diff --git a/airflow/Dockerfile b/airflow/Dockerfile
index 9723c5f..da562af 100644
--- a/airflow/Dockerfile
+++ b/airflow/Dockerfile
@@ -9,9 +9,6 @@ ENV AIRFLOW_EXTRAS=async,all_dbs,celery,crypto,devel_hadoop,jdbc,ldap,password,r
 ENV AIRFLOW_HOME=/opt/airflow
 ENV AIRFLOW_CONFIG=/opt/airflow/airflow.cfg
 
-ARG FERNET_KEY=4XHGZH0dZ40iOv6z5cyfrXVg5qg3s_d06A7BFfbSsqA=
-ENV FERNET_KEY=${FERNET_KEY}
-
 RUN set -xe \
     && apk add --no-cache \
         build-base \
@@ -23,7 +20,7 @@ RUN set -xe \
         mariadb-dev \
         postgresql-dev \
         python3-dev \
-    && pip install cython numpy \
+    && pip install cython gunicorn numpy psycopg2-binary \
     && pip install apache-airflow[${AIRFLOW_EXTRAS}]==${AIRFLOW_VERSION} \
     && pip install "websocket-client<0.55.0,>=0.35" \
     && apk del \
diff --git a/airflow/README.md b/airflow/README.md
index 1cceb0a..acf3206 100644
--- a/airflow/README.md
+++ b/airflow/README.md
@@ -22,9 +22,6 @@ airflow
 ## Quick Start
 
 ```bash
-$ python -c 'from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())'
-4XHGZH0dZ40iOv6z5cyfrXVg5qg3s_d06A7BFfbSsqA=
-
 $ docker stack deploy -c docker-stack.yaml airflow
 $ docker service update --replicas-max-per-node=1 airflow_worker
 $ docker service update --replicas 3 airflow_worker
@@ -33,5 +30,11 @@ $ curl http://localhost:8080/
 $ curl http://localhost:5555/
 ```
 
-> :warning: This docker image was built with a static `FERNET_KEY` environment variable.
-> You should set another value to it in `docker-stack.yaml`.
+> :warning: You need to prepare nfs server with `airflow.cfg`.
+
+```
+$ python -c 'from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())'
+CD2wL7G0zt1SLuO4JQpLJuHtBaBEcXWKbQyvkvf2cZ8=
+```
+
+> :warning: You should set another value to `fernet_key` in `airflow.cfg` to improve security.
diff --git a/airflow/data/airflow.cfg b/airflow/data/airflow.cfg
index 5a2d71f..b464eb0 100644
--- a/airflow/data/airflow.cfg
+++ b/airflow/data/airflow.cfg
@@ -1,43 +1,12 @@
-# -*- coding: utf-8 -*-
-#
-# 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.
-
-
-# This is the template for Airflow's default configuration. When Airflow is
-# imported, it looks for a configuration file at $AIRFLOW_HOME/airflow.cfg. If
-# it doesn't exist, Airflow uses this template to generate it by replacing
-# variables in curly braces with their global values from configuration.py.
-
-# Users should not modify this file; they should customize the generated
-# airflow.cfg instead.
-
-
-# ----------------------- TEMPLATE BEGINS HERE -----------------------
-
 [core]
 # The folder where your airflow pipelines live, most likely a
 # subfolder in a code repository
 # This path must be absolute
-dags_folder = {AIRFLOW_HOME}/dags
+dags_folder = /opt/airflow/dags
 
 # The folder where airflow should store its log files
 # This path must be absolute
-base_log_folder = {AIRFLOW_HOME}/logs
+base_log_folder = /opt/airflow/logs
 
 # Airflow can store logs remotely in AWS S3, Google Cloud Storage or Elastic Search.
 # Users must supply an Airflow connection id that provides access to the storage
@@ -61,20 +30,16 @@ logging_config_class =
 # Log format
 # Colour the logs when the controlling terminal is a TTY.
 colored_console_log = True
-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_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
 
-log_format = [%%(asctime)s] {{%%(filename)s:%%(lineno)d}} %%(levelname)s - %%(message)s
+log_format = [%%(asctime)s] {%%(filename)s:%%(lineno)d} %%(levelname)s - %%(message)s
 simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s
 
