Spark SQL:从入门到精通(三)[多数据源交互]_spark能连接多数据源吗

Spark SQL多数据源交互

Spark SQL可以与多种数据源交互，如普通文本、json、parquet、csv、MySQL等
1.写入不同数据源
2.读取不同数据源

写数据

package cn.itcast.sql

import java.util.Properties

import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, SaveMode, SparkSession}


object WriterDataSourceDemo {
case class Person(id:Int,name:String,age:Int)

  def main(args: Array[String]): Unit = {
    //1.创建SparkSession
    val spark: SparkSession = SparkSession.builder().master("local[*]").appName("SparkSQL")
.getOrCreate()
    val sc: SparkContext = spark.sparkContext
    sc.setLogLevel("WARN")
    //2.读取文件
    val fileRDD: RDD[String] = sc.textFile("D:\\data\\person.txt")
    val linesRDD: RDD[Array[String]] = fileRDD.map(_.split(" "))
    val rowRDD: RDD[Person] = linesRDD.map(line =>Person(line(0).toInt,line(1),line(2).toInt))
    //3.将RDD转成DF
    //注意:RDD中原本没有toDF方法,新版本中要给它增加一个方法,可以使用隐式转换
    import spark.implicits._
    //注意:上面的rowRDD的泛型是Person,里面包含了Schema信息
    //所以SparkSQL可以通过反射自动获取到并添加给DF
    val personDF: DataFrame = rowRDD.toDF
    //==================将DF写入到不同数据源===================
    //Text data source supports only a single column, and you have 3 columns.;
    //personDF.write.text("D:\\data\\output\\text")
    personDF.write.json("D:\\data\\output\\json")
    personDF.write.csv("D:\\data\\output\\csv")
    personDF.write.parquet("D:\\data\\output\\parquet")
    val prop = new Properties()
    prop.setProperty("user","root")
    prop.setProperty("password","root")
    personDF.write.mode(SaveMode.Overwrite).jdbc(
"jdbc:mysql://localhost:3306/bigdata?characterEncoding=UTF-8","person",prop)
    println("写入成功")
    sc.stop()
    spark.stop()
  }
 }
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读数据

package cn.itcast.sql

import java.util.Properties

import org.apache.spark.SparkContext
import org.apache.spark.sql.SparkSession


object ReadDataSourceDemo {
  def main(args: Array[String]): Unit = {
    //1.创建SparkSession
    val spark: SparkSession = SparkSession.builder().master("local[*]").appName("SparkSQL")
.getOrCreate()
    val sc: SparkContext = spark.sparkContext
    sc.setLogLevel("WARN")
    //2.读取文件
    spark.read.json("D:\\data\\output\\json").show()
    spark.read.csv("D:\\data\\output\\csv").toDF("id","name","age").show()
    spark.read.parquet("D:\\data\\output\\parquet").show()
    val prop = new Properties()
    prop.setProperty("user","root")
    prop.setProperty("password","root")
    spark.read.jdbc(
"jdbc:mysql://localhost:3306/bigdata?characterEncoding=UTF-8","person",prop).show()
    sc.stop()
    spark.stop()
  }
}
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总结

1.SparkSQL写数据:

DataFrame/DataSet.write.json/csv/jdbc
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2.SparkSQL读数据:

SparkSession.read.json/csv/text/jdbc/format
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