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package mobvista.dmp.datasource.age_gender
import java.net.URI
import mobvista.dmp.common.CommonSparkJob
import mobvista.dmp.util.{DateUtil, MRUtils}
import org.apache.commons.cli.Options
import org.apache.commons.lang3.StringUtils
import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.spark.sql.{SaveMode, SparkSession}
/**
* @author wangjf
*/
class CalcDeviceGender extends CommonSparkJob with Serializable {
override protected def run(args: Array[String]): Int = {
val commandLine = commParser.parse(options, args)
if (!checkMustOption(commandLine)) {
printUsage(options)
return -1
} else {
printOptions(commandLine)
}
val date = commandLine.getOptionValue("date")
val merge_input = commandLine.getOptionValue("merge_input")
val dict_input = commandLine.getOptionValue("dict_input")
val output = commandLine.getOptionValue("output")
val parallelism = commandLine.getOptionValue("parallelism")
val spark = SparkSession.builder()
.appName("CalcDeviceGender")
.config("spark.rdd.compress", "true")
.config("spark.io.compression.codec", "snappy")
.config("spark.sql.orc.filterPushdown", "true")
.config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
.config("spark.kryo.registrationRequired", "false")
.config("spark.kryo.registrator", "mobvista.dmp.datasource.age_gender.MyRegisterKryo")
.enableHiveSupport()
.getOrCreate()
val sc = spark.sparkContext
FileSystem.get(new URI(s"s3://mob-emr-test"), spark.sparkContext.hadoopConfiguration).delete(new Path(output), true)
try {
val packageMap = sc.textFile(dict_input).map(_.split("\t"))
.map(r =>
(r(0), MRUtils.JOINER.join(r(1).substring(1), r(2)))
).collectAsMap()
val bPackageMap = sc.broadcast(packageMap)
val update_date = DateUtil.format(DateUtil.parse(date, "yyyyMMdd"), "yyyy-MM-dd")
val df = spark.read.schema(Constant.merge_schema).orc(merge_input)
.where(s"update_date = '$update_date'")
.rdd
.mapPartitions(Logic.calcDeviceGenderLogicJson(_, bPackageMap))
.filter(d => {
StringUtils.isNotBlank(d.gender)
})
/*
df.repartition(parallelism.toInt)
.map(r => {
MRUtils.JOINER.join(r.device_id, r.device_type, r.package_names, r.source, r.gender, r.ratio, r.tag)
}).saveAsTextFile(output, classOf[GzipCodec])
*/
import spark.implicits._
df.coalesce(numPartitions = parallelism.toInt, shuffle = true)
.toDF
.write.mode(SaveMode.Overwrite)
.option("orc.compress", "zlib")
.orc(output)
} finally {
sc.stop()
spark.stop()
}
0
}
override protected def buildOptions(): Options = {
val options = new Options
options.addOption("date", true, "[must] date")
options.addOption("merge_input", true, "[must] merge_input")
options.addOption("dict_input", true, "[must] dict_input")
options.addOption("output", true, "[must] output")
options.addOption("parallelism", true, "[must] parallelism")
options
}
}
object CalcDeviceGender {
def main(args: Array[String]): Unit = {
new CalcDeviceGender().run(args)
}
}