Airflow docker-compose 리뷰 및 운영 고민 + .airflowignore
Airflow docker-compose 리뷰 및 운영 고민 + .airflowignore
Apr 08, 2024
Airflow Docker docker-compose.yml 리뷰Docker 기반 Airflow 실행x-airflow-commonairflow-scheduler 서비스 보기Dag를 구현하며 특정 파이썬 모듈을 설치해야한다면?Docker compose yml에서 수정된 내용 (환경 변수, 라이브러리 설치)수정 후 docker-compose.yml (토글 형식)Airflow Docker 운영 방안에 있어 고민사항고민 포인트: Airflow 실행환경 관리방안고민 포인트: Airflow DAG 관리방안👍🏻팁 : .airflowignore
Airflow Docker docker-compose.yml 리뷰
Airflow를 Docker Compose로 실행해보자
Docker 기반 Airflow 실행
- Docker 기반 Airflow 설치 문서 참조
- 먼저 터미널 프로그램을 실행하고 적당한 폴더로 이동
- airflow-setup Github repo를 클론
- airflow-setup 폴더로 이동하고 2.5.1 이미지 관련 yml 파일 다운로드
- cd airflow-setup
- curl -LfO ‘https://airflow.apache.org/docs/apache-airflow/2.5.1/docker-compose.yaml’
- 다음 2개의 명령을 수행 (이미지 다운로드와 컨테이너 실행)
- docker-compose -f docker-compose.yaml pull
- docker-compose -f docker-compose.yaml up
- http://localhost:8080으로 웹 UI 로그인
- airflow:airflow 사용
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: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__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
- ${AIRFLOW_PROJ_DIR:-.}/plugins:/opt/airflow/plugins
user: "${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
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:
- version
- x-airflow-common
- airflow-common이라는 별칭 정의. 여러 서비스에서 공유하는 공통 구성을 정의
- 이를 보통 anchor라고 부르며 YML 파일 블록을 나중에 계승이란 형태로 재사용 가능하게 해줌
- version, services, volumes, networks를 제외한 최상위 레벨 키워드는 모두 anchor
- 아래 서비스들은 디폴트 네트워크에 포함됨
- services
- postgres
- redis
- airflow-webserver
- airflow-scheduler
- airflow-worker
- airflow-triggerer
- airflow-init
- volumes
- postgres-db-volume
x-airflow-common
- 내용
x-airflow-common:
&airflow-common
image: ${AIRFLOW_IMAGE_NAME:-apache/airflow:2.5.1} # 1번
environment:
&airflow-common-env
AIRFLOW__CORE__EXECUTOR: CeleryExecutor
_PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:-} # 4번
volumes:
- ${AIRFLOW_PROJ_DIR:-.}/dags:/opt/airflow/dags # 2번
- ${AIRFLOW_PROJ_DIR:-.}/logs:/opt/airflow/logs # 2번
- ${AIRFLOW_PROJ_DIR:-.}/plugins:/opt/airflow/plugins # 2번
user: "${AIRFLOW_UID:-50000}:0"
depends_on:
&airflow-common-depends-on
redis:
condition: service_healthy # 3번
postgres:
condition: service_healthy # 3번
- 즉 같은 airflow 이미지가 모든 서비스 기본 이미지로 사용됨
- 모두 세 개의 volume을 공유함 (host volumes)
- 모두 redis와 postgres가 정상동작할 때 대기
- 파이썬 추가 설치 모듈과 관련된 것들은 4번 항목에 작성
&airflow-common 과 같이 &을 붙여주면 해당 indentation에 해당하는 항목들은 위 네이밍을 통해 접근 및 상속 가능
<<: *airflow-common
위와 같이 접근 및 상속 가능 (아래 airflow-scheduler 서비스에서 확인 가능)
airflow-scheduler 서비스 보기
- 내용
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-init : docker container들이 돌 때, 사용하는 리소스들이 충분히 존재하는지 확인하는 컨테이너
Dag를 구현하며 특정 파이썬 모듈을 설치해야한다면?
import yfinance as yf
@task
def get_historical_prices(symbol):
ticket = yf.Ticker(symbol)
data = ticket.history()
DAG를 구현하며 새로 생긴 모듈을 어떻게 설치해주어야할까?
일일히 docker container에 들어가서 설치해주는 것은 유지보수도 안되고 불가능!
