本地想玩耍spark，Hadoop其他环境都使用的是CDH 5.7.0，本想一路下来继续使用cloudera提供的版本，但是官方编译的版本运行起来各种错误，所以先码一片文章，发扬奉献精神，走你>>>

开始入坑：（直接上编译步骤）

1.环境介绍：

CentOS6.8-64位
JDK7
Spark1.6.0
Scala2.10.5
Hadoop2.6.0-CDH5.7.0
maven3.3.9（只需要3.3.3+就可以）

2.编译环境部署

(1)jdk的安装，发扬工匠精神，直接都码上吧

• tar -zxvf jdk1.7.0_65.tar.gz -C /opt/cluster/
• vim /etc/profile(添加环境变量)

# Java Home
export JAVA_HOME=/opt/jdk1.7.0_65
export CLASSPATH=.:$JAVA_HOME/jre/lib/rt.jar:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar
bin:$JAVA_HOME/bin

• source /etc/profile
• 验证安装：java -version 或者 echo $JAVA_HOME

(2)Maven的安装

• tar -zxvf apache-maven-3.3.9.tar.gz -C /opt/cluster/
• vim /etc/profile(添加环境变量)

# Maven Home
export MAVEN_HOME=/opt/cluster/apache-maven-3.3.9
bin:$MAVEN_HOME/bin

• source /etc/profile
• 验证安装：mvn -version 或者 echo $MAVEN_HOME

(3)Scala的安装

• tar -zxvf scala-2.10.5.tgz -C /opt/cluster/
• vim /etc/profile(添加环境变量)

# Scala Home
export SCALA_HOME=/opt/cluster/scala-2.10.5
bin:$SCALA_HOME/bin

• source /etc/profile
• 验证安装：scala

(4)spark源码安装与配置

• tar -zxvf spark-1.6.0.tgz -C /opt/cluster/
• 为了编译比较快，要更改make-distribution.sh文件

添加版本（不用spark自己去解析生成）
VERSION=1.6.0
SCALA_VERSION=2.10
SPARK_HADOOP_VERSION=2.6.0-cdh5.7.0
SPARK_HIVE=1

将130多行的解析版本代码注释掉
#VERSION=$("$MVN" help:evaluate -Dexpression=project.version $@ 2>/dev/null | grep -v "INFO" | tail -n 1)
#SCALA_VERSION=$("$MVN" help:evaluate -Dexpression=scala.binary.version $@ 2>/dev/null\
#    | grep -v "INFO"\
#    | tail -n 1)
#SPARK_HADOOP_VERSION=$("$MVN" help:evaluate -Dexpression=hadoop.version $@ 2>/dev/null\
#    | grep -v "INFO"\
#    | tail -n 1)
#SPARK_HIVE=$("$MVN" help:evaluate -Dexpression=project.activeProfiles -pl sql/hive $@ 2>/dev/null\
#    | grep -v "INFO"\
#    | fgrep --count "<id>hive</id>";\
#    # Reset exit status to 0, otherwise the script stops here if the last grep finds nothing\
#   # because we use "set -o pipefail"
#    echo -n)

3.开始编译

• 我建议使用jdk7去编译，jdk8的不去特此说明了

step1：进入spark源码目录
cd /opt/cluster/spark-1.6.0
step2：配置一下MAVEN_OPTS
export MAVEN_OPTS="-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m"
step3：开始编译
./make-distribution.sh --tgz -Phadoop-2.6 -Dhadoop.version=2.6.0-cdh5.7.0 -Pyarn -Phive  -Phive-thriftserver

4.编译过程

• 编译是按照下面的模块执行的，可以去观察日志打印更好的了解执行过程

[INFO] ------------------------------------------------------------------------
[INFO] Reactor Summary:
[INFO] 
[INFO] Spark Project Parent POM ........................... SUCCESS [  5.850 s]
[INFO] Spark Project Test Tags ............................ SUCCESS [  4.403 s]
[INFO] Spark Project Launcher ............................. SUCCESS [ 15.255 s]
[INFO] Spark Project Networking ........................... SUCCESS [ 11.419 s]
[INFO] Spark Project Shuffle Streaming Service ............ SUCCESS [  7.578 s]
[INFO] Spark Project Unsafe ............................... SUCCESS [ 20.146 s]
[INFO] Spark Project Core ................................. SUCCESS [05:10 min]
[INFO] Spark Project Bagel ................................ SUCCESS [ 17.821 s]
[INFO] Spark Project GraphX ............................... SUCCESS [ 45.020 s]
[INFO] Spark Project Streaming ............................ SUCCESS [01:12 min]
[INFO] Spark Project Catalyst ............................. SUCCESS [01:39 min]
[INFO] Spark Project SQL .................................. SUCCESS [02:23 min]
[INFO] Spark Project ML Library ........................... SUCCESS [02:24 min]
[INFO] Spark Project Tools ................................ SUCCESS [ 19.271 s]
[INFO] Spark Project Hive ................................. SUCCESS [01:53 min]
[INFO] Spark Project Docker Integration Tests ............. SUCCESS [  8.271 s]
[INFO] Spark Project REPL ................................. SUCCESS [ 46.352 s]
[INFO] Spark Project YARN Shuffle Service ................. SUCCESS [ 18.256 s]
[INFO] Spark Project YARN ................................. SUCCESS [01:37 min]
[INFO] Spark Project Hive Thrift Server ................... SUCCESS [02:47 min]
[INFO] Spark Project Assembly ............................. SUCCESS [02:55 min]
[INFO] Spark Project External Twitter ..................... SUCCESS [ 56.260 s]
[INFO] Spark Project External Flume Sink .................. SUCCESS [02:39 min]
[INFO] Spark Project External Flume ....................... SUCCESS [ 27.604 s]
[INFO] Spark Project External Flume Assembly .............. SUCCESS [ 16.969 s]
[INFO] Spark Project External MQTT ........................ SUCCESS [03:33 min]
[INFO] Spark Project External MQTT Assembly ............... SUCCESS [ 20.258 s]
[INFO] Spark Project External ZeroMQ ...................... SUCCESS [01:17 min]
[INFO] Spark Project External Kafka ....................... SUCCESS [01:24 min]
[INFO] Spark Project Examples ............................. SUCCESS [07:13 min]
[INFO] Spark Project External Kafka Assembly .............. SUCCESS [ 11.575 s]
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 44:07 min
[INFO] Final Memory: 101M/1243M
[INFO] ------------------------------------------------------------------------

5.后续

• 编译成功后会在当前目录下生成一个包：spark-1.6.0-bin-2.6.0-cdh5.7.0.tgz

• 解压这个到你想解压的位置

 tar -zxvfspark-1.6.0-bin-2.6.0-cdh5.7.0.tgz -C /opt/cluster/

• 可以运行一个Wordcount体验一下

step1:进入解压目录执行
bin/spark-shell
step2:自己在HDFS上添加一个文件，随便写几个单词，空格隔开，执行scala
sc.textFile("hdfs://hadoop-master:8020/user/master/mapreduce/wordcount/input/hello.txt").flatMap(_.split(" ")).map((_,1)).reduceByKey(_ + _)
step3:查看结果
rse0.collect