cert =
ssl_cacert =

# Celery Pool implementation.
# Choices include: ``prefork`` (default), ``eventlet``, ``gevent`` or ``solo``.
# See:
# https://docs.celeryproject.org/en/latest/userguide/workers.html#concurrency
# https://docs.celeryproject.org/en/latest/userguide/concurrency/eventlet.html
pool = prefork

# The number of seconds to wait before timing out ``send_task_to_executor`` or
# ``fetch_celery_task_state`` operations.
operation_timeout = 1.0

# Celery task will report its status as 'started' when the task is executed by a worker.
# This is used in Airflow to keep track of the running tasks and if a Scheduler is restarted
# or run in HA mode, it can adopt the orphan tasks launched by previous SchedulerJob.
task_track_started = True

# Time in seconds after which adopted tasks which are queued in celery are assumed to be stalled,
# and are automatically rescheduled. This setting does the same thing as ``stalled_task_timeout`` but
# applies specifically to adopted tasks only. When set to 0, the ``stalled_task_timeout`` setting
# also applies to adopted tasks. To calculate adoption time, subtract the
# :ref:`task duration<ui:task-duration>` from the task's :ref:`landing time<ui:landing-times>`.
task_adoption_timeout = 600

# Time in seconds after which tasks queued in celery are assumed to be stalled, and are automatically
# rescheduled. Adopted tasks will instead use the ``task_adoption_timeout`` setting if specified.
# When set to 0, automatic clearing of stalled tasks is disabled.
stalled_task_timeout = 0

# The Maximum number of retries for publishing task messages to the broker when failing
# due to ``AirflowTaskTimeout`` error before giving up and marking Task as failed.
task_publish_max_retries = 3

# Worker initialisation check to validate Metadata Database connection
worker_precheck = False

[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
# Example: visibility_timeout = 21600
# visibility_timeout =

[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

# TLS/ SSL settings to access a secured Dask scheduler.
tls_ca =
tls_cert =
tls_key =

[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

# The number of times to try to schedule each DAG file
# -1 indicates unlimited number
num_runs = -1

# Controls how long the scheduler will sleep between loops, but if there was nothing to do
# in the loop. i.e. if it scheduled something then it will start the next loop
# iteration straight away.
scheduler_idle_sleep_time = 1

# Number of seconds after which a DAG file is parsed. The DAG file is parsed every
# ``min_file_process_interval`` number of seconds. Updates to DAGs are reflected after
# this interval. Keeping this number low will increase CPU usage.
min_file_process_interval = 30

# How often (in seconds) to check for stale DAGs (DAGs which are no longer present in
# the expected files) which should be deactivated, as well as datasets that are no longer
# referenced and should be marked as orphaned.
parsing_cleanup_interval = 60

# How long (in seconds) to wait after we have re-parsed a DAG file before deactivating stale
# DAGs (DAGs which are no longer present in the expected files). The reason why we need
# this threshold is to account for the time between when the file is parsed and when the
# DAG is loaded. The absolute maximum that this could take is `dag_file_processor_timeout`,
# but when you have a long timeout configured, it results in a significant delay in the
# deactivation of stale dags.
stale_dag_threshold = 50

# How often (in seconds) to scan the DAGs directory for new files. Default to 5 minutes.
dag_dir_list_interval = 300

# How often should stats be printed to the logs. Setting to 0 will disable printing stats
print_stats_interval = 30

# How often (in seconds) should pool usage stats be sent to StatsD (if statsd_on is enabled)
pool_metrics_interval = 5.0

# If the last scheduler heartbeat happened more than scheduler_health_check_threshold
# ago (in seconds), scheduler is considered unhealthy.
# This is used by the health check in the "/health" endpoint
scheduler_health_check_threshold = 30

# When you start a scheduler, airflow starts a tiny web server
# subprocess to serve a health check if this is set to True
enable_health_check = False

# When you start a scheduler, airflow starts a tiny web server
# subprocess to serve a health check on this port
scheduler_health_check_server_port = 8978

# How often (in seconds) should the scheduler check for orphaned tasks and SchedulerJobs
orphaned_tasks_check_interval = 300.0
#child_process_log_directory = {AIRFLOW_HOME}/logs/scheduler
child_process_log_directory = /home/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

# How often (in seconds) should the scheduler check for zombie tasks.
zombie_detection_interval = 10.0

# 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
catchup_by_default = False

# Setting this to True will make first task instance of a task
# ignore depends_on_past setting. A task instance will be considered
# as the first task instance of a task when there is no task instance
# in the DB with an execution_date earlier than it., i.e. no manual marking
# success will be needed for a newly added task to be scheduled.
ignore_first_depends_on_past_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
# complexity of query predicate, and/or 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

