DF保存到mysql中或者保存成.csv .json parquet文件

DataFrame保存到mysql

import java.util.Properties

import cn.doit.sparksql.day01.utils.SparkUtils
import org.apache.spark.sql.{DataFrame, SaveMode, SparkSession}

/**
 * @description:DataFrame保存到mysql
 **/
object DFSaveMysql {

  def main(args: Array[String]): Unit = {

    val spark: SparkSession = SparkUtils.getSparkSession()
    import spark.implicits._

    // 获得一个DF
    val frame: DataFrame = spark.read
      .options(Map("header" -> "true", "inferSchema" -> "true"))
      .csv("doc/stu2.csv")
    frame.printSchema()
    frame.show()

    val pro = new Properties()
    pro.setProperty("user", "root")
    pro.setProperty("password", "123456")

    //
    // Table or view 'people' already exists. SaveMode: ErrorIfExists.;
    /***
     * def mode(saveMode: SaveMode)
     *
     * 警告：Wed Jan 01 15:02:58 CST 2020 WARN: Establishing SSL connection without server's
     *      identity verification is not recommended. According to MySQL 5.5.45+, 5.6.26+ and 5.7.6+
     *      requirements SSL connection must be established by default if explicit option isn't set.
     *      For compliance with existing applications not using SSL the verifyServerCertificate property
     *      is set to 'false'. You need either to explicitly disable SSL by setting useSSL=false,
     *      or set useSSL=true and provide truststore for server certificate verification.
     *
     * 异常：Exception in thread "main" org.apache.spark.sql.AnalysisException:Table or view 'people' already exists. SaveMode: ErrorIfExists.;
     */
    frame.write.mode(SaveMode.Append).jdbc("jdbc:mysql://localhost:3306/bigdata", "people", pro)

  }
}

DataFrame保存成.csv .json parquet文件的代码实现

import cn.doit.sparksql.day01.utils.SparkUtils
import org.apache.spark.sql.{DataFrame, SparkSession}

/**
 * DataFrame保存成.csv .json parquet文件
 **/
object DFSaveFiles {

  def main(args: Array[String]): Unit = {

    val spark: SparkSession = SparkUtils.getSparkSession()
    import spark.implicits._

    // 获得一个DF
    val frame: DataFrame = spark.read
      .options(Map("header" -> "true", "inferSchema" -> "true"))
      .csv("doc/stu2.csv")
    frame.printSchema()
    frame.show()

    /** *
     * 保存为json
     * 保存为CSV 没有头文件
     * 保存为CSV 有头文件
     * 保存为parquet
     * 保存为 text ，这个函数只能保存单列
     * frame.write.text 的保存信息如下：
     * Text data source supports only a single column, and you have 6 columns.;
     */
    //    frame.write.json("doc/output/json")
    //    frame.write.csv("doc/output/csv1")
    //    frame.write.option("header", true).csv("doc/output/csv2")
    //    frame.write.parquet("doc/output/parquet")

    import org.apache.spark.sql.functions._

    // def concat_ws(sep: String, exprs: Column*)
    //    frame.write.text("doc/output/text")
//    frame.selectExpr("concat_ws('\t' , id , name , age ,sex ,city,score )").write.text("doc/output/text")
  }
}