关于sparksql

1.读取json文件，并且进行查询等操作

所使用的jar包为

json文件内容

{ "id":1 ,"name":" Ella","age":36 } { "id":2,"name":"Bob","age":29 } { "id":3 ,"name":"Jack","age":29 } { "id":4 ,"name":"Jim","age":28 } { "id":5 ,"name":"Damon" } { "id":5 ,"name":"Damon" }

　　val conf = new SparkConf().setAppName("DataFrameTest").setMaster("local")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
    val df = sqlContext.jsonFile("H:\\文件\\数据集\\test1\\1.json");
　　
    df.show()  　　　　　　//查询所有
    df.distinct.show()   //去重
    df.filter(df.col("age")>).show()  //age>20的行
    df.groupBy("name").count().show()   //根据name分组
    df.sort(df("name").asc).show()      //将数据按 name 升序排列
    df.head().foreach(print)           //取出前 3 行数据
    df.select(df("name").as("username")).show()  //查询所有记录的 name 列，并为其取别名为 username
    df.agg("age"->"avg").foreach(print)   //查询年龄 age 的平均值
    df.agg("age"->"min").foreach(print)   //) 查询年龄 age 的最小值

2.编程实现将 RDD 转换为 DataFrame

文件内容

1,Ella,36

2,Bob,29

3,Jack,29

 val conf = new SparkConf()
       conf.setMaster("local")
           .setAppName("Testsql")
       val sc = new SparkContext(conf)
       val sqlContext = new SQLContext(sc)
       //hdfs://192.168.6.134:9000/wys/1.txt
       //H:\文件\数据集
       val aRDD = sc.textFile("H:\\文件\\数据集\\test1\\2.txt", )
      .map { line => Row(line.split(",")(), line.split(",")(),line.split(",")() )} 

      // 第二步，编程方式动态构造元数据
      val structType = StructType(Array(
          StructField("id", StringType, true),
          StructField("name", StringType, true),
          StructField("age", StringType, true)))  

      // 第三步，进行RDD到DataFrame的转换
      val aDF = sqlContext.createDataFrame(aRDD, structType)  

      // 继续正常使用
      aDF.registerTempTable("A")
      val teenagerDF4 = sqlContext.sql("select id,name,age from A")
      teenagerDF4.map(t => "id:"+t()+","+"name:"+t()+","+"age:"+t()).foreach(println)

3.编程实现利用 DataFrame 读写 MySQL 的数据

 val conf = new SparkConf()
       conf.setMaster("local")
             .setAppName("Testsql")
      val sc = new SparkContext(conf)
      val sqlContext = new SQLContext(sc)
      val employeeRDD = sc.parallelize(Array("3 Mary F 26","4 Tom M 23")).map(_.split(" "))
      val schema = StructType(List(StructField("id", IntegerType,true),StructField("name", StringType, true),StructField("gender", StringType,true),StructField("age", IntegerType, true)))
      val rowRDD = employeeRDD.map(p => Row(p().toInt,p().trim,p().trim,p().toInt))
      val employeeDF = sqlContext.createDataFrame(rowRDD, schema)
      val prop = new Properties()
      prop.put("user", "root")
      prop.put("password", "root")
      prop.put("driver","com.mysql.jdbc.Driver")
      employeeDF.write.mode("append").jdbc("jdbc:mysql://localhost:3306/sparktest","sparktest.spark", prop)
      val jdbcDF = sqlContext.read.format("jdbc").option("url","jdbc:mysql://localhost:3306/sparktest").option("driver","com.mysql.jdbc.Driver").option("dbtable","spark").option("user","root").option("password", "root").load()
      jdbcDF.agg("age" -> "max", "age" -> "sum")