-# Specify prefix pattern like mentioned below with stream handler TaskHandlerWithCustomFormatter
-# task_log_prefix_template =  {{ti.dag_id}}-{{ti.task_id}}-{{execution_date}}-{{try_number}}
-task_log_prefix_template =
-
 # Log filename format
-log_filename_template = {{{{ ti.dag_id }}}}/{{{{ ti.task_id }}}}/{{{{ ts }}}}/{{{{ try_number }}}}.log
-log_processor_filename_template = {{{{ filename }}}}.log
-dag_processor_manager_log_location = {AIRFLOW_HOME}/logs/dag_processor_manager/dag_processor_manager.log
+log_filename_template = {{ ti.dag_id }}/{{ ti.task_id }}/{{ ts }}/{{ try_number }}.log
+log_processor_filename_template = {{ filename }}.log
+dag_processor_manager_log_location = /opt/airflow/logs/dag_processor_manager/dag_processor_manager.log
 
 # Hostname by providing a path to a callable, which will resolve the hostname
 # The format is "package:function". For example,
@@ -94,7 +59,7 @@ executor = CeleryExecutor
 # The SqlAlchemy connection string to the metadata database.
 # SqlAlchemy supports many different database engine, more information
 # their website
-sql_alchemy_conn = postgresql+psycopg2://airflow:airflow@postgres:5432/airflow
+sql_alchemy_conn = postgresql+psycopg2://airflow:airflow@postges:5432/airflow
 
 # The encoding for the databases
 sql_engine_encoding = utf-8
@@ -122,10 +87,9 @@ sql_alchemy_max_overflow = 10
 # 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/13/core/pooling.html#disconnect-handling-pessimistic
-sql_alchemy_pool_pre_ping = True
+# How many seconds to retry re-establishing a DB connection after
+# disconnects. Setting this to 0 disables retries.
+sql_alchemy_reconnect_timeout = 300
 
 # The schema to use for the metadata database
 # SqlAlchemy supports databases with the concept of multiple schemas.
@@ -151,20 +115,17 @@ max_active_runs_per_dag = 16
 load_examples = False
 
 # Where your Airflow plugins are stored
-plugins_folder = {AIRFLOW_HOME}/plugins
+plugins_folder = /opt/airflow/plugins
 
 # Secret key to save connection passwords in the db
-fernet_key = {FERNET_KEY}
+fernet_key = CD2wL7G0zt1SLuO4JQpLJuHtBaBEcXWKbQyvkvf2cZ8=
 
 # Whether to disable pickling dags
-donot_pickle = True
+donot_pickle = False
 
-# How long before timing out a python file import
+# How long before timing out a python file import while filling the DagBag
 dagbag_import_timeout = 30
 
-# 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
 task_runner = StandardTaskRunner
 
@@ -195,8 +156,8 @@ enable_xcom_pickling = True
 # 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.
+# Whether to override params with dag_run.conf. If you pass some key-value pairs through `airflow backfill -c` or
+# `airflow trigger_dag -c`, the key-value pairs will override the existing ones in params.
 dag_run_conf_overrides_params = False
 
 # Worker initialisation check to validate Metadata Database connection
@@ -205,9 +166,6 @@ worker_precheck = False
 # When discovering DAGs, ignore any files that don't contain the strings `DAG` and `airflow`.
 dag_discovery_safe_mode = True
 
-# The number of retries each task is going to have by default. Can be overridden at dag or task level.
-default_task_retries = 0
-
 
 [cli]
 # In what way should the cli access the API. The LocalClient will use the
@@ -247,9 +205,6 @@ default_gpus = 0
 [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 = Airflow HiveOperator task for {{hostname}}.{{dag_id}}.{{task_id}}.{{execution_date}}
 
 [webserver]
 # The base url of your website as airflow cannot guess what domain or
@@ -283,8 +238,7 @@ worker_refresh_batch_size = 1
 worker_refresh_interval = 30
 
 # Secret key used to run your flask app
-# It should be as random as possible
-secret_key = {SECRET_KEY}
+secret_key = temporary_key
 
