其他
大数据入门：Spark+Kudu的广告业务项目实战笔记(五)
点击上方蓝色字体，选择“设为星标”
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`.DataProcessimport com.imooc.bigdata.cp08.utils.SQLUtilsimport 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() ).asJavanew Schema(columns) }看下结果：
OK收工！
版权声明：
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
微信公众号｜import_bigdata
文章不错？点个【在看】吧！ 👇