大数据入门：Spark+Kudu的广告业务项目实战笔记(五)

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Spark+Kudu的广告业务项目实战系列：

1.统计需求

本章主要实现需求四：APP统计。需求如下：

2.代码编写

入口搭好：

AppStatProcessor.process(spark)

先看一下第一步的运行情况：

package com.imooc.bigdata.cp08.business

import com.imooc.bigdata.cp08.`trait`.DataProcess
import com.imooc.bigdata.cp08.utils.SQLUtils
import org.apache.spark.sql.SparkSession

object AppStatProcessor extends DataProcess{
override def process(spark: SparkSession): Unit = {
val sourceTableName = "ods"
val masterAddresses = "hadoop000"

val odsDF = spark.read.format("org.apache.kudu.spark.kudu")
      .option("kudu.table",sourceTableName)
      .option("kudu.master",masterAddresses)
      .load()

    odsDF.createOrReplaceTempView("ods")
val resultTmp = spark.sql(SQLUtils.APP_SQL_STEP1)
    resultTmp.show()

  }
}

其中SQL代码如下：

  lazy val APP_SQL_STEP1 = "select appid,appname, " +
    "sum(case when requestmode=1 and processnode >=1 then 1 else 0 end) origin_request," +
    "sum(case when requestmode=1 and processnode >=2 then 1 else 0 end) valid_request," +
    "sum(case when requestmode=1 and processnode =3 then 1 else 0 end) ad_request," +
    "sum(case when adplatformproviderid>=100000 and iseffective=1 and isbilling=1 and isbid=1 and adorderid!=0 then 1 else 0 end) bid_cnt," +
    "sum(case when adplatformproviderid>=100000 and iseffective=1 and isbilling=1 and iswin=1 then 1 else 0 end) bid_success_cnt," +
    "sum(case when requestmode=2 and iseffective=1 then 1 else 0 end) ad_display_cnt," +
    "sum(case when requestmode=3 and processnode=1 then 1 else 0 end) ad_click_cnt," +
    "sum(case when requestmode=2 and iseffective=1 and isbilling=1 then 1 else 0 end) medium_display_cnt," +
    "sum(case when requestmode=3 and iseffective=1 and isbilling=1 then 1 else 0 end) medium_click_cnt," +
    "sum(case when adplatformproviderid>=100000 and iseffective=1 and isbilling=1 and iswin=1 and adorderid>20000  then 1*winprice/1000 else 0 end) ad_consumption," +
    "sum(case when adplatformproviderid>=100000 and iseffective=1 and isbilling=1 and iswin=1 and adorderid>20000  then 1*adpayment/1000 else 0 end) ad_cost " +
    "from ods group by appid,appname"

结果：

没毛病就往下跑第二个SQL，具体做法和需求三区别不大：

    resultTmp.createOrReplaceTempView("app_tmp")
val result = spark.sql(SQLUtils.APP_SQL_STEP2)
    result.show()

第二个SQL如下：

 lazy val APP_SQL_STEP2 = "select appid,appname, " +
    "origin_request," +
    "valid_request," +
    "ad_request," +
    "bid_cnt," +
    "bid_success_cnt," +
    "bid_success_cnt/bid_cnt bid_success_rate," +
    "ad_display_cnt," +
    "ad_click_cnt," +
    "ad_click_cnt/ad_display_cnt ad_click_rate," +
    "ad_consumption," +
    "ad_cost from app_tmp " +
    "where bid_cnt!=0 and ad_display_cnt!=0"

然后run一下，都可以就可以写入Kudu了。

3.落地Kudu

val sinkTableName = "app_stat"
val partitionId = "appid"
val schema = SchemaUtils.APPSchema

    KuduUtils.sink(result,sinkTableName,masterAddresses,schema,partitionId)
    spark.read.format("org.apache.kudu.spark.kudu")
      .option("kudu.master",masterAddresses)
      .option("kudu.table",sinkTableName)
      .load().show()

schema：

  lazy val APPSchema: Schema = {
    val columns = List(
new ColumnSchemaBuilder("appid", Type.STRING).nullable(false).key(true).build(),
new ColumnSchemaBuilder("appname", Type.STRING).nullable(false).key(true).build(),
new ColumnSchemaBuilder("origin_request", Type.INT64).nullable(false).build(),
new ColumnSchemaBuilder("valid_request", Type.INT64).nullable(false).build(),
new ColumnSchemaBuilder("ad_request", Type.INT64).nullable(false).build(),
new ColumnSchemaBuilder("bid_cnt", Type.INT64).nullable(false).build(),
new ColumnSchemaBuilder("bid_success_cnt", Type.INT64).nullable(false).build(),
new ColumnSchemaBuilder("bid_success_rate", Type.DOUBLE).nullable(false).build(),
new ColumnSchemaBuilder("ad_display_cnt", Type.INT64).nullable(false).build(),
new ColumnSchemaBuilder("ad_click_cnt", Type.INT64).nullable(false).build(),
new ColumnSchemaBuilder("ad_click_rate", Type.DOUBLE).nullable(false).build(),
new ColumnSchemaBuilder("ad_consumption", Type.DOUBLE).nullable(false).build(),
new ColumnSchemaBuilder("ad_cost", Type.DOUBLE).nullable(false).build()
    ).asJava
new Schema(columns)
  }

看下结果：

OK收工！

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本文为大数据技术与架构整理，原作者独家授权。未经原作者允许转载追究侵权责任。

本文编辑：冷眼丶

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