 # Number of workers to run the Gunicorn web server
 workers = 4
@@ -298,8 +252,25 @@ access_logfile = -
 error_logfile = -
 
 # Expose the configuration file in the web server
+# This is only applicable for the flask-admin based web UI (non FAB-based).
+# In the FAB-based web UI with RBAC feature,
+# access to configuration is controlled by role permissions.
 expose_config = False
 
+# Set to true to turn on authentication:
+# https://airflow.apache.org/security.html#web-authentication
+authenticate = False
+
+# Filter the list of dags by owner name (requires authentication to be enabled)
+filter_by_owner = False
+
+# Filtering mode. Choices include user (default) and ldapgroup.
+# Ldap group filtering requires using the ldap backend
+#
+# Note that the ldap server needs the "memberOf" overlay to be set up
+# in order to user the ldapgroup mode.
+owner_mode = user
+
 # Default DAG view.  Valid values are:
 # tree, graph, duration, gantt, landing_times
 dag_default_view = tree
@@ -323,6 +294,9 @@ hide_paused_dags_by_default = False
 # Consistent page size across all listing views in the UI
 page_size = 100
 
+# Use FAB-based webserver with RBAC feature
+rbac = False
+
 # Define the color of navigation bar
 navbar_color = #007A87
 
@@ -362,10 +336,6 @@ smtp_ssl = False
 smtp_port = 25
 smtp_mail_from = airflow@example.com
 
-[sentry]
-# Sentry (https://docs.sentry.io) integration
-sentry_dsn = 
-
 
 [celery]
 # This section only applies if you are using the CeleryExecutor in
@@ -407,7 +377,7 @@ broker_url = redis://redis:6379/1
 # 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+postgresql://airflow:airflow@postgres/airflow
+result_backend = db+postgresql://airflow:airflow@postges/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
@@ -487,12 +457,9 @@ job_heartbeat_sec = 5
 # 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
-
-# The number of seconds to wait between consecutive DAG file processing
-processor_poll_interval = 1
+# after how much time should the scheduler terminate in seconds
+# -1 indicates to run continuously (see also num_runs)
+run_duration = -1
 
 # after how much time (seconds) a new DAGs should be picked up from the filesystem
 min_file_process_interval = 0
@@ -508,7 +475,7 @@ print_stats_interval = 30
 # This is used by the health check in the "/health" endpoint
 scheduler_health_check_threshold = 30
 
-child_process_log_directory = {AIRFLOW_HOME}/logs/scheduler
+child_process_log_directory = /opt/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
@@ -541,11 +508,6 @@ statsd_host = localhost
 statsd_port = 8125
 statsd_prefix = airflow
 
-# If you want to avoid send all the available metrics to StatsD,
-# you can configure an allow list of prefixes to send only the metrics that
-# start with the elements of the list (e.g: scheduler,executor,dagrun)
-statsd_allow_list =
-
 # The scheduler can run multiple threads in parallel to schedule dags.
 # This defines how many threads will run.
 max_threads = 2
@@ -574,6 +536,48 @@ search_scope = LEVEL
 # broken schema, or do not return a schema.
 ignore_malformed_schema = False
 
+[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
+
+# Optional Docker Image to run on slave before running the command
+# This image should be accessible from mesos slave i.e mesos slave
+# should be able to pull this docker image before executing the command.
+# docker_image_slave = puckel/docker-airflow
+
 [kerberos]
 ccache = /tmp/airflow_krb5_ccache
 # gets augmented with fqdn
@@ -594,7 +598,7 @@ hide_sensitive_variable_fields = True
 # 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}}-{{execution_date}}-{{try_number}}
+log_id_template = {dag_id}-{task_id}-{execution_date}-{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
@@ -674,7 +678,7 @@ git_password =
 git_sync_root = /git
 git_sync_dest = repo
 # Mount point of the volume if git-sync is being used.
-# i.e. {AIRFLOW_HOME}/dags
+# i.e. /opt/airflow/dags
 git_dags_folder_mount_point =
 
