//定義case class，相當(dāng)于表結(jié)構(gòu)
case class People(var name:String,var age:Int)
object TestDataFrame1 {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("RDDToDataFrame").setMaster("local")
    val sc = new SparkContext(conf)
    val context = new SQLContext(sc)
    // 將本地的數(shù)據(jù)讀入 RDD， 并將 RDD 與 case class 關(guān)聯(lián)
    val peopleRDD = sc.textFile("E:\\666\\people.txt")
      .map(line => People(line.split(",")(0), line.split(",")(1).trim.toInt))
    import context.implicits._
    // 將RDD 轉(zhuǎn)換成 DataFrames
    val df = peopleRDD.toDF
    //將DataFrames創(chuàng)建成一個(gè)臨時(shí)的視圖
    df.createOrReplaceTempView("people")
    //使用SQL語(yǔ)句進(jìn)行查詢
    context.sql("select * from people").show()
  }
}

object TestDataFrame2 {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("TestDataFrame2").setMaster("local")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
    val fileRDD = sc.textFile("E:\\666\\people.txt")
    // 將 RDD 數(shù)據(jù)映射成 Row，需要 import org.apache.spark.sql.Row
    val rowRDD: RDD[Row] = fileRDD.map(line => {
      val fields = line.split(",")
      Row(fields(0), fields(1).trim.toInt)
    })
    // 創(chuàng)建 StructType 來(lái)定義結(jié)構(gòu)
    val structType: StructType = StructType(
      //字段名，字段類型，是否可以為空
      StructField("name", StringType, true) ::
      StructField("age", IntegerType, true) :: Nil
    )
    /**
      * rows: java.util.List[Row],
      * schema: StructType
      * */
    val df: DataFrame = sqlContext.createDataFrame(rowRDD,structType)
    df.createOrReplaceTempView("people")
    sqlContext.sql("select * from people").show()
  }
}

object TestDataFrame3 {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("TestDataFrame2").setMaster("local")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
    val df: DataFrame = sqlContext.read.json("E:\\666\\people.json")
    df.createOrReplaceTempView("people")
    sqlContext.sql("select * from people").show()
  }
}

object TestRead {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("TestDataFrame2").setMaster("local")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
    //方式一
    val df1 = sqlContext.read.json("E:\\666\\people.json")
    val df2 = sqlContext.read.parquet("E:\\666\\users.parquet")
    //方式二
    val df3 = sqlContext.read.format("json").load("E:\\666\\people.json")
    val df4 = sqlContext.read.format("parquet").load("E:\\666\\users.parquet")
    //方式三，默認(rèn)是parquet格式
    val df5 = sqlContext.load("E:\\666\\users.parquet")
  }
}

object TestSave {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("TestDataFrame2").setMaster("local")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
    val df1 = sqlContext.read.json("E:\\666\\people.json")
    //方式一
    df1.write.json("E:\\111")
    df1.write.parquet("E:\\222")
    //方式二
    df1.write.format("json").save("E:\\333")
    df1.write.format("parquet").save("E:\\444")
    //方式三
    df1.write.save("E:\\555")

  }
}

df1.write.format("parquet").mode(SaveMode.Ignore).save("E:\\444")

object TestMysql {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("TestMysql").setMaster("local")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)

    val url = "jdbc:mysql://192.168.123.102:3306/hivedb"
    val table = "dbs"
    val properties = new Properties()
    properties.setProperty("user","root")
    properties.setProperty("password","root")
    //需要傳入Mysql的URL、表明、properties（連接數(shù)據(jù)庫(kù)的用戶名密碼）
    val df = sqlContext.read.jdbc(url,table,properties)
    df.createOrReplaceTempView("dbs")
    sqlContext.sql("select * from dbs").show()

  }
}

<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-hive -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_2.11</artifactId>
            <version>2.3.0</version>
        </dependency>

<configuration>
        <property>
                <name>javax.jdo.option.ConnectionURL</name>
                <value>jdbc:mysql://localhost:3306/hivedb?createDatabaseIfNotExist=true</value>
                <description>JDBC connect string for a JDBC metastore</description>
                <!-- 如果 mysql 和 hive 在同一個(gè)服務(wù)器節(jié)點(diǎn)，那么請(qǐng)更改 hadoop02 為 localhost -->
        </property>
        <property>
                <name>javax.jdo.option.ConnectionDriverName</name>
                <value>com.mysql.jdbc.Driver</value>
                <description>Driver class name for a JDBC metastore</description>
        </property>
        <property>
                <name>javax.jdo.option.ConnectionUserName</name>
                <value>root</value>
                <description>username to use against metastore database</description>
        </property>
        <property>
                <name>javax.jdo.option.ConnectionPassword</name>
                <value>root</value>
        <description>password to use against metastore database</description>
        </property>
    <property>
                <name>hive.metastore.warehouse.dir</name>
                <value>/hive/warehouse</value>
                <description>hive default warehouse, if nessecory, change it</description>
        </property>  
</configuration>

object TestHive {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setMaster("local").setAppName(this.getClass.getSimpleName)
    val sc = new SparkContext(conf)
    val sqlContext = new HiveContext(sc)
    sqlContext.sql("select * from myhive.student").show()
  }
}