vers
  
ion: '3'

services:
  spark:
    image: s1mplecc/spark-hadoop:3
    hostname: master
    environment:
      - SPARK_MODE=master
      - SPARK_RPC_AUTHENTICATION_ENABLED=no
      - SPARK_RPC_ENCRYPTION_ENABLED=no
      - SPARK_LOCAL_STORAGE_ENCRYPTION_ENABLED=no
      - SPARK_SSL_ENABLED=no
    volumes:
      - /data/docker-data/hadoop-spark/spark/share:/opt/share
    ports:
      - '8880:8080'
      - '4440:4040'
      - '8888:8088'
      - '8042:8042'
      - '9870:9870'
      - '19888:19888'
      - '7077:7077'
    extra_hosts:
      - "worker1:127.0.0.1"
      - "worker2:127.0.0.1"
      - "master:127.0.0.1"
  spark-worker-1:
    image: s1mplecc/spark-hadoop:3
    hostname: worker1
    environment:
      - SPARK_MODE=worker
      - SPARK_MASTER_URL=spark://master:7077
      - SPARK_WORKER_MEMORY=1G
      - SPARK_WORKER_CORES=1
      - SPARK_RPC_AUTHENTICATION_ENABLED=no
      - SPARK_RPC_ENCRYPTION_ENABLED=no
      - SPARK_LOCAL_STORAGE_ENCRYPTION_ENABLED=no
      - SPARK_SSL_ENABLED=no
    volumes:
      - /data/docker-data/hadoop-spark/spark/share:/opt/share
    ports:
      - '8881:8081'
    extra_hosts:
      - "worker1:127.0.0.1"
      - "worker2:127.0.0.1"
      - "master:127.0.0.1"
  spark-worker-2:
    image: s1mplecc/spark-hadoop:3
    hostname: worker2
    environment:
      - SPARK_MODE=worker
      - SPARK_MASTER_URL=spark://master:7077
      - SPARK_WORKER_MEMORY=1G
      - SPARK_WORKER_CORES=1
      - SPARK_RPC_AUTHENTICATION_ENABLED=no
      - SPARK_RPC_ENCRYPTION_ENABLED=no
      - SPARK_LOCAL_STORAGE_ENCRYPTION_ENABLED=no
      - SPARK_SSL_ENABLED=no
    volumes:
      - /data/docker-data/hadoop-spark/spark/share:/opt/share
    ports:
      - '8882:8081'
    extra_hosts:
      - "worker1:127.0.0.1"
      - "worker2:127.0.0.1"
      - "master:127.0.0.1"



# docker-compose -f docker_compose_spark_hadoop.yml up -d
# docker-compose -f docker_compose_spark_hadoop.yml down

#Subscribe to project updates by watching https://github.com/bitnami/bitnami-docker-spark
#Submit issues and feature requests at https://github.com/bitnami/bitnami-docker-spark/issues

# work Spark Command: /opt/bitnami/java/bin/java -cp /opt/bitnami/spark/conf/:/opt/bitnami/spark/jars/*:/opt/hadoop/etc/hadoop/ -Xmx1g org.apache.spark.deploy.worker.Worker --webui-port 8081 spark://master:7077
# master Spark Command: /opt/bitnami/java/bin/java -cp /opt/bitnami/spark/conf/:/opt/bitnami/spark/jars/*:/opt/hadoop/etc/hadoop/ -Xmx1g org.apache.spark.deploy.master.Master --host master --port 7077 --webui-port 8080
# https://zhuanlan.zhihu.com/p/421375012?utm_medium=social&utm_oi=840347507367215104
# https://github.com/s1mplecc/spark-hadoop-docker


#由于 Spark 使用了 Hadoop 的客户端依赖库，所以 Spark 安装包会指定依赖的 Hadoop 特定版本，如 spark-3.1.2-bin-hadoop3.2.tgz。而 bitnami/spark 镜像中只包含 Hadoop 客户端，并不包含服务器端。因此，如果需要使用 HDFS 和 YARN 功能，还需要部署 Hadoop 集群。
#将 Hadoop 部署在 Spark 集群上，可以避免不必要的网络通信，并且面向磁盘的 HDFS 与面向内存的 Spark 天生互补。因此，考虑在 bitnami/spark 镜像基础上构建安装有 Hadoop 的新镜像。
# sc._gateway.jvm.org.apache.hadoop.util.VersionInfo.getVersion()   # 3.3.4


# 针对：ValueError: Cannot run multiple SparkContexts at once; existing SparkContext(app=PySparkShell, master=local[])
# 先执行
#  sc.stop()
# 然后再执行：
#  from pyspark import SparkConf
#  conf = SparkConf().setAppName('My App')
#  sc = SparkContext(conf=conf)
#
#  count = sc.range(1, 1000 * 1000 * 100).filter(lambda x: x > 100).count()
#  print('count: ', count)




# ./start-hadoop.sh
#Starting OpenBSD Secure Shell server: sshd.
#Starting namenodes on [master]
#Starting secondary namenodes [master]
#Starting resourcemanager
#Starting nodemanagers

#  https://zhuanlan.zhihu.com/p/401967378

# https://mirrors.tuna.tsinghua.edu.cn   这个网页下载速度快一点
# 这个下载速度也快一点:  http://archive.apache.org/dist/hadoop/core/