spark2的编译

0、操作系统

centos:6.4
hadoop:2.5.0-cdh5.3.6

1、为什么要编译 spark 源码？

学习spark的第一步 就应该是编译源码，后期修改和调试，扩展集成的功能模块

2、Spark 源码编译的三种形式？

a.maven 编译
# export MAVEN_OPTS="-Xmx2g -XX:ReservedCodeCacheSize=512m"
# ${SPARK_HOME_SRC}/./build/mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -DskipTests clean package

b.SBT 编译
#${SPARK_HOME_SRC}/./build/sbt -Pyarn -Phadoop-2.3 package

c.打包编译
# ${SPARK_HOME_SRC}/./dev/make-distribution.sh --tgz -Psparkr -Dhadoop.version=2.5.0-cdh5.3.6 -Phadoop-2.4 -Phive -Phive-thriftserver -Pyarn

3、版本要求：

Maven 3.3.9

JDK 1.8+(1.8.0_12)
Scala 2.11.8
Note: Starting version 2.0, Spark is built with Scala 2.11 by default.
R(3.2.0)
wget http://mirrors.tuna.tsinghua.edu.cn/CRAN/src/base/R-3/R-3.2.0.tar.gz

4、编译步骤概览:

0. root 用户编译 + 网络通畅
1. jdk 环境搭建
2. maven 环境搭建
3. R(3.2.0)语言环境
4. 正式编译

5、jdk、maven 环境都是采用压缩包安装形式

操作形式:上传压缩包、解压、配置环境变量、更新source 资源文件
NOTE:
检查Maven 是否和现有Java 环境对应起来
给Maven 配置阿里云镜像:
修改 ${MAVEN_HOME}/conf/settings.xml
添加镜像:
<mirror>
<id>alimaven</id>
<name>aliyun maven</name>
<url>http://maven.aliyun.com/nexus/content/groups/public/</url>
<mirrorOf>central</mirrorOf>
</mirror>

R 语言 搭建
下载源码
# cd ${R_HOME}
# yum install gcc-gfortran readline-devel libXt-devel

error:
# yum install gcc-gfortran #否则报”configure: error: No F77 compiler found”错误

# yum install gcc gcc-c++ #否则报”configure: error: C++ preprocessor “/lib/cpp” fails sanity check”错误

# yum install readline-devel #否则报”–with-readline=yes (default) and headers/libs are not available”错误

# yum install libXt-devel #否则报”configure: error: –with-x=yes (default) and X11 headers/libs are not available”错误

# ./configure --enable-R-shlib

#make && make install
# vi ~/.bashrc (配置环境变量)
export R_HOME=/opt/modules/R-3.2.0
export PATH=$R_HOME/bin:$PATH、

6、正式编译

上传源码压缩包并解压
# cd ${SPARK_HOME_SRC}
# ${SPARK_HOME_SRC}/./dev/make-distribution.sh --tgz -Psparkr -Phadoop-2.4 -Dhadoop.version=2.5.0-cdh5.3.6 -Phive -Phive-thriftserver -Pyarn
a. 添加 sparkr

b. 添加hadoop版本 -Dhadoop.version=2.5.0-cdh5.3.6

c. scala 压缩包解压到${SPARK_HOME_SRC}/build/

d. 修改为对应的版本(dev/make-distribution.sh)
初始
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)
替换为下面对应的参数值
VERSION=2.10
SCALA_VERSION=2.11
SPARK_HADOOP_VERSION=2.5.0-cdh5.3.6
SPARK_HIVE=1

e.spark pom.xml 添加 cdh reponsitory
<repository>
<id>cloudera</id>
<url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
<releases>
<enabled>true</enabled>
</releases>
<snapshots>
<enabled>false</enabled>
</snapshots>
</repository>

如果不添加会出现如下错误信息:
Failed to execute goal on project spark-launcher_2.11: Could not resolve dependencies for project org.apache.spark:spark-launcher_2.11:jar:2.1.0: Could not find artifact org.apache.hadoop:hadoop-client:jar:2.5.0-cdh5.3.6

[ERROR] After correcting the problems, you can resume the build with the command
[ERROR] mvn <goals> -rf :spark-launcher_2.11
-rf :spark-launcher_2.11

# ${SPARK_HOME_SRC}/./dev/make-distribution.sh --tgz -Psparkr -Phadoop-2.4 -Dhadoop.version=2.5.0-cdh5.3.6 -Phive -Phive-thriftserver -Pyarn -rf :spark-launcher_2.11

下面是没有使用R模块的
# ${SPARK_HOME_SRC}/./dev/make-distribution.sh --tgz -Phadoop-2.4 -Dhadoop.version=2.5.0-cdh5.3.6 -Phive -Phive-thriftserver -Pyarn
===============================================================================

最终打包编译 生成的包目录对应为${SPARK_HOME_SRC}/spark-2.1.0-bin-2.5.0-cdh5.3.6.tgz
SPARK_VERSION-bin-HADOOP-VERSION.tgz

NOTE:
将编译好的spark 源码打包保存一份，后面 spark sql 及 spark streaming 后续学习会使用到相关的 jar 包.

=====================================================================================

真正使用R 运行在 spark 上，前面编译完成以后你需要初始化 R
# cd {SPARK_HOME_SRC}/R/
# ./install-dev.sh
参考文章：https://github.com/apache/spark/tree/master/R