DAG not running straight out of the box using LocalExecutor with docker-compose?

[ghost]

Using the 1.10.4 airflow version I

• cloned the repo
• run docker-compose -f docker-compose-LocalExecutor.yml up -d (after changing LOAD_EX=y)
• Everything loads up but DAGs are started but never run?

There are some errors in the docker-airflow_webserver_1 container's logs:

ERROR [airflow.models.dagbag.DagBag] Failed to import: /usr/local/lib/python3.7/site-packages/airflow/example_dags/example_subdag_operator.py
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1244, in _execute_context
    cursor, statement, parameters, context
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/default.py", line 552, in do_execute
    cursor.execute(statement, parameters)
psycopg2.ProgrammingError: relation "slot_pool" does not exist
LINE 2: FROM slot_pool 
             ^


The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/airflow/models/dagbag.py", line 202, in process_file
    m = imp.load_source(mod_name, filepath)
  File "/usr/local/lib/python3.7/imp.py", line 171, in load_source
    module = _load(spec)
  File "<frozen importlib._bootstrap>", line 696, in _load
  File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 728, in exec_module
  File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
  File "/usr/local/lib/python3.7/site-packages/airflow/example_dags/example_subdag_operator.py", line 47, in <module>
    dag=dag,
  File "/usr/local/lib/python3.7/site-packages/airflow/utils/db.py", line 74, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/airflow/utils/decorators.py", line 98, in wrapper
    result = func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/airflow/operators/subdag_operator.py", line 77, in __init__
    .filter(Pool.pool == self.pool)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/orm/query.py", line 3228, in first
    ret = list(self[0:1])
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/orm/query.py", line 3018, in __getitem__
    return list(res)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/orm/query.py", line 3330, in __iter__
    return self._execute_and_instances(context)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/orm/query.py", line 3355, in _execute_and_instances
    result = conn.execute(querycontext.statement, self._params)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 988, in execute
    return meth(self, multiparams, params)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/sql/elements.py", line 287, in _execute_on_connection
    return connection._execute_clauseelement(self, multiparams, params)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1107, in _execute_clauseelement
    distilled_params,
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1248, in _execute_context
    e, statement, parameters, cursor, context
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1466, in _handle_dbapi_exception
    util.raise_from_cause(sqlalchemy_exception, exc_info)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/util/compat.py", line 398, in raise_from_cause
    reraise(type(exception), exception, tb=exc_tb, cause=cause)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/util/compat.py", line 152, in reraise
    raise value.with_traceback(tb)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1244, in _execute_context
    cursor, statement, parameters, context
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/default.py", line 552, in do_execute
    cursor.execute(statement, parameters)
sqlalchemy.exc.ProgrammingError: (psycopg2.ProgrammingError) relation "slot_pool" does not exist
LINE 2: FROM slot_pool 
             ^

and the docker-airflow_postgres_1 container's too:

PostgreSQL init process complete; ready for start up.

LOG:  database system was shut down at 2019-10-09 02:15:24 UTC
LOG:  MultiXact member wraparound protections are now enabled
LOG:  autovacuum launcher started
LOG:  database system is ready to accept connections
LOG:  incomplete startup packet
ERROR:  relation "slot_pool" does not exist at character 161
STATEMENT:  SELECT slot_pool.id AS slot_pool_id, slot_pool.pool AS slot_pool_pool, slot_pool.slots AS slot_pool_slots, slot_pool.description AS slot_pool_description 
	FROM slot_pool 
	WHERE slot_pool.slots = 1 AND slot_pool.pool = 'default_pool' 
	 LIMIT 1

Shouldn't this just work out of the box?

[brent-hoover]

I can confirm that I see the same error when run with Examples enabled

[playermanny2]

same problem on this side, @whillas @zenweasel have you all found any work arounds?

[ghost]

Nope @playermanny2, seems to be an issue with the scheduler. Looks like its this issue https://github.com/puckel/docker-airflow/issues/94

[brent-hoover]

I'm not having any problems when I don't have the examples enabled so I think it's something in one of these dags?

[NumesSanguis]

Same problem here (both when examples is LOAD_EX=n or LOAD_EX=y), for both image: puckel/docker-airflow:1.10.4 and image: puckel/docker-airflow:latest in Docker-compose.

