湖仓一体电商项目（八）：业务实现之编写写入ODS层业务代码

Lansonli • 2023-01-02 • 云技术社区 • 269 阅读

​业务实现之编写写入ODS层业务代码

一、代码逻辑和架构图

ODS层在湖仓一体架构中主要是存储原始数据，这里主要是读取Kafka “KAFKA-DB-BUSSINESS-DATA”topic中的数据实现如下两个方面功能:

  • 将MySQL业务数据原封不动的存储在Iceberg-ODS层中方便项目临时业务需求使用。
  • 将事实数据和维度数据进行分离，分别存储Kafka对应的topic中

以上两个方面中第一个方面需要再Hive中预先创建对应的Iceberg表，才能写入，第二个方面不好分辨topic“KAFKA-DB-BUSSINESS-DATA”中哪些binlog数据是事实数据哪些binlog是维度数据，所以这里我们在mysql 配置表“lakehousedb.dim_tbl_config_info”中写入表信息，这样通过Flink获取此表维度表信息进行广播与Kafka实时流进行关联将事实数据和维度数据进行区分。

二、​​​​​​​代码编写

数据写入ODS层代码是“ProduceKafkaDBDataToODS.scala”，主要代码逻辑实现如下：

object ProduceKafkaDBDataToODS {
  private val mysqlUrl: String = ConfigUtil.MYSQL_URL
  private val mysqlUser: String = ConfigUtil.MYSQL_USER
  private val mysqlPassWord: String = ConfigUtil.MYSQL_PASSWORD
  private val kafkaBrokers: String = ConfigUtil.KAFKA_BROKERS
  private val kafkaDimTopic: String = ConfigUtil.KAFKA_DIM_TOPIC
  private val kafkaOdsTopic: String = ConfigUtil.KAFKA_ODS_TOPIC
  private val kafkaDwdUserLogTopic: String = ConfigUtil.KAFKA_DWD_USERLOG_TOPIC

  def main(args: Array[String]): Unit = {
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    val tblEnv: StreamTableEnvironment = StreamTableEnvironment.create(env)

    import org.apache.flink.streaming.api.scala._

    env.enableCheckpointing(5000)

    /**
      * 1.需要预先创建 Catalog
      * 创建Catalog,创建表需要在Hive中提前创建好，不在代码中创建，因为在Flink中创建iceberg表不支持create table if not exists ...语法
      */
    tblEnv.executeSql(
      """
        |create catalog hadoop_iceberg with (
        | 'type'='iceberg',
        | 'catalog-type'='hadoop',
        | 'warehouse'='hdfs://mycluster/lakehousedata'
        |)
      """.stripMargin)

    /**
      * 2.创建 Kafka Connector,连接消费Kafka中数据
      * 注意：1).关键字要使用 " 飘"符号引起来 2).对于json对象使用 map < String,String>来接收
      */
    tblEnv.executeSql(
      """
        |create table kafka_db_bussiness_tbl(
        |   database string,
        |   `table` string,
        |   type string,
        |   ts string,
        |   xid string,
        |   `commit` string,
        |   data map<string,string>
        |) with (
        | 'connector' = 'kafka',
        | 'topic' = 'KAFKA-DB-BUSSINESS-DATA',
        | 'properties.bootstrap.servers'='node1:9092,node2:9092,node3:9092',
        | 'scan.startup.mode'='latest-offset', --也可以指定 earliest-offset 、latest-offset
        | 'properties.group.id' = 'my-group-id',
        | 'format' = 'json'
        |)
      """.stripMargin)

    /**
      * 3.将不同的业务库数据存入各自的Iceberg表
      */
    tblEnv.executeSql(
      """
        |insert into hadoop_iceberg.icebergdb.ODS_MEMBER_INFO
        |select
        |   data['id'] as id ,
        |   data['user_id'] as user_id,
        |   data['member_growth_score'] as member_growth_score,
        |   data['member_level'] as member_level,
        |   data['balance'] as balance,
        |   data['gmt_create'] as gmt_create,
        |   data['gmt_modified'] as  gmt_modified
        | from kafka_db_bussiness_tbl where `table` = 'mc_member_info'
      """.stripMargin)


    tblEnv.executeSql(
      """
        |insert into hadoop_iceberg.icebergdb.ODS_MEMBER_ADDRESS
        |select
        |   data['id'] as id ,
        |   data['user_id'] as user_id,
        |   data['province'] as province,
        |   data['city'] as city,
        |   data['area'] as area,
        |   data['address'] as address,
        |   data['log'] as log,
        |   data['lat'] as lat,
        |   data['phone_number'] as phone_number,
        |   data['consignee_name'] as consignee_name,
        |   data['gmt_create'] as gmt_create,
        |   data['gmt_modified'] as  gmt_modified
        | from kafka_db_bussiness_tbl where `table` = 'mc_member_address'
      """.stripMargin)

    tblEnv.executeSql(
      """
        |insert into hadoop_iceberg.icebergdb.ODS_USER_LOGIN
        |select
        |   data['id'] as id ,
        |   data['user_id'] as user_id,
        |   data['ip'] as ip,
        |   data['login_tm'] as login_tm,
        |   data['logout_tm'] as logout_tm
        | from kafka_db_bussiness_tbl where `table` = 'mc_user_login'
      """.stripMargin)

