Issue 1:
Apache Airflow version

2.5.1

What happened

when i run the code i cant see the dags graph

What you think should happen instead

the dag must appears in the graph view

How to reproduce

run this code

from airflow.decorators import dag,task

from datetime import datetime

@task

def make_list():

return [1, 2, 3, 4]

@task

def consumer(arg):

print(arg)

@task

def add_one(arg):

if(arg == 3):

raise ValueError("the value is 3 ")

return arg+1

@dag(dag_id="dynamic-task-map",

start_date=datetime(2022, 4, 2),

catchup=False)

def create_dag():

add_one_to_list = add_one.expand(arg=make_list())

consumer.expand(arg=add_one_to_list)

dag_instance = create_dag()

Operating System

windows

Versions of Apache Airflow Providers

No response

Deployment

Docker-Compose

Deployment details

Docker Compose version v2.15.1

yaml 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:2.5.1
# AIRFLOW_UID - User ID in Airflow containers
# Default: 50000
# AIRFLOW_PROJ_DIR - Base path to which all the files will be volumed.
# Default: .
# 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
# In order to add custom dependencies or upgrade provider packages you can use your extended image.
# Comment the image line, place your Dockerfile in the directory where you placed the docker-compose.yaml
# and uncomment the "build" line below, Then run `docker-compose build` to build the images.
image: ${AIRFLOW_IMAGE_NAME:-apache/airflow:2.5.1}
# build: .
environment:
&airflow-common-env
# AIRFLOW__CORE__EXECUTOR: CeleryExecutor
AIRFLOW__CORE__EXECUTOR: LocalExecutor
AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow
# For backward compatibility, with Airflow <2.3
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: 'true'
AIRFLOW__API__AUTH_BACKENDS: 'airflow.api.auth.backend.basic_auth,airflow.api.auth.backend.session'
_PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:-}
volumes:
- ${AIRFLOW_PROJ_DIR:-.}/dags:/opt/airflow/dags
- ${AIRFLOW_PROJ_DIR:-.}/logs:/opt/airflow/logs
- ${AIRFLOW_PROJ_DIR:-.}/plugins:/opt/airflow/plugins
# - ${AIRFLOW_PROJ_DIR:-.}/config/airflow.cfg:/opt/airflow/airflow.cfg
user: "${AIRFLOW_UID:-50000}:0"
depends_on:
&airflow-common-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
ports:
- 5432:5432
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
# expose:
# - 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
depends_on:
<<: *airflow-common-depends-on
airflow-init:
condition: service_completed_successfully

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
depends_on:
<<: *airflow-common-depends-on
airflow-init:
condition: service_completed_successfully

# 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
# environment:
# <<: *airflow-common-env
# # Required to handle warm shutdown of the celery workers properly
# # See https://airflow.apache.org/docs/docker-stack/entrypoint.html#signal-propagation
# DUMB_INIT_SETSID: "0"
# restart: always
# depends_on:
# <<: *airflow-common-depends-on
# airflow-init:
# condition: service_completed_successfully

# airflow-triggerer:
# <<: *airflow-common
# command: triggerer
# healthcheck:
# test: ["CMD-SHELL", 'airflow jobs check --job-type TriggererJob --hostname "$${HOSTNAME}"']
# interval: 10s
# timeout: 10s
# retries: 5
# restart: always
# depends_on:
# <<: *airflow-common-depends-on
# airflow-init:
# condition: service_completed_successfully

