湖仓一体电商项目（二十）：业务实现之编写写入 DM 层业务代码

2023-02-12
广东
本文字数：2825 字
阅读完需：约 9 分钟

业务实现之编写写入 DM 层业务代码

DM 层主要是报表数据，针对实时业务将 DM 层设置在 Clickhouse 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 Clickhouse 中。

一、代码编写

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

object ProcessBrowseLogInfoToDM {  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_user_login_wide_tbl (        |   user_id string,        |   product_name string,        |   first_category_name string,        |   second_category_name string,        |   obtain_points string        |) with (        | 'connector' = 'kafka',        | 'topic' = 'KAFKA-DWS-BROWSE-LOG-WIDE-TOPIC',        | 'properties.bootstrap.servers'='node1:9092,node2:9092,node3:9092',        | 'scan.startup.mode'='earliest-offset', --也可以指定 earliest-offset 、latest-offset        | '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Clickhouse      */
    val dwsTbl:Table = tblEnv.sqlQuery(      """        | select user_id,product_name,first_category_name,second_category_name from kafka_dws_user_login_wide_tbl      """.stripMargin)
    //4.将Row 类型数据转换成对象类型操作    val browseDS: DataStream[BrowseLogWideInfo] = tblEnv.toAppendStream[Row](dwsTbl)      .map(row => {        val user_id: String = row.getField(0).toString        val product_name: String = row.getField(1).toString        val first_category_name: String = row.getField(2).toString        val second_category_name: String = row.getField(3).toString        BrowseLogWideInfo(null, user_id, null, product_name, null, null, first_category_name, second_category_name, null)      })

    val dwsDS: DataStream[ProductVisitInfo] = browseDS.keyBy(info => {      info.first_category_name + "-" + info.second_category_name + "-" + info.product_name    })      .timeWindow(Time.seconds(10))      .process(new ProcessWindowFunction[BrowseLogWideInfo, ProductVisitInfo, String, TimeWindow] {
        override def process(key: String, context: Context, elements: Iterable[BrowseLogWideInfo], 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 secondCatName: String = arr(1)          val productName: String = arr(2)          val cnt: Int = elements.toList.size          out.collect(ProductVisitInfo(currentDt, startTime, endTime, firstCatName, secondCatName, productName, cnt))        }
      })
    /**      * 5.将以上结果写入到Clickhouse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,      *    second_cat String,      *    product String,      *    product_cnt UInt32      *  ) engine = MergeTree() order by current_dt      *      */
    //准备向ClickHouse中插入数据的sql    val insertIntoCkSql = "insert into dm_product_visit_info (current_dt,window_start,window_end,first_cat,second_cat,product,product_cnt) values (?,?,?,?,?,?,?)"

    val ckSink: SinkFunction[ProductVisitInfo] = MyClickHouseUtil.clickhouseSink[ProductVisitInfo](insertIntoCkSql,new JdbcStatementBuilder[ProductVisitInfo] {      override def accept(pst: PreparedStatement, productVisitInfo: ProductVisitInfo): Unit = {        pst.setString(1,productVisitInfo.currentDt)        pst.setString(2,productVisitInfo.windowStart)        pst.setString(3,productVisitInfo.windowEnd)        pst.setString(4,productVisitInfo.firstCat)        pst.setString(5,productVisitInfo.secondCat)        pst.setString(6,productVisitInfo.product)        pst.setLong(7,productVisitInfo.productCnt)
      }    })
    //针对数据加入sink    dwsDS.addSink(ckSink)
    env.execute()
  }}

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二、创建 Clickhouse-DM 层表

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

#node1节点启动clickhouse[root@node1 bin]# service clickhouse-server start
#node1节点进入clickhouse[root@node1 bin]# clickhouse-client -m
#node1节点创建clickhouse-DM层表create table dm_product_visit_info( current_dt String, window_start String, window_end String, first_cat String, second_cat String, product String, product_cnt UInt32) engine = MergeTree() order by current_dt;