If I run DAGs from the web interface, everything is fine. However, if I want to run commands in the Docker container, the airflow command seems to use SequentialExecutor:

# Go inside the Docker container
docker exec -it docker-airflow_webserver_1 /bin/bash

# list tasks
airflow@66f0499a4b18:~$ airflow list_tasks tutorial

# Log output
[2019-10-17 05:38:21,582] {{__init__.py:51}} INFO - Using executor SequentialExecutor
[2019-10-17 05:38:21,856] {{cli_action_loggers.py:70}} ERROR - Failed on pre-execution callback using <function default_action_log at 0x7fbd1b56eef0>

Full error log (CLICK ME)

airflow@66f0499a4b18:~$ airflow list_tasks tutorial
[2019-10-17 05:38:21,582] {{__init__.py:51}} INFO - Using executor SequentialExecutor
[2019-10-17 05:38:21,856] {{cli_action_loggers.py:70}} ERROR - Failed on pre-execution callback using <function default_action_log at 0x7fbd1b56eef0>
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1244, in _execute_context
    cursor, statement, parameters, context
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/default.py", line 552, in do_execute
    cursor.execute(statement, parameters)
sqlite3.OperationalError: no such table: log

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/airflow/utils/cli_action_loggers.py", line 68, in on_pre_execution
    cb(**kwargs)
  File "/usr/local/lib/python3.7/site-packages/airflow/utils/cli_action_loggers.py", line 99, in default_action_log
    session.add(log)
  File "/usr/local/lib/python3.7/contextlib.py", line 119, in __exit__
    next(self.gen)
  File "/usr/local/lib/python3.7/site-packages/airflow/utils/db.py", line 45, in create_session
    session.commit()
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/orm/session.py", line 1027, in commit
    self.transaction.commit()
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/orm/session.py", line 494, in commit
    self._prepare_impl()
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/orm/session.py", line 473, in _prepare_impl
    self.session.flush()
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/orm/session.py", line 2459, in flush
    self._flush(objects)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/orm/session.py", line 2597, in _flush
    transaction.rollback(_capture_exception=True)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/util/langhelpers.py", line 68, in __exit__
    compat.reraise(exc_type, exc_value, exc_tb)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/util/compat.py", line 153, in reraise
    raise value
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/orm/session.py", line 2557, in _flush
    flush_context.execute()
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/orm/unitofwork.py", line 422, in execute
    rec.execute(self)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/orm/unitofwork.py", line 589, in execute
    uow,
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/orm/persistence.py", line 245, in save_obj
    insert,
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/orm/persistence.py", line 1138, in _emit_insert_statements
    statement, params
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 988, in execute
    return meth(self, multiparams, params)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/sql/elements.py", line 287, in _execute_on_connection
    return connection._execute_clauseelement(self, multiparams, params)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1107, in _execute_clauseelement
    distilled_params,
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1248, in _execute_context
    e, statement, parameters, cursor, context
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1466, in _handle_dbapi_exception
    util.raise_from_cause(sqlalchemy_exception, exc_info)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/util/compat.py", line 398, in raise_from_cause
    reraise(type(exception), exception, tb=exc_tb, cause=cause)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/util/compat.py", line 152, in reraise
    raise value.with_traceback(tb)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1244, in _execute_context
    cursor, statement, parameters, context
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/default.py", line 552, in do_execute
    cursor.execute(statement, parameters)
sqlalchemy.exc.OperationalError: (sqlite3.OperationalError) no such table: log
[SQL: INSERT INTO log (dttm, dag_id, task_id, event, execution_date, owner, extra) VALUES (?, ?, ?, ?, ?, ?, ?)]
[parameters: ('2019-10-17 05:38:21.853310', 'tutorial', None, 'cli_list_tasks', None, 'airflow', '{"host_name": "66f0499a4b18", "full_command": "[\'/usr/local/bin/airflow\', \'list_tasks\', \'tutorial\']"}')]
(Background on this error at: http://sqlalche.me/e/e3q8)
[2019-10-17 05:38:21,859] {{dagbag.py:90}} INFO - Filling up the DagBag from /usr/local/airflow/dags
[2019-10-17 05:38:21,879] {{dagbag.py:205}} ERROR - Failed to import: /usr/local/lib/python3.7/site-packages/airflow/example_dags/example_subdag_operator.py
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1244, in _execute_context
    cursor, statement, parameters, context
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/default.py", line 552, in do_execute
    cursor.execute(statement, parameters)
sqlite3.OperationalError: no such table: slot_pool

