brent-hoover
commented
4 years ago I can confirm that I see the same error when run with Examples enabled
Open ghost opened 4 years ago
brent-hoover
commented
4 years ago I can confirm that I see the same error when run with Examples enabled
playermanny2
commented
4 years ago same problem on this side, @whillas @zenweasel have you all found any work arounds?
ghost
commented
4 years ago 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
commented
4 years ago 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
commented
4 years ago 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>
```bash
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
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
```cfg
[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 = airflow@example.com
[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://
NumesSanguis
commented
4 years ago @whillas Can you edit the title to include using LocalExecutor with docker-compose? That would make this issue more descriptive.
NumesSanguis
commented
4 years ago 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:
Enter bash through the entrypoint.sh with: docker exec -it docker-airflow_webserver_1 /entrypoint.sh bash
bash skips the starting of the server: https://github.com/puckel/docker-airflow/blob/master/script/entrypoint.sh#L93-L96Have 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:
``` #!/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 "$@" ;; esac ```
For solution 2, also add build information to docker-compose-LocalExecutor.yml, so that we can rebuild the Docker image by adding --build:
webserver:
# get puckel image from DockerHub
image: puckel/docker-airflow:latest
# ADDED: recreate puckel image
build:
context: .
dockerfile: DockerfileThen rebuild the Docker image with: docker-compose -f docker-compose-LocalExecutor.yml up --detach --build
amit-jha
commented
4 years ago Some of the example are having issues. I was able to get rid of this error by changing load_examples = False in airflow.cfg
esko22
commented
4 years ago Multiple ways you can go about it but simply making sure AIRFLOW__CORE__SQL_ALCHEMY_CONN is exported properly worked for me, as mentioned by @NumesSanguis .
mindatasleep
commented
3 years ago Running Airflow with Celery from docker-compose in an EC2, I'd exec into the webserver and see the following error when running airflow commands. It seemed odd since the same configuration worked locally.
airflow@2290c6f1879a:~$ 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
commented
3 years ago 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"
Using the 1.10.4 airflow version I
docker-compose -f docker-compose-LocalExecutor.yml up -d(after changingLOAD_EX=y)There are some errors in the
docker-airflow_webserver_1container's logs:and the
docker-airflow_postgres_1container's too:Shouldn't this just work out of the box?