Scheduled DAG data_interval_end and logical_date are identical

[manisnitt]

Apache Airflow version

Other Airflow 3 version (please specify below)

If "Other Airflow 3 version" selected, which one?

3.1.3

What happened?

I started running dags daily and I see that the logical_date, data_interval_start and data_interval_end all are same. I tried pulling different ways but always all 3 dates are same. I have given codes as well to reproduce. Also it is scheduled run not manual run. So Dag id created like scheduled__2026-01-02T00:00:00+00:00 . I understand if I do maunal run then all dates will be same but here in scheduled run as well same dates are printing due to which incremetal loading is not working.

What you think should happen instead?

I was expecting logical_date should be 2 Jan , start_date should be 2 Jan and end_date should be 3 Jan if Dag is running for 3rd Jan. So that all data from 2Jan will be pulled. Also when I ran for every 30 min same case was there.

How to reproduce

`from datetime import datetime
from airflow.sdk import dag, task
from airflow.sdk import get_current_context

@dag(
start_date=datetime(2025, 12, 31),
schedule="@daily",
catchup=True,
is_paused_upon_creation=False,
)
def print_execution_dates_v2():

@task
def print_actual_interval():
    context = get_current_context()

    start = context["data_interval_start"]
    end = context["data_interval_end"]

    dag_run = context["dag_run"]
    logical = dag_run.logical_date
    run_after = dag_run.run_after

    print(f"Period Start:  {start}")
    print(f"Period End:    {end}")
    print(f"Logical Date:  {logical}")
    print(f"Run After:     {run_after}")

print_actual_interval()

print_execution_dates_v2()
`

Operating System

I am using mac but Airflow is running inside docker container

Versions of Apache Airflow Providers

No response

Deployment

Docker-Compose

Deployment details

`# 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:3.1.3

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.

Use this option ONLY for quick checks. Installing requirements at container

startup is done EVERY TIME the service is started.

A better way is to build a custom image or extend the official image

as described in https://airflow.apache.org/docs/docker-stack/build.html.

Default: ''

Feel free to modify this file to suit your needs.

---

x-airflow-common:
&airflow-common

In order to add custom dependencies or upgrade provider distributions 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:3.1.3}

build: .

env_file:
- ${ENV_FILE_PATH:-.env}
environment:
&airflow-common-env
AIRFLOW__CORE__EXECUTOR: CeleryExecutor
AIRFLOW__CORE__AUTH_MANAGER: airflow.providers.fab.auth_manager.fab_auth_manager.FabAuthManager
AIRFLOW__DATABASE__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__EXECUTION_API_SERVER_URL: 'http://airflow-apiserver:8080/execution/'
# yamllint disable rule:line-length
# Use simple http server on scheduler for health checks
# See https://airflow.apache.org/docs/apache-airflow/stable/administration-and-deployment/logging-monitoring/check-health.html#scheduler-health-check-server
# yamllint enable rule:line-length
AIRFLOW__SCHEDULER__ENABLE_HEALTH_CHECK: 'true'
# WARNING: Use _PIP_ADDITIONAL_REQUIREMENTS option ONLY for a quick checks
# for other purpose (development, test and especially production usage) build/extend Airflow image.
_PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:-}
# The following line can be used to set a custom config file, stored in the local config folder
AIRFLOW_CONFIG: '/opt/airflow/config/airflow.cfg'
volumes:
- ${AIRFLOW_PROJ_DIR:-.}/dags:/opt/airflow/dags
- ${AIRFLOW_PROJ_DIR:-.}/logs:/opt/airflow/logs
- ${AIRFLOW_PROJ_DIR:-.}/config:/opt/airflow/config
- ${AIRFLOW_PROJ_DIR:-.}/plugins:/opt/airflow/plugins
- ${AIRFLOW_PROJ_DIR:-.}/output_files:/opt/airflow/output_files
user: "${AIRFLOW_UID:-50000}:0"
depends_on:
&airflow-common-depends-on
redis:
condition: service_healthy
postgres:
condition: service_healthy

services:
postgres:
image: postgres:16
ports:
- "5432:5432"
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: 10s
retries: 5
start_period: 5s
restart: always

redis:
# Redis is limited to 7.2-bookworm due to licencing change
# https://redis.io/blog/redis-adopts-dual-source-available-licensing/
image: redis:7.2-bookworm
expose:
- 6379
healthcheck:
test: ["CMD", "redis-cli", "ping"]
interval: 10s
timeout: 30s
retries: 50
start_period: 30s
restart: always

airflow-apiserver:
<<: *airflow-common
command: api-server
ports:
- "8080:8080"
healthcheck:
test: ["CMD", "curl", "--fail", "http://localhost:8080/api/v2/version"]
interval: 30s
timeout: 10s
retries: 5
start_period: 30s
restart: always
depends_on:
<<: *airflow-common-depends-on
airflow-init:
condition: service_completed_successfully