# Should the scheduler issue ``SELECT ... FOR UPDATE`` in relevant queries.
# If this is set to False then you should not run more than a single
# scheduler at once
use_row_level_locking = True

# Max number of DAGs to create DagRuns for per scheduler loop.
max_dagruns_to_create_per_loop = 10

# How many DagRuns should a scheduler examine (and lock) when scheduling
# and queuing tasks.
max_dagruns_per_loop_to_schedule = 20

# Should the Task supervisor process perform a "mini scheduler" to attempt to schedule more tasks of the
# same DAG. Leaving this on will mean tasks in the same DAG execute quicker, but might starve out other
# dags in some circumstances
schedule_after_task_execution = True

# The scheduler can run multiple processes in parallel to parse dags.
# This defines how many processes will run.
parsing_processes = 2

# One of ``modified_time``, ``random_seeded_by_host`` and ``alphabetical``.
# The scheduler will list and sort the dag files to decide the parsing order.
#
# * ``modified_time``: Sort by modified time of the files. This is useful on large scale to parse the
#   recently modified DAGs first.
# * ``random_seeded_by_host``: Sort randomly across multiple Schedulers but with same order on the
#   same host. This is useful when running with Scheduler in HA mode where each scheduler can
#   parse different DAG files.
# * ``alphabetical``: Sort by filename
file_parsing_sort_mode = modified_time

# Whether the dag processor is running as a standalone process or it is a subprocess of a scheduler
# job.
standalone_dag_processor = False

# Only applicable if `[scheduler]standalone_dag_processor` is true and  callbacks are stored
# in database. Contains maximum number of callbacks that are fetched during a single loop.
max_callbacks_per_loop = 20

# Only applicable if `[scheduler]standalone_dag_processor` is true.
# Time in seconds after which dags, which were not updated by Dag Processor are deactivated.
dag_stale_not_seen_duration = 600

# Turn off scheduler use of cron intervals by setting this to False.
# DAGs submitted manually in the web UI or with trigger_dag will still run.
use_job_schedule = True

# Allow externally triggered DagRuns for Execution Dates in the future
# Only has effect if schedule_interval is set to None in DAG
allow_trigger_in_future = False

# How often to check for expired trigger requests that have not run yet.
trigger_timeout_check_interval = 15

[triggerer]
# How many triggers a single Triggerer will run at once, by default.
default_capacity = 1000

[kerberos]
ccache = /tmp/airflow_krb5_ccache

# gets augmented with fqdn
principal = airflow
reinit_frequency = 3600
kinit_path = kinit
keytab = airflow.keytab

# Allow to disable ticket forwardability.
forwardable = True

# Allow to remove source IP from token, useful when using token behind NATted Docker host.
include_ip = True

[elasticsearch]
# Elasticsearch host
host =

# Format of the log_id, which is used to query for a given tasks logs
log_id_template = {{dag_id}}-{{task_id}}-{{run_id}}-{{map_index}}-{{try_number}}

# Used to mark the end of a log stream for a task
end_of_log_mark = end_of_log

# Qualified URL for an elasticsearch frontend (like Kibana) with a template argument for log_id
# Code will construct log_id using the log_id template from the argument above.
# NOTE: scheme will default to https if one is not provided
# Example: frontend = http://localhost:5601/app/kibana#/discover?_a=(columns:!(message),query:(language:kuery,query:'log_id: "{{log_id}}"'),sort:!(log.offset,asc))
frontend =

# Write the task logs to the stdout of the worker, rather than the default files
write_stdout = False

# Instead of the default log formatter, write the log lines as JSON
json_format = False

# Log fields to also attach to the json output, if enabled
json_fields = asctime, filename, lineno, levelname, message

# The field where host name is stored (normally either `host` or `host.name`)
host_field = host

# The field where offset is stored (normally either `offset` or `log.offset`)
offset_field = offset

# Comma separated list of index patterns to use when searching for logs (default: `_all`).
# Example: index_patterns = something-*
index_patterns = _all

[elasticsearch_configs]
use_ssl = False
verify_certs = True

[kubernetes_executor]
# Path to the YAML pod file that forms the basis for KubernetesExecutor workers.
pod_template_file =