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.7/site-packages/airflow/models/dagbag.py", line 202, in process_file
    m = imp.load_source(mod_name, filepath)
  File "/usr/local/lib/python3.7/imp.py", line 171, in load_source
    module = _load(spec)
  File "<frozen importlib._bootstrap>", line 696, in _load
  File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 728, in exec_module
  File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
  File "/usr/local/lib/python3.7/site-packages/airflow/example_dags/example_subdag_operator.py", line 47, in <module>
    dag=dag,
  File "/usr/local/lib/python3.7/site-packages/airflow/utils/db.py", line 74, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/airflow/utils/decorators.py", line 98, in wrapper
    result = func(*args, **kwargs)
  File "/usr/local/lib/python3.7/site-packages/airflow/operators/subdag_operator.py", line 77, in __init__
    .filter(Pool.pool == self.pool)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/orm/query.py", line 3228, in first
    ret = list(self[0:1])
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/orm/query.py", line 3018, in __getitem__
    return list(res)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/orm/query.py", line 3330, in __iter__
    return self._execute_and_instances(context)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/orm/query.py", line 3355, in _execute_and_instances
    result = conn.execute(querycontext.statement, self._params)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 988, in execute
    return meth(self, multiparams, params)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/sql/elements.py", line 287, in _execute_on_connection
    return connection._execute_clauseelement(self, multiparams, params)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1107, in _execute_clauseelement
    distilled_params,
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1248, in _execute_context
    e, statement, parameters, cursor, context
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1466, in _handle_dbapi_exception
    util.raise_from_cause(sqlalchemy_exception, exc_info)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/util/compat.py", line 398, in raise_from_cause
    reraise(type(exception), exception, tb=exc_tb, cause=cause)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/util/compat.py", line 152, in reraise
    raise value.with_traceback(tb)
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1244, in _execute_context
    cursor, statement, parameters, context
  File "/usr/local/lib/python3.7/site-packages/sqlalchemy/engine/default.py", line 552, in do_execute
    cursor.execute(statement, parameters)
sqlalchemy.exc.OperationalError: (sqlite3.OperationalError) no such table: slot_pool
[SQL: SELECT slot_pool.id AS slot_pool_id, slot_pool.pool AS slot_pool_pool, slot_pool.slots AS slot_pool_slots, slot_pool.description AS slot_pool_description 
FROM slot_pool 
WHERE slot_pool.slots = ? AND slot_pool.pool = ?
 LIMIT ? OFFSET ?]
[parameters: (1, 'default_pool', 1, 0)]
(Background on this error at: http://sqlalche.me/e/e3q8)
print_date
sleep
templated

It might be related to airflow.cfg in the Docker container (puckel/docker-airflow:latest) still having SequentialExecutor set.

# get file `airflow.cfg` from Docker container and save it in current folder
docker cp docker-airflow_webserver_1:/usr/local/airflow/airflow.cfg ./airflow.cfg
# then open `airflow.cfg` with a text editor
# find the following inside (L46-48):
gedit airflow.cfg
# The executor class that airflow should use. Choices include
# SequentialExecutor, LocalExecutor, CeleryExecutor, DaskExecutor, KubernetesExecutor
executor = SequentialExecutor

Full airflow.cfg from Docker container (CLICK ME)

[core]
# The folder where your airflow pipelines live, most likely a
# subfolder in a code repository
# This path must be absolute
dags_folder = /usr/local/airflow/dags

# The folder where airflow should store its log files
# This path must be absolute
base_log_folder = /usr/local/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
# location. If remote_logging is set to true, see UPDATING.md for additional
# configuration requirements.
remote_logging = False
remote_log_conn_id =
remote_base_log_folder =
encrypt_s3_logs = False

