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()
}
}

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()
}
}

3、总结
1.SparkSQL写数据:
DataFrame/DataSet.write.json/csv/jdbc
2.SparkSQL读数据:
SparkSession.read.json/csv/text/jdbc/format