sparkSQL1.1入门之八：sparkSQL之综合应用

package doc

import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.catalyst.expressions.Row
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.mllib.clustering.KMeans
import org.apache.spark.mllib.linalg.Vectors

object SQLMLlib {
  def main(args: Array[String]) {
    //屏蔽不必要的日志显示在终端上
    Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
    Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)

    //设置运行环境
    val sparkConf = new SparkConf().setAppName("SQLMLlib")
    val sc = new SparkContext(sparkConf)
    val hiveContext = new HiveContext(sc)

    //使用sparksql查出每个店的销售数量和金额
    hiveContext.sql("use saledata")
    val sqldata = hiveContext.sql("select a.locationid, sum(b.qty) totalqty,sum(b.amount) totalamount from tblStock a join tblstockdetail b on a.ordernumber=b.ordernumber group by a.locationid")

    //将查询数据转换成向量
    val parsedData = sqldata.map {
      case Row(_, totalqty, totalamount) =>
        val features = Array[Double](totalqty.toString.toDouble, totalamount.toString.toDouble)
        Vectors.dense(features)
    }

    //对数据集聚类，3个类，20次迭代，形成数据模型
    //注意这里没设置partition的数量，会使用MLLib的缺省partition数200
    val numClusters = 3
    val numIterations = 20
    val model = KMeans.train(parsedData, numClusters, numIterations)

    //用模型对读入的数据进行分类，并输出
    //由于partition没设置，输出为200个小文件，可以使用bin/hdfs dfs -getmerge 合并下载到本地
    val result2 = sqldata.map {
      case Row(locationid, totalqty, totalamount) =>
        val features = Array[Double](totalqty.toString.toDouble, totalamount.toString.toDouble)
        val linevectore = Vectors.dense(features)
        val prediction = model.predict(linevectore)
        locationid + " " + totalqty + " " + totalamount + " " + prediction
    }.saveAsTextFile(args(0))

    sc.stop()
  }
}

package doc

//由于暂时手上缺少数据，本例只给出框架，以后有机会补上
import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.{SparkContext, SparkConf}
import org.apache.spark.graphx._
import org.apache.spark.rdd.RDD

object SQLGraphX {
  def main(args: Array[String]) {
    //屏蔽不必要的日志显示在终端上
    Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
    Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)

    //设置运行环境
    val sparkConf = new SparkConf().setAppName("SQLGraphX")
    val sc = new SparkContext(sparkConf)
    val hiveContext = new HiveContext(sc)

    //切换到销售数据库
    hiveContext.sql("use saledata")

    //使用sparksql查出店铺的销量和库存，作为图的顶点
    //其中locationid为VertexID，(销量,库存)为VD，一般为(Int,Int)类型
    val vertexdata = hiveContext.sql("select a.locationid, b.saleQty, b.InvQty From a join b on a.col1=b.col2 where conditions")

    //使用sparksql查出店铺之间的距离，也可以是花费时间等和调拨相关的属性，作为图的边
    //distance为ED，可以使用Int、Long、Double等数据类型
    val edgedata = hiveContext.sql("select srcid, distid, distance From distanceInfo")

    //构造vertexRDD和edgeRDD
    val vertexRDD: RDD[(Long, (Int, Int))] = vertexdata.map(...)
    val edgeRDD: RDD[Edge[Int]] = edgedata.map(...)

    //构造图Graph[VD,ED]
    val graph: Graph[(Int, Int), Int] = Graph(vertexRDD, edgeRDD)

    //根据调拨的规则进行图处理
    val initialGraph = graph.mapVertices(...)
    initialGraph.pregel(...)

    //输出

    sc.stop()
  }
}