# Logging level
logging_level = INFO
fab_logging_level = WARN

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

# 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_formatter_class = airflow.utils.log.colored_log.CustomTTYColoredFormatter

log_format = [%%(asctime)s] {{%%(filename)s:%%(lineno)d}} %%(levelname)s - %%(message)s
simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s

# Log filename format
# we need to escape the curly braces by adding an additional curly brace
log_filename_template = {{ ti.dag_id }}/{{ ti.task_id }}/{{ ts }}/{{ try_number }}.log
log_processor_filename_template = {{ filename }}.log
dag_processor_manager_log_location = /usr/local/airflow/logs/dag_processor_manager/dag_processor_manager.log

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

# 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

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

# The SqlAlchemy connection string to the metadata database.
# SqlAlchemy supports many different database engine, more information
# their website
# sql_alchemy_conn = sqlite:////tmp/airflow.db

# The encoding for the databases
sql_engine_encoding = utf-8

# 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

# 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.
sql_alchemy_schema =

# The amount of parallelism as a setting to the executor. This defines
# the max number of task instances that should run simultaneously
# on this airflow installation
parallelism = 32

# The number of task instances allowed to run concurrently by the scheduler
dag_concurrency = 16

# Are DAGs paused by default at creation
dags_are_paused_at_creation = True

# When not using pools, tasks are run in the "default pool",
# whose size is guided by this config element
non_pooled_task_slot_count = 128

# The maximum number of active DAG runs per DAG
max_active_runs_per_dag = 16

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

# Where your Airflow plugins are stored
plugins_folder = /usr/local/airflow/plugins

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

# Whether to disable pickling dags
donot_pickle = False

# How long before timing out a python file import while filling the DagBag
dagbag_import_timeout = 30

# The class to use for running task instances in a subprocess
task_runner = StandardTaskRunner

# 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 =

# If set to False enables some unsecure features like Charts and Ad Hoc Queries.
# In 2.0 will default to True.
secure_mode = False

# Turn unit test mode on (overwrites many configuration options with test
# values at runtime)
unit_test_mode = False

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

# Whether to enable pickling for xcom (note that this is insecure and allows for
# RCE exploits). This will be deprecated in Airflow 2.0 (be forced to False).
enable_xcom_pickling = True

# 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 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
worker_precheck = False

# When discovering DAGs, ignore any files that don't contain the strings `DAG` and `airflow`.
dag_discovery_safe_mode = True

[cli]
# In what way should the cli access the API. The LocalClient will use the
# database directly, while the json_client will use the api running on the
# webserver
api_client = airflow.api.client.local_client

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

[api]
# How to authenticate users of the API
auth_backend = airflow.api.auth.backend.default

[lineage]
# what lineage backend to use
backend =

[atlas]
sasl_enabled = False
host =
port = 21000
username =
password =

[operators]
# The default owner assigned to each new operator, unless
# provided explicitly or passed via `default_args`
default_owner = airflow
default_cpus = 1
default_ram = 512
default_disk = 512
default_gpus = 0

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

[webserver]
# The base url of your website as airflow cannot guess what domain or
# cname you are using. This is used in automated emails that
# airflow sends to point links to the right web server
base_url = http://localhost:8080

# The ip specified when starting the web server
web_server_host = 0.0.0.0

# The port on which to run the web server
web_server_port = 8080

# Paths to the SSL certificate and key for the web server. When both are
# provided SSL will be enabled. This does not change the web server port.
web_server_ssl_cert =
web_server_ssl_key =

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

# 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

# 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

# Use FAB-based webserver with RBAC feature
rbac = False

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

# Default dagrun to show in UI
default_dag_run_display_number = 25

# Enable werkzeug `ProxyFix` middleware
enable_proxy_fix = False

# Set secure flag on session cookie
cookie_secure = False

# Set samesite policy on session cookie
cookie_samesite =

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

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

[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 = [email protected]

[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

# The maximum and minimum concurrency that will be used when starting workers with the
# "airflow 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
# worker_autoscale = 16,12

# 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 = redis://redis:6379/1

# 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+postgresql://airflow:airflow@postgres/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

# In case of using SSL
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

[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
#
#visibility_timeout = 21600

[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

# 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

# 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
print_stats_interval = 30

# 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
# This is used by the health check in the "/health" endpoint
scheduler_health_check_threshold = 30

child_process_log_directory = /usr/local/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

