通过共享网络连接docker撰写文件

还是可以?

  version: '3.3'
    services:
      kafka:
        image: spotify/kafka
        ports:
         - "9092:9092"
        networks:
          - docker-elk
        environment:
        - ADVERTISED_HOST=localhost
      neo4jdb:
        image: neo4j:latest
        container_name: neo4jdb
        ports:
          - "7474:7474"
          - "7473:7473"
          - "7687:7687"
        networks:
          - docker-elk
        volumes:
          - /var/lib/neo4j/import:/var/lib/neo4j/import
          - /var/lib/neo4j/data:/datax
          - /var/lib/neo4j/conf:/conf
        environment:
          - NEO4J_dbms_active__database=graphImport.db
      elasticsearch:
        image: elasticsearch:latest
        ports:
          - "9200:9200"
          - "9300:9300"
        networks:
          - docker-elk
        volumes:
            - esdata1:/usr/share/elasticsearch/data
      kibana:
        image: kibana:latest
        ports:
          - "5601:5601"
        networks:
          - docker-elk
    volumes:
      esdata1:
        driver: local

networks:
  docker-elk:
    driver: bridge

我的docker文件:

    version: '3.3'
    services:
      producer-demo:
        build:
          context: .
          dockerfile: Dockerfile
          args:
            - ARG_CLASS=producer
            - HOST=neo4jdb
        volumes:
          - ./:/workdir
        working_dir: /workdir
        networks:
      - common_docker-elk

networks:
  common_docker-elk:
    external: true

Dockerfile文件:

FROM java:8
ARG ARG_CLASS
ARG HOST
ARG SPARK_CONFIG
ARG NEO4J_CONFIG
ENV MAIN_CLASS $ARG_CLASS
ENV SCALA_VERSION 2.11.8
ENV SBT_VERSION 1.1.1
ENV SPARK_VERSION 2.2.0
ENV SPARK_DIST spark-$SPARK_VERSION-bin-hadoop2.6
ENV SPARK_ARCH $SPARK_DIST.tgz
ENV SPARK_MASTER $SPARK_CONFIG
ENV DB_CONFIG neo4j_local
ENV KAFKA_STREAMS_NUMBER 5
ENV KAFKA_EVENTS_NUMBER 10
ENV MESSAGES_BATCH_SIZE 16777216
ENV LINGER_MESSAGES_TIME 5
ENV HOSTNAME bolt://$HOST:7687

VOLUME /workdir

WORKDIR /opt

# Install Scala
RUN \
  cd /root && \
  curl -o scala-$SCALA_VERSION.tgz http://downloads.typesafe.com/scala/$SCALA_VERSION/scala-$SCALA_VERSION.tgz && \
  tar -xf scala-$SCALA_VERSION.tgz && \
  rm scala-$SCALA_VERSION.tgz && \
  echo >> /root/.bashrc && \
  echo 'export PATH=~/scala-$SCALA_VERSION/bin:$PATH' >> /root/.bashrc

# Install SBT
RUN \
  curl -L -o sbt-$SBT_VERSION.deb https://dl.bintray.com/sbt/debian/sbt-$SBT_VERSION.deb && \
  dpkg -i sbt-$SBT_VERSION.deb && \
  rm sbt-$SBT_VERSION.deb


# Install Spark
RUN \
    cd /opt && \
    curl -o $SPARK_ARCH http://d3kbcqa49mib13.cloudfront.net/$SPARK_ARCH && \
    tar xvfz $SPARK_ARCH && \
    rm $SPARK_ARCH && \
    echo 'export PATH=$SPARK_DIST/bin:$PATH' >> /root/.bashrc


EXPOSE 9851 9852 4040 9092 9200 9300 5601 7474 7687 7473

CMD /workdir/runDemo.sh "$MAIN_CLASS" "$SPARK_MASTER" "$DB_CONFIG" "$KAFKA_STREAMS_NUMBER" "$KAFKA_EVENTS_NUMBER" "$MESSAGES_BATCH_SIZE" "$LINGER_MESSAGES_TIME"

加载项目的Bash脚本:

#!/usr/bin/env bash
if [ "$1" = "consumer" ]
then
    java -cp "jars/spark_consumer.jar" consumer.SparkConsumer $2 $3 $4
elif [ "$1" = "producer" ]
then
    java -cp "jars/kafka_producer.jar" producer.KafkaCheckinsProducer $5 $3 $6 $7
else
    echo "Wrong parameter. It should be consumer or producer, but it is $1"
fi