 # To get Git-sync SSH authentication set up follow this format
@@ -765,7 +769,9 @@ tolerations =
 # List of supported params in **kwargs are similar for all core_v1_apis, hence a single config variable for all apis
 # See:
 #   https://raw.githubusercontent.com/kubernetes-client/python/master/kubernetes/client/apis/core_v1_api.py
-kube_client_request_args =
+# Note that if no _request_timeout is specified, the kubernetes client will wait indefinitely for kubernetes
+# api responses, which will cause the scheduler to hang. The timeout is specified as [connect timeout, read timeout]
+kube_client_request_args = {"_request_timeout" : [60,60] }
 
 # Worker pods security context options
 # See:
@@ -779,17 +785,15 @@ run_as_user =
 # that allows for the key to be read, e.g. 65533
 fs_group =
 
-# Annotations configuration as a single line formatted JSON object.
-# See the naming convention in:
-#   https://kubernetes.io/docs/concepts/overview/working-with-objects/annotations/
-worker_annotations =
-
-
 [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_environment_variables]
 # The scheduler sets the following environment variables into your workers. You may define as
 # many environment variables as needed and the kubernetes launcher will set them in the launched workers.
diff --git a/airflow/data/default_airflow.cfg b/airflow/data/default_airflow.cfg
index ae46feb..8d9d360 100644
--- a/airflow/data/default_airflow.cfg
+++ b/airflow/data/default_airflow.cfg
@@ -67,10 +67,6 @@ colored_formatter_class = airflow.utils.log.colored_log.CustomTTYColoredFormatte
 log_format = [%%(asctime)s] {{%%(filename)s:%%(lineno)d}} %%(levelname)s - %%(message)s
 simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s
 
-# Specify prefix pattern like mentioned below with stream handler TaskHandlerWithCustomFormatter
-# task_log_prefix_template =  {{ti.dag_id}}-{{ti.task_id}}-{{execution_date}}-{{try_number}}
-task_log_prefix_template =
-
 # Log filename format
 log_filename_template = {{{{ ti.dag_id }}}}/{{{{ ti.task_id }}}}/{{{{ ts }}}}/{{{{ try_number }}}}.log
 log_processor_filename_template = {{{{ filename }}}}.log
@@ -122,10 +118,9 @@ sql_alchemy_max_overflow = 10
 # 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/13/core/pooling.html#disconnect-handling-pessimistic
-sql_alchemy_pool_pre_ping = True
+# How many seconds to retry re-establishing a DB connection after
+# disconnects. Setting this to 0 disables retries.
+sql_alchemy_reconnect_timeout = 300
 
 # The schema to use for the metadata database
 # SqlAlchemy supports databases with the concept of multiple schemas.
@@ -157,14 +152,11 @@ plugins_folder = {AIRFLOW_HOME}/plugins
 fernet_key = {FERNET_KEY}
 
 # Whether to disable pickling dags
-donot_pickle = True
+donot_pickle = False
 
-# How long before timing out a python file import
+# How long before timing out a python file import while filling the DagBag
 dagbag_import_timeout = 30
 
-# 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
 task_runner = StandardTaskRunner
 
@@ -195,8 +187,8 @@ enable_xcom_pickling = True
 # 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.
+# Whether to override params with dag_run.conf. If you pass some key-value pairs through `airflow backfill -c` or
+# `airflow trigger_dag -c`, the key-value pairs will override the existing ones in params.
 dag_run_conf_overrides_params = False
 
 # Worker initialisation check to validate Metadata Database connection
@@ -205,9 +197,6 @@ worker_precheck = False
 # When discovering DAGs, ignore any files that don't contain the strings `DAG` and `airflow`.
 dag_discovery_safe_mode = True
 
-# The number of retries each task is going to have by default. Can be overridden at dag or task level.
-default_task_retries = 0
-
 
 [cli]
 # In what way should the cli access the API. The LocalClient will use the
@@ -247,9 +236,6 @@ default_gpus = 0
 [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 = Airflow HiveOperator task for {{hostname}}.{{dag_id}}.{{task_id}}.{{execution_date}}
 
 [webserver]
 # The base url of your website as airflow cannot guess what domain or
@@ -283,8 +269,7 @@ worker_refresh_batch_size = 1
 worker_refresh_interval = 30
 
 # Secret key used to run your flask app
-# It should be as random as possible
-secret_key = {SECRET_KEY}
+secret_key = temporary_key
 