# 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

# This changes the batch size of queries in the scheduling main loop.
# If this is too high, SQL query performance may be impacted by one
# or more of the following:
#  - reversion to full table scan
#  - complexity of query predicate
#  - 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

# Statsd (https://github.com/etsy/statsd) integration settings
statsd_on = False
statsd_host = localhost
statsd_port = 8125
statsd_prefix = airflow

# The scheduler can run multiple threads in parallel to schedule dags.
# This defines how many threads will run.
max_threads = 2

authenticate = False

# 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

[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

# This setting allows the use of LDAP servers that either return a
# 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
principal = airflow
reinit_frequency = 3600
kinit_path = kinit
keytab = airflow.keytab

[github_enterprise]
api_rev = v3

[admin]
# UI to hide sensitive variable fields when set to True
hide_sensitive_variable_fields = 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}}-{{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
# Code will construct log_id using the log_id template from the argument above.
# NOTE: The code will prefix the https:// automatically, don't include that here.
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

[kubernetes]
# The repository, tag and imagePullPolicy of the Kubernetes Image for the Worker to Run
worker_container_repository =
worker_container_tag =
worker_container_image_pull_policy = IfNotPresent

# If True (default), worker pods will be deleted upon termination
delete_worker_pods = True

# Number of Kubernetes Worker Pod creation calls per scheduler loop
worker_pods_creation_batch_size = 1

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

# The name of the Kubernetes ConfigMap Containing the Airflow Configuration (this file)
airflow_configmap =

# For docker image already contains DAGs, this is set to `True`, and the worker will search for dags in dags_folder,
# otherwise use git sync or dags volume claim to mount DAGs
dags_in_image = False

# For either git sync or volume mounted DAGs, the worker will look in this subpath for DAGs
dags_volume_subpath =

# For DAGs mounted via a volume claim (mutually exclusive with git-sync and host path)
dags_volume_claim =

# For volume mounted logs, the worker will look in this subpath for logs
logs_volume_subpath =

# A shared volume claim for the logs
logs_volume_claim =

# For DAGs mounted via a hostPath volume (mutually exclusive with volume claim and git-sync)
# Useful in local environment, discouraged in production
dags_volume_host =

# A hostPath volume for the logs
# Useful in local environment, discouraged in production
logs_volume_host =

# A list of configMapsRefs to envFrom. If more than one configMap is
# specified, provide a comma separated list: configmap_a,configmap_b
env_from_configmap_ref =

# A list of secretRefs to envFrom. If more than one secret is
# specified, provide a comma separated list: secret_a,secret_b
env_from_secret_ref =

# Git credentials and repository for DAGs mounted via Git (mutually exclusive with volume claim)
git_repo =
git_branch =
git_subpath =
# Use git_user and git_password for user authentication or git_ssh_key_secret_name and git_ssh_key_secret_key
# for SSH authentication
git_user =
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
git_dags_folder_mount_point =

# To get Git-sync SSH authentication set up follow this format
#
# airflow-secrets.yaml:
# ---
# apiVersion: v1
# kind: Secret
# metadata:
#   name: airflow-secrets
# data:
#   # key needs to be gitSshKey
#   gitSshKey: <base64_encoded_data>
# ---
# airflow-configmap.yaml:
# apiVersion: v1
# kind: ConfigMap
# metadata:
#   name: airflow-configmap
# data:
#   known_hosts: |
#       github.com ssh-rsa <...>
#   airflow.cfg: |
#       ...
#
# git_ssh_key_secret_name = airflow-secrets
# git_ssh_known_hosts_configmap_name = airflow-configmap
git_ssh_key_secret_name =
git_ssh_known_hosts_configmap_name =

# For cloning DAGs from git repositories into volumes: https://github.com/kubernetes/git-sync
git_sync_container_repository = k8s.gcr.io/git-sync
git_sync_container_tag = v3.1.1
git_sync_init_container_name = git-sync-clone

# The name of the Kubernetes service account to be associated with airflow workers, if any.
# Service accounts are required for workers that require access to secrets or cluster resources.
# See the Kubernetes RBAC documentation for more:
#   https://kubernetes.io/docs/admin/authorization/rbac/
worker_service_account_name =

