Spark 数据源

一、mysql作为数据源

import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
 
/**
  * mysql作为数据源
  *
  * schema信息
  * root
  * |-- uid: integer (nullable = false)
  * |-- xueyuan: string (nullable = true)
  * |-- number_one: string (nullable = true)
  */
object JdbcSource {
  def main(args: Array[String]): Unit = {
    //1.sparkSQL 创建sparkSession
    val sparkSession: SparkSession = SparkSession.builder().appName("JdbcSource")
      .master("local[2]").getOrCreate()
 
    //2.加载数据源
    val urlData: DataFrame = sparkSession.read.format("jdbc").options(Map(
      "url" -> "jdbc:mysql://localhost:3306/urlcount",
      "driver" -> "com.mysql.jdbc.Driver",
      "dbtable" -> "url_data",
      "user" -> "root",
      "password" -> "root"
    )).load()
 
    //测试
    //urlData.printSchema()
    //urlData.show()
 
    //3.过滤数据
    val fData: Dataset[Row] = urlData.filter(x => {
      //uid>2 如何拿到uid？
      x.getAs[Int](0) > 2
    })
 
    fData.show()
    sparkSession.stop()
  }
}

mysql数据：

二、Spark写出数据格式

import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
 
object JdbcSource1 {
  def main(args: Array[String]): Unit = {
    //1.sparkSQL 创建sparkSession
    val sparkSession: SparkSession = SparkSession.builder().appName("JdbcSource")
      .master("local[2]").getOrCreate()
 
    import sparkSession.implicits._
    //2.加载数据源
    val urlData: DataFrame = sparkSession.read.format("jdbc").options(Map(
      "url" -> "jdbc:mysql://localhost:3306/urlcount",
      "driver" -> "com.mysql.jdbc.Driver",
      "dbtable" -> "url_data",
      "user" -> "root",
      "password" -> "root"
    )).load()
 
    //3.uid>2
    val r = urlData.filter($"uid" > 2)
    val rs: DataFrame = r.select($"xueyuan", $"number_one")
 
    //val rs: DataFrame = r.select($"xueyuan")
 
    //写入以text格式
    //rs.write.text("e:/saveText")
 
    //写入以json格式
    //rs.write.json("e:/saveJson")
 
    //写入以csv格式
    rs.write.csv("e:/saveCsv")
 
    //rs.write.parquet("e:/savePar")
 
    rs.show()
    sparkSession.stop()
  }
}

三、Json作为数据源

import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
 
object JsonSource {
  def main(args: Array[String]): Unit = {
    //1.创建sparkSession
    val sparkSession: SparkSession = SparkSession.builder().appName("JsonSource")
      .master("local[2]").getOrCreate()
 
    import sparkSession.implicits._
    //2.读取json数据源
    val jread: DataFrame = sparkSession.read.json("e:/saveJson")
 
    //3.处理数据
    val fread: Dataset[Row] = jread.filter($"xueyuan" === "bigdata")
 
    //4.触发action
    fread.show()
 
    //5.关闭资源
    sparkSession.stop()
  }
}

四、Csv作为数据源

import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
 
object CsvSource {
  def main(args: Array[String]): Unit = {
    //1.创建sparkSession
    val sparkSession: SparkSession = SparkSession.builder().appName("CsvSource")
      .master("local[2]").getOrCreate()
 
    import sparkSession.implicits._
    //2.读取csv数据源
    val cread: DataFrame = sparkSession.read.csv("e:/saveCsv")
 
    //3.处理数据
    val rdf = cread.toDF("id", "xueyuan")
    val rs = rdf.filter($"id" <= 3)
 
    //4.触发action
    rs.show()
 
    //5.关闭资源
    sparkSession.stop()
  }
}