centos7 hdfs yarn spark 搭建笔记

1.搭建3台虚拟机

2.建立账户及信任关系

3.安装java

wget jdk-xxx

rpm -i jdk-xxx

4.添加环境变量（全部）

export JAVA_HOME=/usr/java/jdk1.8.0_141
export JRE_HOME=$JAVA_HOME/jre
export PATH=$PATH:$JAVA_HOME/bin:$JAVA_HOME/jre/bin
export CLASSPATH=$CLASSPATH:.:$JAVA_HOME/lib:$JAVA_HOME/jre/lib
export HADOOP_HOME=/data/spark/bin/hadoop
export PATH=$PATH:$HADOOP_HOME/bin/:$HADOOP_HOME/sbin
export HADOOP_OPTS="-Djava.library.path=$HADOOP_HOME/lib/native"
export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop
export SPARK_HOME=/data/spark/bin/spark
export PATH=$PATH:$SPARK_HOME/bin

5.搭建hadoop

1>vi $HADOOP_HOME/etc/hadoop/hadoop-env.sh

export JAVA_HOME=/usr/java/jdk1.8.0_141

2>vi $HADOOP_HOME/etc/hadoop/core-site.xml

<configuration>
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://10.0.0.5:9000</value>
    </property>
    <property>
        <name>hadoop.tmp.dir</name>
        <value>/data/spark/bin/hadoop/tmp</value>
    </property>
</configuration>

3>vi $HADOOP_HOME/etc/hadoop/hdfs-site.xml

<configuration>
    <property>
        <name>dfs.namenode.name.dir</name>
        <value>file:///data/spark/hdfs/name</value>
    </property>
    <property>
        <name>dfs.datanode.data.dir</name>
        <value>file:///data1/hdfs-ext,file:///data2/hdfs-ext,file:///data3/hdfs-ext</value>
    </property>
    <property>
        <name>dfs.namenode.checkpoint.dir</name>
        <value>/data/spark/hdfs/namesecondary</value>
    </property>
    <property>
        <name>dfs.namenode.http-address</name>
        <value>0.0.0.0:50070</value>
    </property>
    <property>
        <name>dfs.namenode.secondary.http-address</name>
        <value>0.0.0.0:50090</value>
    </property>
    <property>
        <name>dfs.datanode.http.address</name>
        <value>0.0.0.0:50075</value>
    </property>
    <property>
      <name>dfs.namenode.datanode.registration.ip-hostname-check</name>
      <value>false</value>
    </property>
</configuration>

4>vi $HADOOP_HOME/etc/hadoop/yarn-site.xml

<configuration>
    <property>
        <name>yarn.resourcemanager.hostname</name>
        <value>10.0.0.5</value>
    </property>
    <property>
        <name>yarn.nodemanager.local-dirs</name>
        <value>/data/spark/hdfs/nm-local-dir</value>
    </property>
    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
    <property>
        <name>yarn.nodemanager.resource.memory-mb</name>
        <value>8192</value>
    </property>
    <property>
        <name>yarn.nodemanager.resource.cpu-vcores</name>
        <value>4</value>
    </property>
    <property>
        <name>yarn.resourcemanager.webapp.address</name>
        <value>0.0.0.0:8088</value>
    </property>
    <property>
        <name>yarn.nodemanager.webapp.address</name>
        <value>0.0.0.0:8042</value>
    </property>
    <property>
        <name>yarn.nodemanager.pmem-check-enabled</name>
        <value>false</value>
    </property>
    <property>
        <name>yarn.nodemanager.vmem-check-enabled</name>
        <value>false</value>
    </property>
    <property>
        <name>yarn.nodemanager.vmem-pmem-ratio</name>
        <value>5</value>
    </property>
</configuration>

 5>vi $HADOOP_HOME/etc/hadoop/slaves

10.0.0.5
10.0.0.6
10.0.0.7

6>拷贝hadoop文件到各个从机,并设置PATH

7>hdfs namenode格式化

hdfs namenode -format

8>启动hdfs并查看日志

start-dfs.sh

9>启动yarn并查看日志

start-yarn.sh

10>查看各节点进程情况,一定要看日志

jps

一定要看日志

11>测试并查看日志

cd /xxx
echo "this is a test for hdfs" > 1.txt
hadoop fs -mkdir /spark
hadoop fs -mkdir /spark/test
hadoop fs -appendToFile 1.txt hdfs://10.0.0.5:9000/spark/test/1.txt
hadoop fs -cat hdfs://10.0.0.5:9000/spark/test/1.txt

6.搭建spark

1>修改spark-env.sh

mv $SPARK_HOME/conf/spark-env.sh.template $SPARK_HOME/conf/spark-env.sh
vi $SPARK_HOME/conf/spark-env.sh

export SPARK_HOME=/data/spark/bin/spark
export JAVA_HOME=/usr/java/jdk1.8.0_141
export HADOOP_HOME=/data/spark/bin/hadoop
export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop
export YARN_CONF_DIR=$HADOOP_HOME/etc/hadoop
export SPARK_MASTER_IP=10.0.0.5
export SPARK_LOCAL_DIRS=/data/spark/bin/spark
export SPARK_LIBARY_PATH=.:$JAVA_HOME/lib:$JAVA_HOME/jre/lib:$HADOOP_HOME/lib/native
export SPARK_LOG_DIR=/data/spark/bin/spark/logs

2>修改spark-defaults.conf

mv $SPARK_HOME/conf/spark-defaults.conf.template $SPARK_HOME/conf/spark-defaults.conf
vi $SPARK_HOME/conf/spark-defaults.conf

spark.yarn.jars  hdfs://10.0.0.5:9000/spark/jars/*

 3>上传jars

cd $SPARK_HOME/jars
hadoop fs -mkdir /spark/jars
hadoop fs -put * hdfs://10.0.0.5:9000/spark/jars/

4>修改slave（没什么用）

mv $SPARK_HOME/conf/slaves.template $SPARK_HOME/conf/slaves
vi $SPARK_HOME/conf/slaves

10.0.0.5
10.0.0.6
10.0.0.7

 5>单点交互测试

pyspark --master local[4]

6>集群交互测试

pyspark --master yarn --deploy-mode client

7>建立测试脚本 vi test.py

from __future__ import print_function

import sys
from random import random
from operator import add

from pyspark.sql import SparkSession

if __name__ == "__main__":
"""
Usage: pi [partitions]
"""
    spark = SparkSession
.builder
.appName("PythonPi")
.getOrCreate()

lines = spark.sparkContext.textFile("hdfs://10.0.0.5:9000/spark/test/1.txt")
num = lines.count()
p_str = lines.first()
print("--------------------"+str(num)+"---------------------")
print("--------------------"+p_str+"---------------------")

spark.stop()

8>单点任务测试

spark-submit --master local[4] test.py

9>集群任务测试

spark-submit --master yarn --deploy-mode cluster test.py