 # Number of workers to run the Gunicorn web server
 workers = 4
@@ -298,8 +283,25 @@ access_logfile = -
 error_logfile = -
 
 # Expose the configuration file in the web server
+# This is only applicable for the flask-admin based web UI (non FAB-based).
+# In the FAB-based web UI with RBAC feature,
+# access to configuration is controlled by role permissions.
 expose_config = False
 
+# Set to true to turn on authentication:
+# https://airflow.apache.org/security.html#web-authentication
+authenticate = False
+
+# Filter the list of dags by owner name (requires authentication to be enabled)
+filter_by_owner = False
+
+# Filtering mode. Choices include user (default) and ldapgroup.
+# Ldap group filtering requires using the ldap backend
+#
+# Note that the ldap server needs the "memberOf" overlay to be set up
+# in order to user the ldapgroup mode.
+owner_mode = user
+
 # Default DAG view.  Valid values are:
 # tree, graph, duration, gantt, landing_times
 dag_default_view = tree
@@ -323,6 +325,9 @@ hide_paused_dags_by_default = False
 # Consistent page size across all listing views in the UI
 page_size = 100
 
+# Use FAB-based webserver with RBAC feature
+rbac = False
+
 # Define the color of navigation bar
 navbar_color = #007A87
 
@@ -362,10 +367,6 @@ smtp_ssl = False
 smtp_port = 25
 smtp_mail_from = airflow@example.com
 
-[sentry]
-# Sentry (https://docs.sentry.io) integration
-sentry_dsn = 
-
 
 [celery]
 # This section only applies if you are using the CeleryExecutor in
@@ -487,12 +488,9 @@ job_heartbeat_sec = 5
 # 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
-
-# The number of seconds to wait between consecutive DAG file processing
-processor_poll_interval = 1
+# after how much time should the scheduler terminate in seconds
+# -1 indicates to run continuously (see also num_runs)
+run_duration = -1
 
 # after how much time (seconds) a new DAGs should be picked up from the filesystem
 min_file_process_interval = 0
@@ -541,11 +539,6 @@ statsd_host = localhost
 statsd_port = 8125
 statsd_prefix = airflow
 
-# If you want to avoid send all the available metrics to StatsD,
-# you can configure an allow list of prefixes to send only the metrics that
-# start with the elements of the list (e.g: scheduler,executor,dagrun)
-statsd_allow_list =
-
 # The scheduler can run multiple threads in parallel to schedule dags.
 # This defines how many threads will run.
 max_threads = 2
@@ -574,6 +567,48 @@ search_scope = LEVEL
 # broken schema, or do not return a schema.
 ignore_malformed_schema = False
 
+[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
+
+# Optional Docker Image to run on slave before running the command
+# This image should be accessible from mesos slave i.e mesos slave
+# should be able to pull this docker image before executing the command.
+# docker_image_slave = puckel/docker-airflow
+
 [kerberos]
 ccache = /tmp/airflow_krb5_ccache
 # gets augmented with fqdn
@@ -765,7 +800,9 @@ tolerations =
 # List of supported params in **kwargs are similar for all core_v1_apis, hence a single config variable for all apis
 # See:
 #   https://raw.githubusercontent.com/kubernetes-client/python/master/kubernetes/client/apis/core_v1_api.py
-kube_client_request_args =
+# Note that if no _request_timeout is specified, the kubernetes client will wait indefinitely for kubernetes
+# api responses, which will cause the scheduler to hang. The timeout is specified as [connect timeout, read timeout]
+kube_client_request_args = {{"_request_timeout" : [60,60] }}
 
 # Worker pods security context options
 # See:
@@ -779,17 +816,15 @@ run_as_user =
 # that allows for the key to be read, e.g. 65533
 fs_group =
 
-# Annotations configuration as a single line formatted JSON object.
-# See the naming convention in:
-#   https://kubernetes.io/docs/concepts/overview/working-with-objects/annotations/
-worker_annotations =
-
-
 [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_environment_variables]
 # The scheduler sets the following environment variables into your workers. You may define as
 # many environment variables as needed and the kubernetes launcher will set them in the launched workers.