3
我們試圖實現一個簡單的spark作業，它讀取一個CSV文件（1行數據），並使用預先構建的隨機森林模型對象進行預測。這項工作不包括任何數據預處理或數據處理。減少Apache Spark作業/應用程序的運行時間
我們在獨立模式下運行spark，應用程序在本地運行。的配置如下： RAM：8GB 內存：40GB 芯數：2 火花版本：1.5.2 Scala的版本：2.10.5 輸入文件大小：1KB（1行的數據） 模型文件大小：1,595 KB（400樹隨機森林）
目前，spark-submit中的實現大約需要13秒。然而，運行時間是該應用程序，因此
巨大關注的是有沒有辦法來優化代碼，使運行時間縮短至1或2秒？ （高優先級）
我們注意到，啓動時實際代碼的執行時間約爲7-8秒，設置上下文大約需要5-6秒，所以有一種方法可以在運行時保持火花上下文運行火花提交。
這裏是應用程序代碼
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.SparkConf
object RF_model_App {
def main(args: Array[String]) {
val conf = new SparkConf().setAppName("Simple Application")
val sc = new SparkContext(conf)
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.feature4.{RandomForestfeature4Model, RandomForestClassifier}
import org.apache.spark.ml.evaluation.Multiclassfeature4Evaluator
import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer}
import org.apache.spark.sql.functions.udf
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.feature.StringIndexer
import sqlContext.implicits._
val Test = sqlContext.read.format("com.databricks.spark.csv").option("header","true").load("/home/ubuntu/Test.csv")
Test.registerTempTable("Test")
val model_L1 = sc.objectFile[RandomForestfeature4Model]("/home/ubuntu/RF_L1.model").first()
val toInt = udf[Int, String](_.toInt)
val toDouble = udf[Double, String](_.toDouble)
val featureDf = Test.withColumn("id1", toInt(Test("id1"))) .withColumn("id2", toInt(Test("id2"))) .withColumn("id3", toInt(Test("id3"))) .withColumn("id4", toInt(Test("id4"))) .withColumn("feature3", toInt(Test("feature3"))) .withColumn("feature9", toInt(Test("feature9"))) .withColumn("feature10", toInt(Test("feature10"))) .withColumn("feature12", toInt(Test("feature12"))) .withColumn("feature14", toDouble(Test("feature14"))) .withColumn("feature15", toDouble(Test("feature15"))) .withColumn("feature16", toInt(Test("feature16"))) .withColumn("feature17", toDouble(Test("feature17"))) .withColumn("feature18", toInt(Test("feature18")))
val feature4_index = new StringIndexer() .setInputCol("feature4") .setOutputCol("feature4_index")
val feature6_index = new StringIndexer() .setInputCol("feature6") .setOutputCol("feature6_index")
val feature11_index = new StringIndexer() .setInputCol("feature11") .setOutputCol("feature11_index")
val feature8_index = new StringIndexer() .setInputCol("feature8") .setOutputCol("feature8_index")
val feature13_index = new StringIndexer() .setInputCol("feature13") .setOutputCol("feature13_index")
val feature2_index = new StringIndexer() .setInputCol("feature2") .setOutputCol("feature2_index")
val feature5_index = new StringIndexer() .setInputCol("feature5") .setOutputCol("feature5_index")
val feature7_index = new StringIndexer() .setInputCol("feature7") .setOutputCol("feature7_index")
val vectorizer_L1 = new VectorAssembler() .setInputCols(Array("feature3", "feature2_index", "feature6_index", "feature4_index", "feature8_index", "feature7_index", "feature5_index", "feature10", "feature9", "feature12", "feature11_index", "feature13_index", "feature14", "feature15", "feature18", "feature17", "feature16")).setOutputCol("features_L1")
val feature_pipeline_L1 = new Pipeline() .setStages(Array(feature4_index, feature6_index, feature11_index,feature8_index, feature13_index, feature2_index, feature5_index, feature7_index,vectorizer_L1))
val testPredict= feature_pipeline_L1.fit(featureDf).transform(featureDf)
val getPOne = udf((v: org.apache.spark.mllib.linalg.Vector) => v(1))
val getid2 = udf((v: Int) => v)
val L1_output = model_L1.transform(testPredict).select(getid2($"id2") as "id2",getid2($"prediction") as "L1_prediction",getPOne($"probability") as "probability")
L1_output.repartition(1).write.format("com.databricks.spark.csv").option("header", "true").mode("overwrite").save("/home/L1_output")
}
};