airflow-scheduler:
<<: *airflow-common
command: scheduler
healthcheck:
test: ["CMD", "curl", "--fail", "http://localhost:8974/health"]
interval: 30s
timeout: 10s
retries: 5
start_period: 30s
restart: always
depends_on:
<<: *airflow-common-depends-on
airflow-init:
condition: service_completed_successfully

airflow-dag-processor:
<<: *airflow-common
command: dag-processor
healthcheck:
test: ["CMD-SHELL", 'airflow jobs check --job-type DagProcessorJob --hostname "$${HOSTNAME}"']
interval: 30s
timeout: 10s
retries: 5
start_period: 30s
restart: always
depends_on:
<<: *airflow-common-depends-on
airflow-init:
condition: service_completed_successfully

airflow-worker:
<<: *airflow-common
command: celery worker
healthcheck:
# yamllint disable rule:line-length
test:
- "CMD-SHELL"
- 'celery --app airflow.providers.celery.executors.celery_executor.app inspect ping -d "celery@$${HOSTNAME}" || celery --app airflow.executors.celery_executor.app inspect ping -d "celery@$${HOSTNAME}"'
interval: 30s
timeout: 10s
retries: 5
start_period: 30s
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-apiserver:
condition: service_healthy
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: 30s
timeout: 10s
retries: 5
start_period: 30s
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
- |
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
export AIRFLOW_UID=$$(id -u)
fi
one_meg=1048576
mem_available=$$(($$(getconf _PHYS_PAGES) * $$(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
echo
echo "Creating missing opt dirs if missing:"
echo
mkdir -v -p /opt/airflow/{logs,dags,plugins,config}
echo
echo "Airflow version:"
/entrypoint airflow version
echo
echo "Files in shared volumes:"
echo
ls -la /opt/airflow/{logs,dags,plugins,config}
echo
echo "Running airflow config list to create default config file if missing."
echo
/entrypoint airflow config list >/dev/null
echo
echo "Files in shared volumes:"
echo
ls -la /opt/airflow/{logs,dags,plugins,config}
echo
echo "Change ownership of files in /opt/airflow to ${AIRFLOW_UID}:0"
echo
chown -R "${AIRFLOW_UID}:0" /opt/airflow/
echo
echo "Change ownership of files in shared volumes to ${AIRFLOW_UID}:0"
echo
chown -v -R "${AIRFLOW_UID}:0" /opt/airflow/{logs,dags,plugins,config}
echo
echo "Files in shared volumes:"
echo
ls -la /opt/airflow/{logs,dags,plugins,config}

# yamllint enable rule:line-length
environment:
  <<: *airflow-common-env
  _AIRFLOW_DB_MIGRATE: '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"

airflow-cli:
<<: *airflow-common
profiles:
- debug
environment:
<<: *airflow-common-env
CONNECTION_CHECK_MAX_COUNT: "0"
# Workaround for entrypoint issue. See: #16252
command:
- bash
- -c
- airflow
depends_on:
<<: *airflow-common-depends-on

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: 30s
timeout: 10s
retries: 5
start_period: 30s
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

[boring-cyborg]

Thanks for opening your first issue here! Be sure to follow the issue template! If you are willing to raise PR to address this issue please do so, no need to wait for approval.

[tirkarthi]

Can you please share the value set for create_cron_data_intervals ? This looks like a duplicate of below

#51371

https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html#create-cron-data-intervals
https://airflow.apache.org/docs/apache-airflow/stable/authoring-and-scheduling/timetable.html#differences-between-trigger-and-data-interval-timetables

[abhijeets25012-tech]

i would like to work on this

[manisnitt]

Thanks @tirkarthi ,
It is working after changing the schedule type. In case anyone wants to see how I did this then you can follow below DAG.

`from datetime import datetime
from airflow.sdk import dag, task
from airflow.sdk import get_current_context
from airflow.timetables.interval import CronDataIntervalTimetable
import pendulum

@dag(
start_date=datetime(2025, 12, 31),
schedule=CronDataIntervalTimetable(cron="@daily",
timezone=pendulum.timezone("Asia/Kolkata")),
# schedule="@daily",
catchup=True,
is_paused_upon_creation=False,
)
def print_execution_dates_v2():

@task
def print_actual_interval():
    context = get_current_context()

    start = context["data_interval_start"]
    end = context["data_interval_end"]

    dag_run = context["dag_run"]
    logical = dag_run.logical_date
    run_after = dag_run.run_after

    print(f"Period Start:  {start}")
    print(f"Period End:    {end}")
    print(f"Logical Date:  {logical}")
    print(f"Run After:     {run_after}")

@task
def debug_timetable():
    context = get_current_context()
    dag = context["dag"]
    print(type(dag.timetable))


print_actual_interval() >> debug_timetable()

print_execution_dates_v2()
`