/
configmaps.yaml
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/
configmaps.yaml
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# Licensed to the Apache Software Foundation (ASF) under one *
# or more contributor license agreements. See the NOTICE file *
# distributed with this work for additional information *
# regarding copyright ownership. The ASF licenses this file *
# to you under the Apache License, Version 2.0 (the *
# "License"); you may not use this file except in compliance *
# with the License. You may obtain a copy of the License at *
# *
# http://www.apache.org/licenses/LICENSE-2.0 *
# *
# Unless required by applicable law or agreed to in writing, *
# software distributed under the License is distributed on an *
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY *
# KIND, either express or implied. See the License for the *
# specific language governing permissions and limitations *
# under the License. *
apiVersion: v1
kind: ConfigMap
metadata:
name: airflow-configmap
namespace: airflow
data:
airflow.cfg: |
[core]
dags_folder = /root/airflow/dags
base_log_folder = /root/airflow/logs
logging_level = INFO
executor = KubernetesExecutor
parallelism = 32
load_examples = False
plugins_folder = /root/airflow/plugins
sql_alchemy_conn = $SQL_ALCHEMY_CONN
[scheduler]
dag_dir_list_interval = 300
child_process_log_directory = /root/airflow/logs/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
max_threads = 2
# 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 a new DAGs should be picked up from the filesystem
min_file_process_interval = 0
statsd_on = False
statsd_host = localhost
statsd_port = 8125
statsd_prefix = airflow
# How many seconds to wait between file-parsing loops to prevent the logs from being spammed.
min_file_parsing_loop_time = 1
print_stats_interval = 30
scheduler_zombie_task_threshold = 300
max_tis_per_query = 0
authenticate = False
# 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
[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://0.0.0.0:8080
rbac=True
# 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 = 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 = 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
# 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
[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
[kubernetes]
airflow_configmap = airflow-configmap
worker_container_repository = 542082525742.dkr.ecr.us-west-2.amazonaws.com/airflow-eks-demo
worker_container_tag = latest
worker_container_image_pull_policy = Always
worker_service_account_name = airflow
namespace = airflow
delete_worker_pods = True
dags_in_image = False
git_repo = https://github.com/apache/airflow.git
git_branch = master
git_subpath = airflow/contrib/example_dags/
git_user =
git_password =
git_sync_root = /git
git_sync_path = repo
git_dags_folder_mount_point =
dags_volume_claim = airflow-efs-pvc
dags_volume_subpath =
logs_volume_claim =
logs_volume_subpath =
dags_volume_host =
logs_volume_host =
in_cluster = True
gcp_service_account_keys =
# Example affinity and toleration definitions.
affinity = {"nodeAffinity":{"requiredDuringSchedulingIgnoredDuringExecution":{"weight":1,"preference":[{"matchExpressions":[{"key":"lifecycle","operator":"In","value":"Ec2Spot"}]}],"nodeSelectorTerms":[{"matchExpressions":[{"key":"kubernetes.io/hostname","operator":"NotIn","values":["4e5e6a99-e28a-450b-bba9-e0124853de9b"]}]}]}}}
tolerations = [{ "key": "spotInstance", "operator": "Equal", "value": "true", "effect": "PreferNoSchedule" },{ "key": "dedicated", "operator": "Equal", "value": "airflow", "effect": "NoSchedule" }, { "key": "prod", "operator": "Exists" }]
# affinity = {"nodeAffinity":{"requiredDuringSchedulingIgnoredDuringExecution":{"nodeSelectorTerms":[{"matchExpressions":[{"key":"kubernetes.io/hostname","operator":"NotIn","values":["4e5e6a99-e28a-450b-bba9-e0124853de9b"]}]}]}}}
# tolerations = [{ "key": "dedicated", "operator": "Equal", "value": "airflow", "effect": "NoSchedule" }, { "key": "prod", "operator": "Exists" }]
# For cloning DAGs from git repositories into volumes: https://github.com/kubernetes/git-sync
git_sync_container_repository = gcr.io/google-containers/git-sync-amd64
git_sync_container_tag = v2.0.5
git_sync_init_container_name = git-sync-clone
[kubernetes_node_selectors]
# The Key-value pairs to be given to worker pods.
# The worker pods will be scheduled to the nodes of the specified key-value pairs.
# Should be supplied in the format: key = value
[kubernetes_annotations]
# The Key-value annotations pairs to be given to worker pods.
# Should be supplied in the format: key = value
[kubernetes_secrets]
SQL_ALCHEMY_CONN = airflow-secrets=sql_alchemy_conn
[hive]
# Default mapreduce queue for HiveOperator tasks
default_hive_mapred_queue =
[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 = 16
# When you start an airflow worker, airflow starts a tiny web server
# subprocess to serve the workers local log files to the airflow main
# web server, who then builds pages and sends them to users. This defines
# the port on which the logs are served. It needs to be unused, and open
# visible from the main web server to connect into the workers.
worker_log_server_port = 8793
# The Celery broker URL. Celery supports RabbitMQ, Redis and experimentally
# a sqlalchemy database. Refer to the Celery documentation for more
# information.
# http://docs.celeryproject.org/en/latest/userguide/configuration.html#broker-settings
broker_url = sqla+mysql://airflow:airflow@localhost:3306/airflow
# 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
# http://docs.celeryproject.org/en/latest/userguide/configuration.html#task-result-backend-settings
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 IP that Celery Flower runs on
flower_host = 0.0.0.0
# The root URL for Flower
# Ex: 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 =
# Default queue that tasks get assigned to and that worker listen on.
default_queue = default
# 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
[celery_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. Especially important in case of using Redis or SQS
visibility_timeout = 21600
# In case of using SSL
ssl_active = False
ssl_key =
ssl_cert =
ssl_cacert =
[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 =
[ldap]
# set this to ldaps://<your.ldap.server>:<port>
uri =
user_filter = objectClass=*
user_name_attr = uid
group_member_attr = memberOf
superuser_filter =
data_profiler_filter =
bind_user = cn=Manager,dc=example,dc=com
bind_password = insecure
basedn = dc=example,dc=com
cacert = /etc/ca/ldap_ca.crt
search_scope = LEVEL
[kerberos]
ccache = /tmp/airflow_krb5_ccache
# gets augmented with fqdn
principal = airflow
reinit_frequency = 3600
kinit_path = kinit
keytab = airflow.keytab
[cli]
api_client = airflow.api.client.json_client
endpoint_url = http://0.0.0.0:8080
[api]
auth_backend = airflow.api.auth.backend.default
[github_enterprise]
api_rev = v3
[admin]
# UI to hide sensitive variable fields when set to True
hide_sensitive_variable_fields = True
[elasticsearch]
elasticsearch_host =