文章目录
- 读取json文件
- 读取csv和tsv文件
- 读取SequenceFile
- 读取ObjectFile格式的数据
- 读取hdfs中的数据(显式调用hadoopAPI)
- 读取mysql中的数据
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()
}
}