docker-compose.yaml

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# to you under the Apache License, Version 2.0 (the
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#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
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# "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:2.8.1
# AIRFLOW_UID                  - User ID in Airflow containers
#                                Default: 1000
# 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: airflow2
# _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 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:2.9.0}
  # image: apache/airflow:2.9.0
  build: .
  environment:
    &airflow-common-env
    AIRFLOW_UID: 1000
    AIRFLOW__CORE__EXECUTOR: CeleryExecutor
    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__API__AUTH_BACKENDS: 'airflow.api.auth.backend.basic_auth,airflow.api.auth.backend.session'
    SQLALCHEMY_DB: "postgresql"
    SQLALCHEMY_DB_JDBC: "psycopg2"
    SQLALCHEMY_USERNAME: "databricks_user"
    SQLALCHEMY_PASSWORD: "Databricks"
    SQLALCHEMY_HOST_ADDRESS: "viaduct.proxy.rlwy.net"
    SQLALCHEMY_HOST_PORT: "53745"
    SQLALCHEMY_HOST_DATABASE: "railway"
    DISCORD_WEBHOOK_URL: "https://discord.com/api/webhooks/1237883719105249293/k1-9FZ1gUUK0krFJj9Wow9X8ajV8dqKBeyx1QhxCHg98gL7IjPkJFTWSPYTNy9RPlu-o"
    # 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:- apache-airflow-providers-docker apache-airflow-providers-microsoft-mssql apache-airflow-providers-apache-spark apache-airflow-providers-postgres apache-airflow-providers-mysql apache-airflow-providers-apache-kafka apache-airflow-providers-google}
    AIRFLOW__CORE__TEST_CONNECTION: 'Enabled'
  volumes:
    - ${AIRFLOW_PROJ_DIR:-.}/dags:/opt/airflow/dags
    - ${AIRFLOW_PROJ_DIR:-.}/logs:/opt/airflow/logs
    - ${AIRFLOW_PROJ_DIR:-.}/config:/opt/airflow/config
    - ${AIRFLOW_PROJ_DIR:-.}/plugins:/opt/airflow/plugins
  user: "${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
    expose:
      - 5432
    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:
    image: redis:latest
    expose:
      - 6379
    healthcheck:
      test: ["CMD", "redis-cli", "ping"]
      interval: 10s
      timeout: 30s
      retries: 50
      start_period: 30s
    restart: always

  airflow-webserver:
    <<: *airflow-common
    command: webserver
    ports:
      - "8080:8080"
    healthcheck:
      test: ["CMD", "curl", "--fail", "http://localhost:8080/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-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-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-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
        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_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: ''
      _AIRFLOW_UID: 1000
      AIRFLOW_UID: 1000
    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: 30s
      timeout: 10s
      retries: 5
      start_period: 30s
    restart: always
    depends_on:
      <<: *airflow-common-depends-on
      airflow-init:
        condition: service_completed_successfully


  pg-admin-init:
    image: dpage/pgadmin4:latest
    environment:
      PGADMIN_DEFAULT_EMAIL: danserban20@gmail.com
      PGADMIN_DEFAULT_PASSWORD: dan
    ports:
      - "5430:80"
    restart: always
    volumes: 
    - pgadmin-data:/var/lib/pgadmin

  ### PHIND SPARK IMAGE SOLUTION
  # spark-master:
  #     image: spark-image
  #     command: /opt/spark/sbin/start-master.sh
  #     ports:
  #       - "7077:7077"
  #       - "8085:8080"
  #     environment:
  #       - SPARK_MASTER_HOST=spark-master

  # spark-worker:
  #     image: spark-image
  #     command: /opt/spark/sbin/start-worker.sh spark://spark-master:7077
  #     depends_on:
  #       - spark-master
  #     environment:
  #       - SPARK_WORKER_CORES=1
  #       - SPARK_WORKER_MEMORY=1g

  # spark-ui:
  #   image: apache/spark:3.5.1-scala2.12-java17-python3-ubuntu
  #   environment:
  #   # needs to be updated whenever I relocate the raspberry pi
  #     SPARK_DRIVER_HOST: "0.0.0.0"
  #     SPARK_DRIVER_BINDADDRESS: "0.0.0.0"
  #     SPARK_MASTER: spark://spark-master:7077
  #     MAIN_CLASS: Main
  #   ports:
  #     - "4039:4040"
  #     - "4038:8080"
  #   networks:
  #     - spark-network
  #   command: /opt/spark/bin/spark-class org.apache.spark.deploy.master.Master
      