# The repository of the Kubernetes Image for the Worker to Run
worker_container_repository =

# The tag of the Kubernetes Image for the Worker to Run
worker_container_tag =

# The Kubernetes namespace where airflow workers should be created. Defaults to ``default``
namespace = default

# If True, all worker pods will be deleted upon termination
delete_worker_pods = True

# If False (and delete_worker_pods is True),
# failed worker pods will not be deleted so users can investigate them.
# This only prevents removal of worker pods where the worker itself failed,
# not when the task it ran failed.
delete_worker_pods_on_failure = False

# Number of Kubernetes Worker Pod creation calls per scheduler loop.
# Note that the current default of "1" will only launch a single pod
# per-heartbeat. It is HIGHLY recommended that users increase this
# number to match the tolerance of their kubernetes cluster for
# better performance.
worker_pods_creation_batch_size = 1

# Allows users to launch pods in multiple namespaces.
# Will require creating a cluster-role for the scheduler,
# or use multi_namespace_mode_namespace_list configuration.
multi_namespace_mode = False

# If multi_namespace_mode is True while scheduler does not have a cluster-role,
# give the list of namespaces where the scheduler will schedule jobs
# Scheduler needs to have the necessary permissions in these namespaces.
multi_namespace_mode_namespace_list =

# Use the service account kubernetes gives to pods to connect to kubernetes cluster.
# It's intended for clients that expect to be running inside a pod running on kubernetes.
# It will raise an exception if called from a process not running in a kubernetes environment.
in_cluster = True

# When running with in_cluster=False change the default cluster_context or config_file
# options to Kubernetes client. Leave blank these to use default behaviour like ``kubectl`` has.
# cluster_context =

# Path to the kubernetes configfile to be used when ``in_cluster`` is set to False
# config_file =

# Keyword parameters to pass while calling a kubernetes client core_v1_api methods
# from Kubernetes Executor provided as a single line formatted JSON dictionary string.
# List of supported params are similar for all core_v1_apis, hence a single config
# variable for all apis. See:
# https://raw.githubusercontent.com/kubernetes-client/python/41f11a09995efcd0142e25946adc7591431bfb2f/kubernetes/client/api/core_v1_api.py
kube_client_request_args =

# Optional keyword arguments to pass to the ``delete_namespaced_pod`` kubernetes client
# ``core_v1_api`` method when using the Kubernetes Executor.
# This should be an object and can contain any of the options listed in the ``v1DeleteOptions``
# class defined here:
# https://github.com/kubernetes-client/python/blob/41f11a09995efcd0142e25946adc7591431bfb2f/kubernetes/client/models/v1_delete_options.py#L19
# Example: delete_option_kwargs = {{"grace_period_seconds": 10}}
delete_option_kwargs =

# Enables TCP keepalive mechanism. This prevents Kubernetes API requests to hang indefinitely
# when idle connection is time-outed on services like cloud load balancers or firewalls.
enable_tcp_keepalive = True

# When the `enable_tcp_keepalive` option is enabled, TCP probes a connection that has
# been idle for `tcp_keep_idle` seconds.
tcp_keep_idle = 120

# When the `enable_tcp_keepalive` option is enabled, if Kubernetes API does not respond
# to a keepalive probe, TCP retransmits the probe after `tcp_keep_intvl` seconds.
tcp_keep_intvl = 30

# When the `enable_tcp_keepalive` option is enabled, if Kubernetes API does not respond
# to a keepalive probe, TCP retransmits the probe `tcp_keep_cnt number` of times before
# a connection is considered to be broken.
tcp_keep_cnt = 6

# Set this to false to skip verifying SSL certificate of Kubernetes python client.
verify_ssl = True

# How long in seconds a worker can be in Pending before it is considered a failure
worker_pods_pending_timeout = 300

# How often in seconds to check if Pending workers have exceeded their timeouts
worker_pods_pending_timeout_check_interval = 120

# How often in seconds to check for task instances stuck in "queued" status without a pod
worker_pods_queued_check_interval = 60

# How many pending pods to check for timeout violations in each check interval.
# You may want this higher if you have a very large cluster and/or use ``multi_namespace_mode``.
worker_pods_pending_timeout_batch_size = 100

# Path to a CA certificate to be used by the Kubernetes client to verify the server's SSL certificate.
ssl_ca_cert =

[sensors]
# Sensor default timeout, 7 days by default (7 * 24 * 60 * 60).
default_timeout = 604800
apache / airflow

dags_are_paused_at_creation = False AND is_paused_upon_creation param are not working #30615

Apache Airflow version

What happened

What you think should happen instead

How to reproduce

Operating System

Versions of Apache Airflow Providers

Deployment

Deployment details

Anything else

Are you willing to submit PR?

Code of Conduct
