Airflow tasks stucked in queue v2.1.4 - docker

Question

I have made some good experiences with Airflow in the past, unfortunately only as a user and I was not ressponsible for the setup. Now I want to setup Airflow on my own but I am really struggeling with it because tasks are stucked in the queue after a short period of time.
As you can see I am using DockerOperator to run tasks in Docker containers.
This is my Dockerfile:
FROM apache/airflow:2.1.4
RUN pip install --no-cache-dir apache-airflow-providers-docker==2.5.0 boto3==1.21.45 apache-airflow-providers-amazon==3.0.0
And here is my docker-compose file:
# 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.
#
# Basic Airflow cluster configuration for CeleryExecutor with Redis and PostgreSQL.
#
# WARNING: This configuration is for local development. Do not use it in a production deployment.
#
# This configuration supports basic configuration using environment variables or an .env file
# The following variables are supported:
#
# AIRFLOW_IMAGE_NAME - Docker image name used to run Airflow.
# Default: apache/airflow:master-python3.8
# AIRFLOW_UID - User ID in Airflow containers
# Default: 50000
# AIRFLOW_GID - Group ID in Airflow containers
# Default: 50000
#
# Those configurations are useful mostly in case of standalone testing/running Airflow in test/try-out mode
#
# _AIRFLOW_WWW_USER_USERNAME - Username for the administrator account (if requested).
# Default: airflow
# _AIRFLOW_WWW_USER_PASSWORD - Password for the administrator account (if requested).
# Default: airflow
# _PIP_ADDITIONAL_REQUIREMENTS - Additional PIP requirements to add when starting all containers.
# Default: ''
#
# Feel free to modify this file to suit your needs.
---
version: '3'
x-airflow-common:
&airflow-common
image: airflow_image:latest
environment:
&airflow-common-env
AIRFLOW__CORE__EXECUTOR: CeleryExecutor
AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow#postgres/airflow
AIRFLOW__CELERY__RESULT_BACKEND: db+postgresql://airflow:airflow#postgres/airflow
AIRFLOW__CELERY__BROKER_URL: redis://:#redis:6379/0
AIRFLOW__CORE__FERNET_KEY: ''
AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION: 'true'
AIRFLOW__CORE__LOAD_EXAMPLES: 'false'
AIRFLOW__CORE__ENABLE_XCOM_PICKLING: 'true'
AIRFLOW__API__AUTH_BACKEND: 'airflow.api.auth.backend.basic_auth'
AIRFLOW__SCHEDULER__MIN_FILE_PROCESS_INTERVAL: 60
_PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:-}
SATELLITE_ARCHIVE_MOUNT: ${SATELLITE_ARCHIVE_MOUNT}
NWP_ARCHIVE_MOUNT: ${NWP_ARCHIVE_MOUNT}
ECCODES_DEFINITION_PATH: ${ECCODES_DEFINITION_PATH}
AWS_DEFAULT_REGION: ${AWS_DEFAULT_REGION}
volumes:
- ./dags:/opt/airflow/dags
- ./logs:/opt/airflow/logs
- ./plugins:/opt/airflow/plugins
- /var/run/docker.sock:/var/run/docker.sock
user: "${AIRFLOW_UID:-50000}:${AIRFLOW_GID:-50000}"
depends_on:
redis:
condition: service_healthy
postgres:
condition: service_healthy
services:
postgres:
image: postgres:13
environment:
POSTGRES_USER: airflow
POSTGRES_PASSWORD: airflow
POSTGRES_DB: airflow
volumes:
- postgres-db-volume:/var/lib/postgresql/data
healthcheck:
test: ["CMD", "pg_isready", "-U", "airflow"]
interval: 5s
retries: 5
restart: always
redis:
image: redis:latest
ports:
- 6379:6379
healthcheck:
test: ["CMD", "redis-cli", "ping"]
interval: 5s
timeout: 30s
retries: 50
restart: always
airflow-webserver:
<<: *airflow-common
command: webserver
ports:
- 8080:8080
healthcheck:
test: ["CMD", "curl", "--fail", "http://localhost:8080/health"]
interval: 10s
timeout: 10s
retries: 5
restart: always
airflow-scheduler:
<<: *airflow-common
command: scheduler
healthcheck:
test: ["CMD-SHELL", 'airflow jobs check --job-type SchedulerJob --hostname "$${HOSTNAME}"']
interval: 10s
timeout: 10s
retries: 5
restart: always
airflow-worker:
<<: *airflow-common
command: celery worker
healthcheck:
test: ["CMD-SHELL", 'celery --app airflow.executors.celery_executor.app inspect ping -d "celery#$${HOSTNAME}"']
interval: 10s
timeout: 10s
retries: 5
restart: always
airflow-init:
<<: *airflow-common
command: version
environment:
<<: *airflow-common-env
_AIRFLOW_DB_UPGRADE: 'true'
_AIRFLOW_WWW_USER_CREATE: 'true'
_AIRFLOW_WWW_USER_USERNAME: ${_AIRFLOW_WWW_USER_USERNAME:-airflow}
_AIRFLOW_WWW_USER_PASSWORD: ${_AIRFLOW_WWW_USER_PASSWORD:-airflow}
flower:
<<: *airflow-common
command: celery flower
ports:
- 5555:5555
healthcheck:
test: ["CMD", "curl", "--fail", "http://localhost:5555/"]
interval: 10s
timeout: 10s
retries: 5
restart: always
volumes:
postgres-db-volume:
I hope anyone of you is able to help me. Using another task scheduler, all tasks run fine, but the task scheduler does not have features like retries with persisting parameters this is why I want to move to Airflow.
Thanks in advance
PS: As a side effect, the tasks run slower in Airflow. Maybe some of you know why?