# Any image pull secrets to be given to worker pods, If more than one secret is
# required, provide a comma separated list: secret_a,secret_b
image_pull_secrets =

# GCP Service Account Keys to be provided to tasks run on Kubernetes Executors
# Should be supplied in the format: key-name-1:key-path-1,key-name-2:key-path-2
gcp_service_account_keys =

# 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 =
# config_file =


# Affinity configuration as a single line formatted JSON object.
# See the affinity model for top-level key names (e.g. `nodeAffinity`, etc.):
#   https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.12/#affinity-v1-core
affinity =

# A list of toleration objects as a single line formatted JSON array
# See:
#   https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.12/#toleration-v1-core
tolerations =

# **kwargs 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 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 =

# Worker pods security context options
# See:
#   https://kubernetes.io/docs/tasks/configure-pod-container/security-context/

# Specifies the uid to run the first process of the worker pods containers as
run_as_user =

# Specifies a gid to associate with all containers in the worker pods
# if using a git_ssh_key_secret_name use an fs_group
# that allows for the key to be read, e.g. 65533
fs_group =

[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.
# Environment variables in this section are defined as follows
#     <environment_variable_key> = <environment_variable_value>
#
# For example if you wanted to set an environment variable with value `prod` and key
# `ENVIRONMENT` you would follow the following format:
#     ENVIRONMENT = prod
#
# Additionally you may override worker airflow settings with the AIRFLOW__<SECTION>__<KEY>
# formatting as supported by airflow normally.

[kubernetes_secrets]
# The scheduler mounts the following secrets into your workers as they are launched by the
# scheduler. You may define as many secrets as needed and the kubernetes launcher will parse the
# defined secrets and mount them as secret environment variables in the launched workers.
# Secrets in this section are defined as follows
#     <environment_variable_mount> = <kubernetes_secret_object>=<kubernetes_secret_key>
#
# For example if you wanted to mount a kubernetes secret key named `postgres_password` from the
# kubernetes secret object `airflow-secret` as the environment variable `POSTGRES_PASSWORD` into
# your workers you would follow the following format:
#     POSTGRES_PASSWORD = airflow-secret=postgres_credentials
#
# Additionally you may override worker airflow settings with the AIRFLOW__<SECTION>__<KEY>
# formatting as supported by airflow normally.

[kubernetes_labels]
# The Key-value pairs to be given to worker pods.
# The worker pods will be given these static labels, as well as some additional dynamic labels
# to identify the task.
# Should be supplied in the format: key = value

[NumesSanguis]

@whillas Can you edit the title to include using LocalExecutor with docker-compose? That would make this issue more descriptive.

[NumesSanguis]

From the Apache Airflow SLACK channel I learned that the issue for me was that the export in entrypoint.sh only stores the variables in that bash session. That means a new bash session would not have access to the variables set, and therefore it uses the default SequentialExecutor.

There are 2 solutions for this:

1. Enter bash through the entrypoint.sh with: docker exec -it docker-airflow_webserver_1 /entrypoint.sh bash

   • This works because an argument like bash skips the starting of the server: https://github.com/puckel/docker-airflow/blob/master/script/entrypoint.sh#L93-L96

2. Have the entrypoint.sh export the values to .bashrc by adding echo "export AIRFLOW__" lines, which means you have to replace the contents of docker-airflow/script/entrypoint.sh with:

entrypoint.sh (CLICK ME)

#!/usr/bin/env bash

TRY_LOOP="20"

: "${REDIS_HOST:="redis"}"
: "${REDIS_PORT:="6379"}"
: "${REDIS_PASSWORD:=""}"

: "${POSTGRES_HOST:="postgres"}"
: "${POSTGRES_PORT:="5432"}"
: "${POSTGRES_USER:="airflow"}"
: "${POSTGRES_PASSWORD:="airflow"}"
: "${POSTGRES_DB:="airflow"}"

