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+package com.aliyun.odps.spark.examples.makedata_ad
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+
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+import com.alibaba.fastjson.JSON
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+import com.aliyun.odps.spark.examples.myUtils.{MyHdfsUtils, ParamUtils}
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+import org.apache.hadoop.io.compress.GzipCodec
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+import org.apache.spark.sql.SparkSession
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+
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+import scala.collection.JavaConversions._
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+import scala.collection.mutable.ArrayBuffer
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+import scala.io.Source
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+/*
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+
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+ */
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+
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+object makedata_32_bucket_20240622 {
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+ def main(args: Array[String]): Unit = {
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+
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+ val spark = SparkSession
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+ .builder()
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+ .appName(this.getClass.getName)
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+ .getOrCreate()
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+ val sc = spark.sparkContext
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+
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+ val loader = getClass.getClassLoader
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+ val resourceUrl = loader.getResource("20240622_ad_feature_name.txt")
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+ val content =
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+ if (resourceUrl != null) {
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+ val content = Source.fromURL(resourceUrl).getLines().mkString("\n")
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+ Source.fromURL(resourceUrl).close()
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+ content
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+ } else {
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+ ""
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+ }
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+ println(content)
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+ val contentList = content.split("\n")
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+ .map(r=> r.replace(" ", "").replaceAll("\n", ""))
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+ .filter(r=> r.nonEmpty).toList
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+
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+
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+
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+ // 1 读取参数
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+ val param = ParamUtils.parseArgs(args)
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+ val readPath = param.getOrElse("readPath", "/dw/recommend/model/31_ad_sample_data/20240620*")
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+ val savePath = param.getOrElse("savePath", "/dw/recommend/model/32_bucket_data/")
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+ val fileName = param.getOrElse("fileName", "20240620_100")
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+ val sampleRate = param.getOrElse("sampleRate", "1.0").toDouble
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+ val bucketNum = param.getOrElse("bucketNum", "100").toInt
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+
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+ val data = sc.textFile(readPath)
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+ val data1 = data.map(r => {
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+ val rList = r.split("\t")
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+ val doubles = JSON.parseObject(rList(2)).mapValues(_.toString.toDouble)
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+ doubles
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+ }).sample(false, sampleRate ).repartition(20)
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+
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+ val result = new ArrayBuffer[String]()
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+
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+ for (i <- contentList.indices){
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+ println("特征:" + contentList(i))
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+ val data2 = data1.map(r => r.getOrDefault(contentList(i), 0D)).filter(_ > 1E-8).collect().sorted
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+ val len = data2.length
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+ val oneBucketNum = (len - 1) / (bucketNum - 1) + 1 // 确保每个桶至少有一个元素
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+ val buffers = new ArrayBuffer[Double]()
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+
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+ var lastBucketValue = data2(0) // 记录上一个桶的切分点
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+ for (j <- 0 until len by oneBucketNum) {
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+ val d = data2(j)
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+ if (j > 0 && d != lastBucketValue) {
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+ // 如果当前切分点不同于上一个切分点,则保存当前切分点
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+ buffers += d
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+ }
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+ lastBucketValue = d // 更新上一个桶的切分点
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+ }
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+
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+ // 最后一个桶的结束点应该是数组的最后一个元素
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+ if (!buffers.contains(data2.last)) {
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+ buffers += data2.last
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+ }
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+ result.add(contentList(i) + "\t" + bucketNum.toString + "\t" + buffers.mkString(","))
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+ }
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+ val data3 = sc.parallelize(result)
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+
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+
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+ // 4 保存数据到hdfs
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+ val hdfsPath = savePath + "/" + fileName
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+ if (hdfsPath.nonEmpty && hdfsPath.startsWith("/dw/recommend/model/")) {
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+ println("删除路径并开始数据写入:" + hdfsPath)
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+ MyHdfsUtils.delete_hdfs_path(hdfsPath)
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+ data3.repartition(1).saveAsTextFile(hdfsPath, classOf[GzipCodec])
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+ } else {
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+ println("路径不合法,无法写入:" + hdfsPath)
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+ }
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+ }
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+}
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