1、读取json

2、读取csv和tsv

3、ObjectFile

4、读取hdfs中的数据

5、读取Parquet文件

6、读取Hive 和mysql

读取json文件

def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setMaster("local[*]")
      .setAppName(this.getClass.getName)
    val sc = new SparkContext(conf)
    val inputJsonFile = sc.textFile("D:\\studyplace\\sparkBook\\chapter4\\data\\chapter4_3_2.json")

    val content = inputJsonFile.map(JSON.parseFull)

    println(content.collect.mkString(","))
    //遍历
    content.foreach(
      {
        case Some(map : Map[String,Any]) => println(map)
        case None => println("无效的JSON")
        case _ => println("其他异常...")
      }
    )
    sc.stop()
  }

注意:json文件中必须是完整的json字符串,并且是同一个文件

读取csv和tsv文件

csv文件为逗号分隔符,tsv为制表符分隔符

val inputFile = sc.textFile("文件路径")
inputFile.flatMap(_.split("分隔符"))

读取SequenceFile

只有键值对的数据才能用sequenceFile格式存储,类比java中Map,scala中Tuple2
sequenceFile可以逐条压缩数据,也可以压缩整个数据块,默认不启用压缩

val inputFile = sc.sequenceFile[String,String]("文件路径")

泛型为读取出的key和value的数据类型

读取ObjectFile格式的数据

spark可以读取Object格式的数据生成RDD,RDD每一个元素都可以被还原成之前的对象
定义一个类

package chapter4

case class Person(name: String, age: Int)

读取数据

import chapter4.Person
import org.apache.spark.{SparkConf, SparkContext}
object chapte4_3_5 {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf()
      .setAppName(this.getClass.getName)
      .setMaster("local[*]")
    val sc = new SparkContext(conf)
    val rddData = sc.objectFile[Person]("D:\\studyplace\\sparkBook\\chapter4\\data\\chapter4_3_5.object")
    println(rddData.collect.toList)
    sc.stop()
  }
}

对象序列化为数据,保留对象的原始信息,包括包名,因此泛型Person必须一致

读取hdfs中的数据(显式调用hadoopAPI)

import org.apache.hadoop.io.{LongWritable, Text}
import org.apache.hadoop.mapred.TextInputFormat
import org.apache.spark.{SparkConf, SparkContext}

object chapter4_3_6 {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf()
      .setMaster("local[*]")
      .setAppName("chapter4_3_6")
    val sc = new SparkContext(conf)

    val path = "hdfs://ip:8020/路径"
    val inputHadoopFile = sc.newAPIHadoopFile[LongWritable,Text,TextInputFormat](path)

    val result = inputHadoopFile.map(_._2.toString).collect()
    println(result.mkString(","))
    sc.stop()
  }
}

对于 newAPIHadoopFile[LongWritable,Text,TextInputFormat] 第一个泛型LongWritable 是hadoop读取文件的偏移量,Text是偏移量对应的数据内容,TextInputFormat
直接对inputHadoopFile.collect.mkString(",")会报序列化错误,
Writable的子类型(LongWritable,IntWritable,Text)需要通过inputHadoopFile.map(_._2.toString) j进行序列化

读取mysql中的数据

导入依赖

<dependency>
    <groupId>mysql</groupId>
    <artifactId>mysql-connector-java</artifactId>
    <version>5.1.40</version>
</dependency>

package chapter4

import java.sql.DriverManager

import org.apache.spark.rdd.JdbcRDD
import org.apache.spark.{SparkConf, SparkContext}

object chapter4_3_7 {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("chapter4_3_7").setMaster("local[*]")
    val sc = new SparkContext(conf)

    val inputMysql = new JdbcRDD(sc, () => {
      Class.forName("com.mysql.jdbc.Driver")
      DriverManager.getConnection("jdbc:mysql://localhost:3306/spark?" +
        "useUnicode=true&characterEncoding=utf-8", "root", "123456")
    },
      "select * from person where id >= ? and id <= ?;",
      1,  //查询条件上界
      3, //查询条件下界
      1,  //分区数
      r => (r.getInt(1), r.getString(2), r.getInt(3)))

    println("查询到的记录条目数:"+inputMysql.count)
    inputMysql.foreach(println)
    sc.stop()
  }
}

操作Parquet文件

package com.imooc.spark

import org.apache.spark.sql.SparkSession

/**
 * Parquet文件操作
 */
object ParquetApp {

  def main(args: Array[String]) {

    val spark = SparkSession.builder().appName("SparkSessionApp")
      .master("local[2]").getOrCreate()


    /**
     * spark.read.format("parquet").load 这是标准写法
     */
    val userDF = spark.read.format("parquet").load("file:///home/hadoop/app/spark-2.1.0-bin-2.6.0-cdh5.7.0/examples/src/main/resources/users.parquet")

    userDF.printSchema()
    userDF.show()

    userDF.select("name","favorite_color").show

    userDF.select("name","favorite_color").write.format("json").save("file:///home/hadoop/tmp/jsonout")

    spark.read.load("file:///home/hadoop/app/spark-2.1.0-bin-2.6.0-cdh5.7.0/examples/src/main/resources/users.parquet").show

    //会报错，因为sparksql默认处理的format就是parquet
    spark.read.load("file:///home/hadoop/app/spark-2.1.0-bin-2.6.0-cdh5.7.0/examples/src/main/resources/people.json").show

    spark.read.format("parquet").option("path","file:///home/hadoop/app/spark-2.1.0-bin-2.6.0-cdh5.7.0/examples/src/main/resources/users.parquet").load().show
    spark.stop()
  }

}

读取Hive 和mysql

package com.imooc.spark

import org.apache.spark.sql.SparkSession

/**
 * 使用外部数据源综合查询Hive和MySQL的表数据
 */
object HiveMySQLApp {

  def main(args: Array[String]) {
    val spark = SparkSession.builder().appName("HiveMySQLApp")
      .master("local[2]").getOrCreate()

    // 加载Hive表数据
    val hiveDF = spark.table("emp")

    // 加载MySQL表数据
    val mysqlDF = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306").option("dbtable", "spark.DEPT").option("user", "root").option("password", "root").option("driver", "com.mysql.jdbc.Driver").load()

    // JOIN
    val resultDF = hiveDF.join(mysqlDF, hiveDF.col("deptno") === mysqlDF.col("DEPTNO"))
    resultDF.show


    resultDF.select(hiveDF.col("empno"),hiveDF.col("ename"),
      mysqlDF.col("deptno"), mysqlDF.col("dname")).show

    spark.stop()
  }

}

参考：http://www.mamicode.com/info-detail-2214729.html

sparkSql外部数据源