# Defaults and back-compat
: "${AIRFLOW_HOME:="/usr/local/airflow"}"
: "${AIRFLOW__CORE__FERNET_KEY:=${FERNET_KEY:=$(python -c "from cryptography.fernet import Fernet; FERNET_KEY = Fernet.generate_key().decode(); print(FERNET_KEY)")}}"
: "${AIRFLOW__CORE__EXECUTOR:=${EXECUTOR:-Sequential}Executor}"

echo "export AIRFLOW_HOME=${AIRFLOW_HOME}" >> ~/.bashrc
# might change later in this script
#export AIRFLOW__CELERY__BROKER_URL
# echo "export AIRFLOW__CELERY__BROKER_URL=${AIRFLOW__CELERY__BROKER_URL}" >> ~/.bashrc
echo "export AIRFLOW__CELERY__RESULT_BACKEND=${AIRFLOW__CELERY__RESULT_BACKEND}" >> ~/.bashrc
echo "export AIRFLOW__CORE__EXECUTOR=${AIRFLOW__CORE__EXECUTOR}" >> ~/.bashrc
echo "export AIRFLOW__CORE__FERNET_KEY=${AIRFLOW__CORE__FERNET_KEY}" >> ~/.bashrc
# might change later in this script
#export AIRFLOW__CORE__LOAD_EXAMPLES
# echo "export AIRFLOW__CORE__LOAD_EXAMPLES=${AIRFLOW__CORE__LOAD_EXAMPLES}" >> ~/.bashrc
echo "export AIRFLOW__CORE__SQL_ALCHEMY_CONN=${AIRFLOW__CORE__SQL_ALCHEMY_CONN}" >> ~/.bashrc

export \
  AIRFLOW_HOME \
  AIRFLOW__CELERY__BROKER_URL \
  AIRFLOW__CELERY__RESULT_BACKEND \
  AIRFLOW__CORE__EXECUTOR \
  AIRFLOW__CORE__FERNET_KEY \
  AIRFLOW__CORE__LOAD_EXAMPLES \
  AIRFLOW__CORE__SQL_ALCHEMY_CONN \

# Load DAGs exemples (default: Yes)
if [[ -z "$AIRFLOW__CORE__LOAD_EXAMPLES" && "${LOAD_EX:=n}" == n ]]
then
  AIRFLOW__CORE__LOAD_EXAMPLES=False
fi
# echo "export AIRFLOW__CORE__LOAD_EXAMPLES=${AIRFLOW__CORE__LOAD_EXAMPLES}" >> ~/.bashrc

# source ~/.bashrc

# Install custom python package if requirements.txt is present
if [ -e "/requirements.txt" ]; then
    $(command -v pip) install --user -r /requirements.txt
fi

if [ -n "$REDIS_PASSWORD" ]; then
    REDIS_PREFIX=:${REDIS_PASSWORD}@
else
    REDIS_PREFIX=
fi
echo "export REDIS_PREFIX=${REDIS_PREFIX}" >> ~/.bashrc

wait_for_port() {
  local name="$1" host="$2" port="$3"
  local j=0
  while ! nc -z "$host" "$port" >/dev/null 2>&1 < /dev/null; do
    j=$((j+1))
    if [ $j -ge $TRY_LOOP ]; then
      echo >&2 "$(date) - $host:$port still not reachable, giving up"
      exit 1
    fi
    echo "$(date) - waiting for $name... $j/$TRY_LOOP"
    sleep 5
  done
}

if [ "$AIRFLOW__CORE__EXECUTOR" != "SequentialExecutor" ]; then
  AIRFLOW__CORE__SQL_ALCHEMY_CONN="postgresql+psycopg2://$POSTGRES_USER:$POSTGRES_PASSWORD@$POSTGRES_HOST:$POSTGRES_PORT/$POSTGRES_DB"
  echo "export AIRFLOW__CORE__SQL_ALCHEMY_CONN=${AIRFLOW__CORE__SQL_ALCHEMY_CONN}" >> ~/.bashrc
  AIRFLOW__CELERY__RESULT_BACKEND="db+postgresql://$POSTGRES_USER:$POSTGRES_PASSWORD@$POSTGRES_HOST:$POSTGRES_PORT/$POSTGRES_DB"
  wait_for_port "Postgres" "$POSTGRES_HOST" "$POSTGRES_PORT"
  echo "export AIRFLOW__CELERY__RESULT_BACKEND=${AIRFLOW__CELERY__RESULT_BACKEND}" >> ~/.bashrc
fi

if [ "$AIRFLOW__CORE__EXECUTOR" = "CeleryExecutor" ]; then
  AIRFLOW__CELERY__BROKER_URL="redis://$REDIS_PREFIX$REDIS_HOST:$REDIS_PORT/1"
  wait_for_port "Redis" "$REDIS_HOST" "$REDIS_PORT"
fi
echo "export AIRFLOW__CELERY__BROKER_URL=${AIRFLOW__CELERY__BROKER_URL}" >> ~/.bashrc

