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#头条创作挑战赛#

DM层主要是报表数据，针对实时业务将DM层设置在Clickhou中，在此业务中DM层主要存储的是通过Flink读取Kafka “KAFKA-DWS-BROWSE-LOG-WIDE-TOPIC” topic中的数据进行设置窗口分析，每隔10s设置滚动窗口统计该窗口内访问商品及商品一级、二级分类分析结果，实时写入到Clickhou中。

具体代码参照“ProcessBrowLogInfoToDM.scala”，大体代码逻辑如下：

object ProcessBrowLogInfoToDM { def main(args: Array[String]): Unit = { //1.准备环境 val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment val tblEnv: StreamTableEnvironment = StreamTableEnvironment.create(env) env.enableCheckpointing(5000) import org.apache.flink.streaming.api.scala._ /** * 2.创建 Kafka Connector,连接消费Kafka dwd中数据 * */ tblEnv.executeSql( """ |create table kafka_dws_ur_login_wide_tbl ( | ur_id string, | product_name string, | first_category_name string, | cond_category_name string, | obtain_points string |) with ( | 'connector' = 'kafka', | 'topic' = 'KAFKA-DWS-BROWSE-LOG-WIDE-TOPIC', | 'properties.bootstrap.rvers'='node1:9092,node2:9092,node3:9092', | 'scan.startup.mode'='earliest-offt', --也可以指定 earliest-offt 、latest-offt | 'properties.group.id' = 'my-group-id', | 'format' = 'json' |) """.stripMargin) /** * 3.实时统计每个用户最近10s浏览的商品次数和商品一级、二级种类次数，存入到Clickhou */ val dwsTbl:Table = tblEnv.sqlQuery( """ | lect ur_id,product_name,first_category_name,cond_category_name from kafka_dws_ur_login_wide_tbl """.stripMargin) //4.将Row 类型数据转换成对象类型操作 val browDS: DataStream[BrowLogWideInfo] = tblEnv.toAppendStream[Row](dwsTbl) .map(row => { val ur_id: String = row.getField(0).toString val product_name: String = row.getField(1).toString val first_category_name: String = row.getField(2).toString val cond_category_name: String = row.getField(3).toString BrowLogWideInfo(null, ur_id, null, product_name, null, null, first_category_name, cond_category_name, null) }) val dwsDS: DataStream[ProductVisitInfo] = browDS.keyBy(info => { info.first_category_name + "-" + info.cond_category_name + "-" + info.product_name }) .timeWindow(Time.conds(10)) .process(new ProcessWindowFunction[BrowLogWideInfo, ProductVisitInfo, String, TimeWindow] { override def process(key: String, context: Context, elements: Iterable[BrowLogWideInfo], out: Collector[ProductVisitInfo]): Unit = { val currentDt: String = DateUtil.getDateYYYYMMDD(context.window.getStart.toString) val startTime: String = DateUtil.getDateYYYYMMDDHHMMSS(context.window.getStart.toString) val endTime: String = DateUtil.getDateYYYYMMDDHHMMSS(context.window.getEnd.toString) val arr: Array[String] = key.split("-") val firstCatName: String = arr(0) val condCatName: String = arr(1) val productName: String = arr(2) val cnt: Int = elements.toList.size out.collect(ProductVisitInfo(currentDt, startTime, endTime, firstCatName, condCatName, productName, cnt)) } }) /** * 5.将以上结果写入到Clickhou表 dm_product_visit_info 表中 * create table dm_product_visit_info( * current_dt String, * window_start String, * window_end String, * first_cat String, * cond_cat String, * product String, * product_cnt UInt32 * ) engine = MergeTree() order by current_dt * */ //准备向ClickHou中插入数据的sql val inrtIntoCkSql = "inrt into dm_product_visit_info (current_dt,window_start,window_end,first_cat,cond_cat,product,product_cnt) values (?,?,?,?,?,?,?)" val ckSink: SinkFunction[ProductVisitInfo] = MyClickHouUtil.clickhouSink[ProductVisitInfo](inrtIntoCkSql,new JdbcStatementBuilder[ProductVisitInfo] { override def accept(pst: PreparedStatement, productVisitInfo: ProductVisitInfo): Unit = { pst.tString(1,productVisitInfo.currentDt) pst.tString(2,productVisitInfo.windowStart) pst.tString(3,productVisitInfo.windowEnd) pst.tString(4,productVisitInfo.firstCat) pst.tString(5,productVisitInfo.condCat) pst.tString(6,productVisitInfo.product) pst.tLong(7,productVisitInfo.productCnt) } }) //针对数据加入sink dwsDS.addSink(ckSink) env.execute() }}二、创建Clickhou-DM层表

代码在执行之前需要在Clickhou中创建对应的DM层商品浏览信息表dm_product_visit_info，clickhou建表语句如下：

#node1节点启动clickhou[root@node1 bin]# rvice clickhou-rver start#node1节点进入clickhou[root@node1 bin]# clickhou-client -m#node1节点创建clickhou-DM层表create table dm_product_visit_info( current_dt String, window_start String, window_end String, first_cat String, cond_cat String, product String, product_cnt UInt32) engine = MergeTree() order by current_dt;三、代码测试

以上代码编写完成后，代码执行测试步骤如下：

代码中Kafka Connector中属性“scan.startup.mode”设置为“earliest-offt”，从头开始消费数据。

这里也可以不设置从头开始消费Kafka数据，而是直接启动向日志采集接口模拟生产日志代码“RTMockUrLogData.java”，需要启动日志采集接口及Flume。

以上代码执行后在，在Clickhou-DM层中表“dm_product_visit_info”中查看对应数据结果如下：