1. Spark中的RDD
3. RDD在Spark中的作用
迭代式计算
其主要实现思想就是RDD，把所有计算的数据保存在分布式的内存中。迭代计算通常情况下都是对同一个数据集做反复的迭代计算，数据在内存中将大大提升IO操作。这也是Spark涉及的核心：内存计算
交互式计算
因为Spark是用scala语言实现的，Spark和scala能够紧密的集成，所以Spark可以完美的运用scala的解释器，使得其中的scala可以向操作本地集合对象一样轻松操作分布式数据集
4. Spark中的名词解释
5. 创建RDD的两种方式
通过并行化集合创建RDD(用于测试)
val list = List("java c++ java","java java java c++")val rdd = sc.parallelize(list)通过加载hdfs中的数据创建RDD(生产环境)
val rdd = sc.textFile("hdfs://uplooking01:8020/sparktest/")6. IDEA开发Spark
6.1 pom依赖
<?xml version="1.0" encoding="UTF-8"?><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/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <groupId>com.uplooking.bigdata</groupId> <artifactId>2018-11-08-spark</artifactId> <version>1.0-SNAPSHOT</version> <properties> <maven.compiler.source>1.8</maven.compiler.source> <maven.compiler.target>1.8</maven.compiler.target> <scala.version>2.11.8</scala.version> <spark.version>2.2.0</spark.version> <hadoop.version>2.7.5</hadoop.version> </properties> <dependencies> <!-- 导入scala的依赖 --> <dependency> <groupId>org.scala-lang</groupId> <artifactId>scala-library</artifactId> <version>${scala.version}</version> </dependency> <!-- 导入spark的依赖 --> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_2.11</artifactId> <version>${spark.version}</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-sql_2.11</artifactId> <version>${spark.version}</version> </dependency> <!-- 指定hadoop-client API的版本 --> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-client</artifactId> <version>${hadoop.version}</version> </dependency> </dependencies> <build> <plugins> <!--编译Scala--> <plugin> <groupId>net.alchim31.maven</groupId> <artifactId>scala-maven-plugin</artifactId> <version>3.2.2</version> <executions> <execution> <id>scala-compile-first</id> <phase>process-resources</phase> <goals> <goal>add-source</goal> <goal>compile</goal> </goals> </execution> <execution> <id>scala-test-compile</id> <phase>process-test-resources</phase> <goals> <goal>testCompile</goal> </goals> </execution> </executions> </plugin> <!--编译Java--> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-compiler-plugin</artifactId> <executions> <execution> <phase>compile</phase> <goals> <goal>compile</goal> </goals> </execution> </executions> </plugin> <!-- 打jar插件 --> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-shade-plugin</artifactId> <version>2.4.3</version> <configuration> <createDependencyReducedPom>false</createDependencyReducedPom> </configuration> <executions> <execution> <phase>package</phase> <goals> <goal>shade</goal> </goals> <configuration> <filters> <filter> <artifact>*:*</artifact> <excludes> <exclude>META-INF/*.SF</exclude> <exclude>META-INF/*.DSA</exclude> <exclude>META-INF/*.RSA</exclude> </excludes> </filter> </filters> </configuration> </execution> </executions> </plugin> </plugins> </build></project>6.2 编写spark程序
val conf = new SparkConf()conf.setAppName("Ops1")val sc = new SparkContext(conf)val rdd1: RDD[String] = sc.parallelize(List("java c+ java", "java java c++"))val ret = rdd1.collect().toBufferprintln(ret)6.3 打包
6.4 在Driver上运行jar包
spark-submit --master spark://uplooking01:7077 --class com.uplooking.bigdata.spark01.Ops1 original-spark-1.0-SNAPSHOT.jar
7. 本地运行Spark程序
import org.apache.spark.rdd.RDDimport org.apache.spark.{SparkConf, SparkContext}import scala.collection.mutableobject Ops1 { def main(args: Array[String]): Unit = { val conf = new SparkConf() conf.setAppName("Ops1") conf.setMaster("local[4]") val sc = new SparkContext(conf) //一般不会指定最小分区数 val rdd1 = sc.textFile("hdfs://uplooking01:8020/sparktest/") val rdd2: RDD[String] = rdd1.flatMap(line => line.split(" ")) val rdd3: RDD[(String, Int)] = rdd2.map(word => (word, 1)) val rdd4: RDD[(String, Int)] = rdd3.reduceByKey(_ + _) val ret: mutable.Buffer[(String, Int)] = rdd4.collect().toBuffer println(ret) println(rdd1.partitions.length) }}8. RDD中的分区数
并行化的方式指定分区数(一般会指定分区数)
val rdd = sc.parallelize(List("java c+ java", "java java c++"), 2)textFile的方式指定分区数
9. Spark作业的运行流程
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