#source ~/.bashrc

case "$1" in
  webserver)
    airflow initdb
    if [ "$AIRFLOW__CORE__EXECUTOR" = "LocalExecutor" ] || [ "$AIRFLOW__CORE__EXECUTOR" = "SequentialExecutor" ]; then
      # With the "Local" and "Sequential" executors it should all run in one container.
      airflow scheduler &
    fi
    exec airflow webserver
    ;;
  worker|scheduler)
    # To give the webserver time to run initdb.
    sleep 10
    exec airflow "$@"
    ;;
  flower)
    sleep 10
    exec airflow "$@"
    ;;
  version)
    exec airflow "$@"
    ;;
  *)
    # The command is something like bash, not an airflow subcommand. Just run it in the right environment.
    exec "$@"
    ;;
esac

For solution 2, also add build information to docker-compose-LocalExecutor.yml, so that we can rebuild the Docker image by adding --build:

    webserver:
        # get puckel image from DockerHub
        image: puckel/docker-airflow:latest
        # ADDED: recreate puckel image
        build:
            context: .
            dockerfile: Dockerfile

Then rebuild the Docker image with: docker-compose -f docker-compose-LocalExecutor.yml up --detach --build

[amit-jha]

Some of the example are having issues. I was able to get rid of this error by changing load_examples = False in airflow.cfg

[esko22]

Multiple ways you can go about it but simply making sure AIRFLOW__CORE__SQL_ALCHEMY_CONN is exported properly worked for me, as mentioned by @NumesSanguis .

[mindatasleep]

Running Airflow with Celery from docker-compose in an EC2, I'd exec into the webserver and see the following error when running airflow commands. It seemed odd since the same configuration worked locally.

airflow@2290c6f1879a:~$ airflow initdb
Traceback (most recent call last):
  File "/usr/local/bin/airflow", line 25, in <module>
    from airflow.configuration import conf
  File "/usr/local/lib/python3.7/site-packages/airflow/__init__.py", line 31, in <module>
    from airflow.utils.log.logging_mixin import LoggingMixin
  File "/usr/local/lib/python3.7/site-packages/airflow/utils/__init__.py", line 24, in <module>
    from .decorators import apply_defaults as _apply_defaults
  File "/usr/local/lib/python3.7/site-packages/airflow/utils/decorators.py", line 36, in <module>
    from airflow import settings
  File "/usr/local/lib/python3.7/site-packages/airflow/settings.py", line 37, in <module>
    from airflow.configuration import conf, AIRFLOW_HOME, WEBSERVER_CONFIG  # NOQA F401
  File "/usr/local/lib/python3.7/site-packages/airflow/configuration.py", line 731, in <module>
    conf.read(AIRFLOW_CONFIG)
  File "/usr/local/lib/python3.7/site-packages/airflow/configuration.py", line 421, in read
    self._validate()
  File "/usr/local/lib/python3.7/site-packages/airflow/configuration.py", line 213, in _validate
    self._validate_config_dependencies()
  File "/usr/local/lib/python3.7/site-packages/airflow/configuration.py", line 247, in _validate_config_dependencies
    self.get('core', 'executor')))
airflow.exceptions.AirflowConfigException: error: cannot use sqlite with the CeleryExecutor

@NumesSanguis's advice, to enter via entrypoint with docker exec -it docker-airflow_webserver_1 /entrypoint.sh bash, solved my issue.

[pinxtor]

It also matters when the "airflow initdb" command is called. It should always be called before starting the webserver. I updated the docker compose file under the webserver service to run the command instead. services webserver: command: bash -c "airflow initdb && airflow webserver"