spark源码编译与基本概念

欲装spark-2.4.0

linux环境有，java8 ，maven-3.6.0，hadoop-2.6.0-cdh5.7.0

下载源码并修改pom.xml里的文件下载地址

然后在spark目录下运行以下指令，设置需要对应的支持和名字

./dev/make-distribution.sh --name 2.6.0-cdh5.7.0 --tgz -Phadoop-2.6 -Phive -Phive-thriftserver -Pyarn -Dhadoop.version=2.6.0-cdh5.7.0

卡住不动，这是在检查环境变量，设置下

修改make-distribution.sh
这些部分加注释
然后加上这么一段，hive=1就是让spark支持hive

在idea里设置pom.xml

spark版本和hdfs的

下载地址

依赖

pom.xml全部代码

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
  <modelVersion>4.0.0</modelVersion>
  <groupId>com.ruozedata.bigdata</groupId>
  <artifactId>g5-spark</artifactId>
  <version>1.0</version>
  <properties>
    <scala.version>2.11.8</scala.version>
    <spark.version>2.4.0</spark.version>
    <hadoop.version>2.6.0-cdh5.7.0</hadoop.version>
  </properties>

  <repositories>
    <repository>
      <id>scala-tools.org</id>
      <name>Scala-Tools Maven2 Repository</name>
      <url>http://scala-tools.org/repo-releases</url>
    </repository>

    <repository>
      <id>cloudera</id>
      <url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
    </repository>


  </repositories>

  <pluginRepositories>
    <pluginRepository>
      <id>scala-tools.org</id>
      <name>Scala-Tools Maven2 Repository</name>
      <url>http://scala-tools.org/repo-releases</url>
    </pluginRepository>
  </pluginRepositories>

  <dependencies>
    <dependency>
      <groupId>org.scala-lang</groupId>
      <artifactId>scala-library</artifactId>
      <version>${scala.version}</version>
    </dependency>

    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-core_2.11</artifactId>
      <version>${spark.version}</version>
    </dependency>

    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-client</artifactId>
      <version>${hadoop.version}</version>
    </dependency>

  </dependencies>

  <build>
    <sourceDirectory>src/main/scala</sourceDirectory>
    <testSourceDirectory>src/test/scala</testSourceDirectory>
    <plugins>
      <plugin>
        <groupId>org.scala-tools</groupId>
        <artifactId>maven-scala-plugin</artifactId>
        <executions>
          <execution>
            <goals>
              <goal>compile</goal>
              <goal>testCompile</goal>
            </goals>
          </execution>
        </executions>
        <configuration>
          <scalaVersion>${scala.version}</scalaVersion>
          <args>
            <arg>-target:jvm-1.5</arg>
          </args>
        </configuration>
      </plugin>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-eclipse-plugin</artifactId>
        <configuration>
          <downloadSources>true</downloadSources>
          <buildcommands>
            <buildcommand>ch.epfl.lamp.sdt.core.scalabuilder</buildcommand>
          </buildcommands>
          <additionalProjectnatures>
            <projectnature>ch.epfl.lamp.sdt.core.scalanature</projectnature>
          </additionalProjectnatures>
          <classpathContainers>
            <classpathContainer>org.eclipse.jdt.launching.JRE_CONTAINER</classpathContainer>
            <classpathContainer>ch.epfl.lamp.sdt.launching.SCALA_CONTAINER</classpathContainer>
          </classpathContainers>
        </configuration>
      </plugin>
    </plugins>
  </build>
  <reporting>
    <plugins>
      <plugin>
        <groupId>org.scala-tools</groupId>
        <artifactId>maven-scala-plugin</artifactId>
        <configuration>
          <scalaVersion>${scala.version}</scalaVersion>
        </configuration>
      </plugin>
    </plugins>
  </reporting>
</project>

RDD：弹性分布式数据集

代表一个不可变的（val），分区的，一群元素，这样可以并行化操作。弹性体现在依赖关系，如果rdd2挂了，会从rdd1重新计算。

看下源码

 rdd类，泛型，抽象，继承序列化和logging，log和ser都是接口，本来是都要用with，但是第一个接口要用extends   
        abstract class RDD[T: ClassTag](
            @transient private var _sc: SparkContext,
            @transient private var deps: Seq[Dependency[_]]
          ) extends Serializable with Logging

五大特性:

1.rdd里面由分区构成
2.rdd作用函数相当于函数作用于每个分区上
3.rdd之间有依赖关系（rdd1=map>rdd2)
4.key-value类型的rdd（比如groupby）有一个分区器作用
5.数据在哪倾向于在哪做计算，移动计算>移动数据

分别对应源码的

1.protected def getPartitions
2.def compute
3.protected def getDependencies
4.@transient val partitioner
5. protected def getPreferredLocations

hdfs也是key-values的
key是偏移量
0 xxxxxxxx
第一行的长度(offset) yyyyyyyyyy

spark基本程序模板

import org.apache.spark.{SparkConf, SparkContext}
//spark自定义参数必须要spark.开头

object SparkContextApp {
  def main(args: Array[String]): Unit = {
    //本地模式
    val sparkconf = new SparkConf().setMaster("local[2]").setAppName("asd")
    //一个context运行在一个jvm。
    val sc = new SparkContext(sparkconf)
    sc.stop()
  }
}