DatatoryJob.scala 5.87 KB
Newer Older
wang-jinfeng committed
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179
package mobvista.dmp.datasource.datatory

import mobvista.dmp.common.CommonSparkJob
import org.apache.commons.cli.{BasicParser, Options}
import org.apache.spark.sql.{Dataset, Row, SaveMode, SparkSession}

/**
  * @package: mobvista.dmp.datasource.datatory
  * @author: wangjf
  * @date: 2019/4/3
  * @time: 下午2:03
  * @email: jinfeng.wang@mobvista.com
  * @phone: 152-1062-7698
  */
class DatatoryJob extends CommonSparkJob with java.io.Serializable {
  def commandOptions(): Options = {
    val options = new Options()
    options.addOption("coalesce", true, "coalesce")
    options.addOption("json", true, "json")
    options.addOption("tag", true, "tag")

    options
  }

  override protected def run(args: Array[String]): Int = {
    val parser = new BasicParser()
    val options = commandOptions()
    val commandLine = parser.parse(options, args)
    val coalesce = commandLine.getOptionValue("coalesce")
    val json = commandLine.getOptionValue("json")
    val tag = commandLine.getOptionValue("tag")

    val spark = SparkSession
      .builder()
      .appName("DatatoryJob")
      .config("spark.rdd.compress", "true")
      .config("spark.shuffle.compress", "true")
      .config("spark.sql.orc.filterPushdown", "true")
      .config("spark.io.compression.codec", "lz4")
      .config("spark.io.compression.lz4.blockSize", "64k")
      .config("spark.sql.autoBroadcastJoinThreshold", "209715200")
      .config("spark.sql.warehouse.dir", "s3://mob-emr-test/spark-warehouse")
      .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
      .enableHiveSupport()
      .getOrCreate()

    val sc = spark.sparkContext
    try {

      val code_sql = Constant.ods2new_sql
      val bMap = sc.broadcast(spark.sql(code_sql).rdd.map(r => {
        (r.getAs("tag_id").toString, (r.getAs("new_first_id").toString, r.getAs("new_second_id").toString))
      }).collectAsMap())

      val sql =
        """
          |SHOW PARTITIONS dwh.ods_dmp_user_info_all
        """.stripMargin
      val partDF = spark.sql(sql)
      val date = partDF.orderBy(partDF("partition").desc).take(1)(0).getString(0).split("=")(1)


      /*
      spark.udf.register("filterByPackage", Constant.filterByPackage _)
      spark.udf.register("filterByCountry", Constant.filterByCountry _)

      val packageFilterEntity: PackageFilterEntity = Constant.parseJsonString(json)

      val jobId = packageFilterEntity.jobId

      val sdf1 = new SimpleDateFormat("yyyy-MM-dd")
      val sdf2 = new SimpleDateFormat("yyyyMMdd")
      val updateDate = sdf1.format(sdf2.parse(packageFilterEntity.start))

      var sql = Constant.filter_sql.replace("@date", packageFilterEntity.end).replace("@update_date", updateDate)

      if (StringUtils.isNotBlank(packageFilterEntity.countryCode)) {
        sql = sql + s" AND filterByCountry(country,'${packageFilterEntity.countryCode}')"
      }

      if (StringUtils.isNotBlank(packageFilterEntity.packageList)) {
        sql = sql + s" AND filterByPackage(install,'${packageFilterEntity.packageList}')"
      }

      val baseDF = spark.sql(sql).rdd
        .map(r => {
          PackageUserInfoEntity(r.getAs("dev_id"), r.getAs("install"), r.getAs("interest"), r.getAs("country"), r.getAs("age"), r.getAs("gender"))
        }).persist(StorageLevel.MEMORY_AND_DISK_SER)
        */

      val base = Constant.processQuery(date, tag, json.replaceAll("&@", " "), spark)


      /*
      val top: Integer = if (packageFilterEntity.top == null) {
        20
      } else {
        packageFilterEntity.top
      }

      val all = baseDF.count()
      val allDF = sc.parallelize(Seq(Result(jobId, "all", "all", all.toInt)))

      val packageDF = baseDF.mapPartitions(Constant.commonPartition(_, bMap, "package"))
        .reduceByKey(_ + _)
        .repartition(coalesce.toInt)
        .sortBy(_._2, false)
        .map(r => {
          Result(jobId, "package", r._1, r._2)
        })

      val packageRDD = sc.parallelize(packageDF.take(top))

      val interestDF = baseDF.mapPartitions(Constant.commonPartition(_, bMap, "interest"))
        .reduceByKey(_ + _)
        .repartition(coalesce.toInt)
        .sortByKey()
        .map(r => {
          Result(jobId, "interest", r._1, r._2)
        })

      val countryDF = baseDF.mapPartitions(Constant.commonPartition(_, bMap, "country"))
        .reduceByKey(_ + _)
        .repartition(coalesce.toInt)
        .sortBy(_._2, false)
        .map(r => {
          Result(jobId, "country", r._1, r._2)
        })

      val countryRDD = sc.parallelize(countryDF.take(top))

      val ageDF = baseDF.mapPartitions(Constant.commonPartition(_, bMap, "age"))
        .reduceByKey(_ + _)
        .repartition(coalesce.toInt)
        .sortByKey()
        .map(r => {
          Result(jobId, "age", r._1, r._2)
        })

      val genderDF = baseDF.mapPartitions(Constant.commonPartition(_, bMap, "gender"))
        .reduceByKey(_ + _)
        .repartition(coalesce.toInt)
        .sortByKey()
        .map(r => {
          Result(jobId, "gender", r._1, r._2)
        })

      import spark.implicits._
      val df = allDF.union(packageRDD).union(interestDF).union(countryRDD).union(ageDF).union(genderDF)
        .toDF
        .repartition(1)
        */
      val df: Dataset[Row] = Constant.processBase(base._1, base._2, base._3, base._4, spark, bMap)

      /*
      val prop = new java.util.Properties
      prop.setProperty("driver", "com.mysql.jdbc.Driver")
      prop.setProperty("user", "apptag_rw")
      prop.setProperty("password", "7gyLEVtkER3u8c9")
      prop.setProperty("characterEncoding", "utf8")

      df.write.mode(SaveMode.Append).jdbc("jdbc:mysql://dataplatform-app-tag.c5yzcdreb1xr.us-east-1.rds.amazonaws.com:3306/datatory", "result", prop)
      */
      Constant.writeMySQL(df, "result", SaveMode.Append)

    } finally {
      if (spark != null) {
        spark.stop()
      }
    }
    0
  }
}

object DatatoryJob {
  def main(args: Array[String]): Unit = {
    new DatatoryJob().run(args)
  }
}