远程调用Spark平台中的程序

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用scala语言，开发好了在spark平台上可以一直运行的机器学习模型
现在有个需求：
要远程调用该模型的一些方法并获取结果
那么可以使用jetty在服务器端主节点占用一个端口然后对外提供http服务

package com.xxx.rec.basic.ccam.jettyimport javax.servlet.http.{HttpServletRequest, HttpServletResponse}import com.xxx.rec.basic.ccam.CanonicalCorrelationAnalysisModelimport org.mortbay.jetty.{HttpStatus, Request, Server}import org.mortbay.jetty.handler._object CannonicalCorrelationAnalysisModelJerseyServer  extends AbstractHandler{  var model: CanonicalCorrelationAnalysisModel = null  /**    * 处理请求 返回响应    * @param target    * @param request    * @param response    * @param dispatch    */  override def handle(target: String,                      request: HttpServletRequest,                      response: HttpServletResponse,                      dispatch: Int): Unit = {    val url=request.getRequestURI    url.substring(url.lastIndexOf("/")+1,url.length) match {      case "recommend" => {        //request中的target 用,号分割        val target: Seq[String] = request.getParameter("target").split(",").toSeq        val topNum: Int = request.getParameter("topNum").toInt        val result = model.recommend(target, topNum)        response.setStatus(HttpStatus.ORDINAL_200_OK);        response.getWriter().println(result.mkString(","))        request.asInstanceOf[Request].setHandled(true)        response.getWriter.close()      }      case _ => {        response.setStatus(HttpStatus.ORDINAL_404_Not_Found);        request.asInstanceOf[Request].setHandled(true)      }    }  }  def main(args: Array[String]): Unit = {    import org.apache.spark.{SparkConf, SparkContext}    val sparkConf = new SparkConf().setAppName("CanonicalCorrelationAnalysisModelDemo")    val textFilePath = "file:///home/xxx/xxx.txt"    val sc = new SparkContext(sparkConf)    val data = sc.textFile(textFilePath).map { line =>      line.split(' ')    }.cache()    model = CanonicalCorrelationAnalysisModel.createModel(data, 0.3, 5)    val server=new Server(9998)    server.setHandler(this)    server.start()  }}

该程序运行后占用了服务器端主节点的9998端口，通过http访问即可

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