docker-compose.yaml에 답이 있음
Docker compose yml에서 수정된 내용 (환경 변수, 라이브러리 설치)
수정 후 docker-compose.yml (토글 형식)
# 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_VAR_DATA_DIR: /opt/airflow/data
AIRFLOW__CORE__EXECUTOR: CeleryExecutor
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:- yfinance pandas numpy oauth2client gspread}
volumes:
- ${AIRFLOW_PROJ_DIR:-.}/dags:/opt/airflow/dags
- ${AIRFLOW_PROJ_DIR:-.}/logs:/opt/airflow/logs
- ${AIRFLOW_PROJ_DIR:-.}/plugins:/opt/airflow/plugins
- ${AIRFLOW_PROJ_DIR:-.}/data:/opt/airflow/data
user: "${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
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 /sources/data
chown -R "${AIRFLOW_UID}:0" /sources/{logs,dags,plugins,data}
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:
- x-airflow-common에서 _PIP_ADDITIONAL_REQUIREMENTS의 값을 변경
- Before:
_PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:-}
- After:
_PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:- yfinance pandas numpy}
_PIP_ADDITIONAL_REQUIREMENTS
은 Python 패키지 관리자인 pip를 통해 설치할 추가적인 패키지의 요구 사항을 정의하는 환경 변수입니다. If 문 같이 사용이 가능합니다이 변수는 기본적으로 값이 설정되지 않았을 경우
yfinance pandas numpy
로 설정됩니다.-
data 폴더
를 호스트 폴더에서 만들고 볼륨으로 공유: 임시 데이터를 저장할 폴더 - 이를 docker volume으로 지정해서 나중에 디버깅에 사용
- 임시 파일이 생기면 호스트 환경에서 볼 수 있게 세팅
- 임시 파일이 저장될 데이터 주소를 환경 변수 AIRFLOW_VAR_DATA_DIR로 만들어서 컨테이너에서 활용 → 웹 UI에서는 보이지 않지만 컨테이너 간 통신으로 DAG 코드 내에서 Airflow Variable로 활용이 가능해진다
environment:
AIRFLOW_VAR_DATA_DIR: /opt/airflow/data
_PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:- yfinance pandas numpy
oauth2client gspread}
volumes:
…
- ${AIRFLOW_PROJ_DIR:-.}/data:/opt/airflow/data
airflow-init:
…
mkdir -p /sources/logs /sources/dags /sources/plugins /sources/data
chown -R "${AIRFLOW_UID}:0" /sources/{logs,dags,plugins,data}
- airflow-init 섹션에서 컨테이너 내부의 sources 폴더 밑에 logs, dags, plugin, data 폴더를 만든다
- 4개의 서브 폴더 주인으로 airflow 유저로 세팅
- Detached 모드로 실행하려면 -d 옵션 지정 (-f 옵션도 존재)
- docker compose up -d
- http://localhost:8080으로 웹 UI 로그인
- airflow:airflow 사용
- ⭐️ 앞서 설정한 DATA_DIR이란 변수는 Admin ⇒ Variables에 안 보임.
- DAG과 Airflow 환경 정보들은 Postgres의 Named Volume으로 유지되고 있음
- 환경변수로 설정한 것들은 Web UI에서는 안 보이지만 프로그램에서는 사용가능
$ docker exec -it learn-airflow-airflow-scheduler-1 airflow variables get DATA_DIR
/opt/airflow/data

- Variables/Connections 설정을 어떻게 관리하는 것이 좋을까?
- 이를 docker-compose.yaml에서 환경변수로 설정.
Airflow Docker 운영 방안에 있어 고민사항
고민 포인트: Airflow 실행환경 관리방안
기타 환경설정값들 (Variables, Connections 등등)을 어떻게 관리/배포할까?
- 보통 docker-compose.yml 파일에서 x-airflow-common → environment 아래 에 정의
- x-airflow-common 부분을 대부분 컨테이너 서비스가 상속받음
x-airflow-common:
&airflow-common
…
environment:
&airflow-common-env
AIRFLOW_VAR_DATA_DIR: /opt/airflow/data
AIRFLOW_CONN_TEST_ID: test_connection
환경변수가 아니라 별도 credentials 전용
Secrets 백엔드라는 것을 사용하기도 함 - 아래 링크
- ⭐️ 앞서 설정한 DATA_DIR이란 변수는 Admin ⇒ Variables에 안 보임.
- DAG과 Airflow 환경 정보들은 Postgres의 Named Volume으로 유지되고 있음
- 환경변수로 설정한 것들은 Web UI에서는 안 보이지만 프로그램에서는 사용가능
$ docker exec -it learn-airflow-airflow-scheduler-1 airflow variables get DATA_DIR
/opt/airflow/data

- 이를 docker-compose.yaml에서 환경변수로 설정.
- 어디까지 Airflow 이미지로 관리하고 무엇을 docker-compose.yml에서 관리할지 생각
- 이는 회사마다 조금씩 다름
- Airflow 자체 이미지를 만들고 거기에 넣을지? 이 경우 환경변수를 자체 이미지에 넣고 이를 docker-compose.yaml 파일에서 사용
x-airflow-common:
&airflow-common
image: ${AIRFLOW_IMAGE_NAME:-apache/airflow:2.5.1}
고민 포인트: Airflow DAG 관리방안
- DAG 코드도 마찬가지
- Docker Airflow image로 DAG 코드를 복사하여 만드는 것이 좀더 깔끔
- Docker airlfow 이미지를 사용할 것인가
- Apache에 있는 이미지를 사용할 것인가
- 아니면 docker-compose에서 host volume 형태로 설정
- 이는 개발/테스트용으로 좀더 적합
👍🏻팁 : .airflowignore
- Airflow의 DAG 스캔 패턴은?
- airflow는 기본적으로 5분에 한번씩 dags 폴더를 스캔하게끔 세팅이 되어있음
- ⭐️ dags_folder가 가리키는 폴더를 서브폴더들까지 다 스캔해서 DAG 모듈이 포함된 모든 파이썬 스크립트를 실행해서 새로운 DAG를 찾게 되며 이는 가끔 사고로 이어짐
- 개발하다만 script를 실행하게끔 하면 어마어마한 부하가 발생할 것
- Airflow가 의도적으로 무시해야 하는 DAG_FOLDER의 디렉터리 또는 파일을 지정
- dags 폴더 밑에
.airflowignore
라는 파일을 생성해서 배치하면 됨
.airflowignore
의 각 줄은 정규식 패턴으로 지정하며 매칭되는 파일들은 무시됨- project_a
- tenant_[\d]
- 위의 경우 아래 파일들이 무시됨
- project_a_dag_1.py, TESTING_project_a.py, tenant_1.py, project_a/dag_1.py
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