  # spark-master:
  #     image: apache/spark:3.5.1-scala2.12-java17-python3-ubuntu
  #     environment:
  #       SPARK_MODE: master
  #       SPARK_MASTER_PORT_NUMBER: "7077"
  #       SPARK_MASTER_WEBUI_PORT_NUMBER: "8080"
  #       SPARK_MASTER_LOG_LEVEL: INFO
  #       SPARK_MASTER_HOST: "192.168.1.139"
  #     ports:
  #       - "7080:8080"
  #       - "7077:7077"
  #       - "4040:4040"
  #     networks:
  #       - spark-network
  #     volumes:
  #       - spark-master-volume:/opt/spark-apps
  #       - spark-master-volume:/opt/spark-data
  #     command: /opt/spark/bin/spark-class org.apache.spark.deploy.master.Master
  #     depends_on:
  #       - spark-ui

  # spark-worker-a:
  #     image: apache/spark:3.5.1-scala2.12-java17-python3-ubuntu
  #     depends_on:
  #       - spark-master
  #     environment:
  #       SPARK_MASTER: "spark://spark-master:7077"
  #       SPARK_WORKER_CORES: "1"
  #       SPARK_WORKER_MEMORY: "4G"
  #       SPARK_DRIVER_MEMORY: "1G"
  #       SPARK_EXECUTOR_MEMORY: "4G"
  #       SPARK_WORKLOAD: "worker"
  #       SPARK_LOCAL_IP: "192.168.1.139"
  #     ports:
  #       - "8081:8081"
  #       - "8082:8080"
  #       - "4042:4040"
  #       - "7000:7000"
  #       - "7078:7077"
  #     networks:
  #       - spark-network
  #     volumes:
  #       - spark-worker-volume:/opt/spark-apps
  #       - spark-worker-volume:/opt/spark-data
  #     command: /opt/spark/bin/spark-class org.apache.spark.deploy.worker.Worker spark://spark-master:7077

  # # spark-worker-2:
  # #     image: apache/spark-py:latest
  # #     depends_on:
  # #       - spark-master
  # #     environment:
  # #       SPARK_MODE: worker
  # #       SPARK_WORKER_CORES: 1
  # #       SPARK_WORKER_MEMORY: 4g
  # #       SPARK_MASTER_URL: spark://spark-master:7077
  # #     networks:
  # #       - spark-network
  # #     ports:
  # #       - "8082:8081"
  # #       - "4043:4040"

  # notebook:
  #   image: jupyter/pyspark-notebook
  #   depends_on:
  #     - pg-admin-init
  #   ports:
  #     - "8888:8888"
  #   command: start-notebook.sh --NotebookApp.token='abcd1234'
  #   volumes:
  #     - notebook-data:/home/jovyan/work
  #   networks:
  #     - spark-network

  pg-db-sandbox:
    image: postgres:latest
    environment:
      POSTGRES_USER: postgres
      POSTGRES_PASSWORD: danpass
      POSTGRES_DB: postgres
    ports:
    - "5432:5432"
    volumes:
      - dedicated-postgres-db-volume:/var/lib/postgresql/data
    healthcheck:
      test: ["CMD", "pg_isready", "-U", "postgres"]
      interval: 30s
      retries: 5
      start_period: 5s
    restart: always
    depends_on:
      - pg-admin-init


#   monitoring:
#     image: grafana/grafana:latest
#     ports:
#       - "3000:3000"
#     healthcheck:
#       test: ["CMD", "curl", "--fail", "http://localhost:3000/"]
#       interval: 30s
#       timeout: 10s
#       retries: 5
#       start_period: 30s
#     networks:
#       - spark-network
#       - default
#     environment:
#       - GF_SECURITY_ADMIN_PASSWORD=grafanadan
#       - GF_SECURITY_ADMIN_USER=dan
#     volumes:
#       - grafana-data:/var/lib/grafana
#     restart: always
#     depends_on:
#       - pg-admin-init
    
#   node-exporter:
#     image: quay.io/prometheus/node-exporter:latest
#     container_name: node-exporter
#     network_mode: host
#     pid: host
#     healthcheck:
#       test: ["CMD", "curl", "--fail", "http://localhost:9100/metrics"]
#       interval: 30s
#       timeout: 10s
#       retries: 5
#       start_period: 30s
#     volumes:
#       - /:/host:ro,rslave
#     restart: always
#     command: --path.rootfs=/host
#     depends_on:
#       - monitoring
    

volumes:
  grafana-data:
    name: grafana-data
  postgres-db-volume:
  dedicated-postgres-db-volume:
    name: dedicated-db-volume
  pgadmin-data:
      name: pgadmin-data
  spark-data:
      name: spark-data
  notebook-data:
      name: notebook-data
  spark-worker-volume:
      name: spark-worker-volume
  spark-master-volume:
      name: spark-master-volume

networks:
  spark-network:
      driver: ipvlan
      driver_opts:
        parent: wlan0
      ipam:
        driver: default
        config:
          - subnet: 10.0.0.0/24
            gateway: 10.0.0.1
            ip_range: 10.0.0.3/32  
      name: spark-network