airflow-init:
<<: *airflow-common
entrypoint: /bin/bash
# yamllint disable rule:line-length
command:
- -c
- |
function ver() {
printf "%04d%04d%04d%04d" $${1//./ }
}
airflow_version=$$(AIRFLOW__LOGGING__LOGGING_LEVEL=INFO && gosu airflow airflow version)
airflow_version_comparable=$$(ver $${airflow_version})
min_airflow_version=2.2.0
min_airflow_version_comparable=$$(ver $${min_airflow_version})
if (( airflow_version_comparable < min_airflow_version_comparable )); then
echo
echo -e "\033[1;31mERROR!!!: Too old Airflow version $${airflow_version}!\e[0m"
echo "The minimum Airflow version supported: $${min_airflow_version}. Only use this or higher!"
echo
exit 1
fi
if [[ -z "${AIRFLOW_UID}" ]]; then
echo
echo -e "\033[1;33mWARNING!!!: AIRFLOW_UID not set!\e[0m"
echo "If you are on Linux, you SHOULD follow the instructions below to set "
echo "AIRFLOW_UID environment variable, otherwise files will be owned by root."
echo "For other operating systems you can get rid of the warning with manually created .env file:"
echo " See: https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#setting-the-right-airflow-user"
echo
fi
one_meg=1048576
mem_available=$$(($$(getconf _PHYS_PAGES) * $$(getconf PAGE_SIZE) / one_meg))
cpus_available=$$(grep -cE 'cpu[0-9]+' /proc/stat)
disk_available=$$(df / | tail -1 | awk '{print $$4}')
warning_resources="false"
if (( mem_available < 4000 )) ; then
echo
echo -e "\033[1;33mWARNING!!!: Not enough memory available for Docker.\e[0m"
echo "At least 4GB of memory required. You have $$(numfmt --to iec $$((mem_available * one_meg)))"
echo
warning_resources="true"
fi
if (( cpus_available < 2 )); then
echo
echo -e "\033[1;33mWARNING!!!: Not enough CPUS available for Docker.\e[0m"
echo "At least 2 CPUs recommended. You have $${cpus_available}"
echo
warning_resources="true"
fi
if (( disk_available < one_meg * 10 )); then
echo
echo -e "\033[1;33mWARNING!!!: Not enough Disk space available for Docker.\e[0m"
echo "At least 10 GBs recommended. You have $$(numfmt --to iec $$((disk_available * 1024 )))"
echo
warning_resources="true"
fi
if [[ $${warning_resources} == "true" ]]; then
echo
echo -e "\033[1;33mWARNING!!!: You have not enough resources to run Airflow (see above)!\e[0m"
echo "Please follow the instructions to increase amount of resources available:"
echo " https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#before-you-begin"
echo
fi
mkdir -p /sources/logs /sources/dags /sources/plugins
chown -R "${AIRFLOW_UID}:0" /sources/{logs,dags,plugins}
exec /entrypoint airflow version
# yamllint enable rule:line-length
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}
_PIP_ADDITIONAL_REQUIREMENTS: ''
user: "0:0"
volumes:
- ${AIRFLOW_PROJ_DIR:-.}:/sources

airflow-cli:
<<: *airflow-common
profiles:
- debug
environment:
<<: *airflow-common-env
CONNECTION_CHECK_MAX_COUNT: "0"
# Workaround for entrypoint issue. See: https://github.com/apache/airflow/issues/16252
command:
- bash
- -c
- airflow

# You can enable flower by adding "--profile flower" option e.g. docker-compose --profile flower up
# or by explicitly targeted on the command line e.g. docker-compose up flower.
# See: https://docs.docker.com/compose/profiles/
# flower:
# <<: *airflow-common
# command: celery flower
# profiles:
# - flower
# ports:
# - 5555:5555
# healthcheck:
# test: ["CMD", "curl", "--fail", "http://localhost:5555/"]
# interval: 10s
# timeout: 10s
# retries: 5
# restart: always
# depends_on:
# <<: *airflow-common-depends-on
# airflow-init:
# condition: service_completed_successfully

volumes:
postgres-db-volume:

Anything else

No response

Are you willing to submit PR?

Yes I am willing to submit a PR!

Code of Conduct

I agree to follow this project's Code of Conduct

Issue 2:
Apache Airflow version

2.5.1

What happened

when using a dynamically generated task that gets the expand data from xcom after a branch_task the graph doesn't render.

It reappears once the dag run is finished.

tried with BashOperator and a KubernetesPodOperator.

the developer console in the browser shows the error:

Uncaught TypeError: Cannot read properties of undefined (reading 'length') at z (graph.1c0596dfced26c638bfe.js:2:17499) at graph.1c0596dfced26c638bfe.js:2:17654 at Array.map (<anonymous>) at z (graph.1c0596dfced26c638bfe.js:2:17646) at graph.1c0596dfced26c638bfe.js:2:26602 at graph.1c0596dfced26c638bfe.js:2:26655 at graph.1c0596dfced26c638bfe.js:2:26661 at graph.1c0596dfced26c638bfe.js:2:222 at graph.1c0596dfced26c638bfe.js:2:227 z @ graph.1c0596dfced26c638bfe.js:2 (anonymous) @ graph.1c0596dfced26c638bfe.js:2 z @ graph.1c0596dfced26c638bfe.js:2 (anonymous) @ graph.1c0596dfced26c638bfe.js:2 (anonymous) @ graph.1c0596dfced26c638bfe.js:2 (anonymous) @ graph.1c0596dfced26c638bfe.js:2 (anonymous) @ graph.1c0596dfced26c638bfe.js:2 (anonymous) @ graph.1c0596dfced26c638bfe.js:2

grid view renders fine.

What you think should happen instead

graph should be rendered.

How to reproduce

def branch_dynamic_flow():
@branch_task
def choose_path():
return 'b'

@task
def a():
print('a')

@task
def get_args():
return ['echo 1', 'echo 2']

b = BashOperator.partial(task_id="b").expand(bash_command=get_args())
path = choose_path()
path >> a()
path >> b

Operating System

red hat

Versions of Apache Airflow Providers

apache-airflow-providers-cncf-kubernetes | 5.1.1 | Kubernetes

Deployment

Official Apache Airflow Helm Chart

Deployment details

No response

Anything else

No response

Are you willing to submit PR?

Yes I am willing to submit a PR!

Code of Conduct

I agree to follow this project's Code of Conduct