    //4.读取 Kafka 中的数据，将维度数据另外存储到 Kafka 中
    val kafkaTbl: Table = tblEnv.sqlQuery("select database,`table`,type,ts,xid,`commit`,data from kafka_db_bussiness_tbl")

    //5.将kafkaTbl Table 转换成DStream 与MySql中的数据
    val kafkaDS: DataStream[Row] = tblEnv.toAppendStream[Row](kafkaTbl)

    //6.设置mapState,用于广播流
    val mapStateDescriptor = new MapStateDescriptor[String,JSONObject]("mapStateDescriptor",classOf[String],classOf[JSONObject])

    //7.从MySQL中获取配置信息，并广播
    val bcConfigDs: BroadcastStream[JSONObject] = env.addSource(MySQLUtil.getMySQLData(mysqlUrl,mysqlUser,mysqlPassWord)).broadcast(mapStateDescriptor)

    //8.设置维度数据侧输出流标记
    val dimDataTag = new OutputTag[String]("dim_data")

    //9.只监控mysql 数据库lakehousedb 中的数据，其他库binlog不监控，连接两个流进行处理
    val factMainDs: DataStream[String] = kafkaDS.filter(row=>{"lakehousedb".equals(row.getField(0).toString)}).connect(bcConfigDs).process(new BroadcastProcessFunction[Row, JSONObject, String] {
      override def processElement(row: Row, ctx: BroadcastProcessFunction[Row, JSONObject, String]#ReadOnlyContext, out: Collector[String]): Unit = {
        //最后返回给Kafka 事实数据的json对象
        val returnJsonObj = new JSONObject()
        //获取广播状态
        val robcs: ReadOnlyBroadcastState[String, JSONObject] = ctx.getBroadcastState(mapStateDescriptor)
        //解析事件流数据
        val nObject: JSONObject = CommonUtil.rowToJsonObj(row)
        //获取当前时间流来自的库和表 ，样例数据如下
        //lackhousedb,pc_product,insert,1646659263,21603,null,{gmt_create=1645493074001, category_id=220, product_name=黄金, product_id=npfSpLHB8U}
        val dbName: String = nObject.getString("database")
        val tableName: String = nObject.getString("table")
        val key = dbName + ":" + tableName
        if (robcs.contains(key)) {
          //维度数据
          val jsonValue: JSONObject = robcs.get(key)
          //维度数据，将对应的 jsonValue中的信息设置到流事件中
          nObject.put("tbl_name", jsonValue.getString("tbl_name"))
          nObject.put("tbl_db", jsonValue.getString("tbl_db"))
          nObject.put("pk_col", jsonValue.getString("pk_col"))
          nObject.put("cols", jsonValue.getString("cols"))
          nObject.put("phoenix_tbl_name", jsonValue.getString("phoenix_tbl_name"))
          ctx.output(dimDataTag, nObject.toString)
        }else{
          //事实数据，加入iceberg 表名写入Kafka ODS-DB-TOPIC topic中
          if("mc_user_login".equals(tableName)){
            returnJsonObj.put("iceberg_ods_tbl_name","ODS_USER_LOGIN")
            returnJsonObj.put("kafka_dwd_topic",kafkaDwdUserLogTopic)
            returnJsonObj.put("data",nObject.toString)
          }
          out.collect(returnJsonObj.toJSONString)
        }
      }

      override def processBroadcastElement(jsonObject: JSONObject, ctx: BroadcastProcessFunction[Row, JSONObject, String]#Context, out: Collector[String]): Unit = {
        val tblDB: String = jsonObject.getString("tbl_db")
        val tblName: String = jsonObject.getString("tbl_name")
        //向状态中更新数据
        val bcs: BroadcastState[String, JSONObject] = ctx.getBroadcastState(mapStateDescriptor)
        bcs.put(tblDB + ":" + tblName, jsonObject)
        println("广播数据流设置完成...")
      }
    })


    //10.结果写入到Kafka -  dim_data_topic topic中
    val props = new Properties()
    props.setProperty("bootstrap.servers",kafkaBrokers)
    factMainDs.addSink(new FlinkKafkaProducer[String](kafkaOdsTopic,new KafkaSerializationSchema[String] {
      override def serialize(element: String, timestamp: java.lang.Long): ProducerRecord[Array[Byte], Array[Byte]] = {
        new ProducerRecord[Array[Byte],Array[Byte]](kafkaOdsTopic,null,element.getBytes())
      }
    },props,FlinkKafkaProducer.Semantic.AT_LEAST_ONCE))//暂时使用at_least_once语义，exactly_once语义有些bug问题

    factMainDs.getSideOutput(dimDataTag).addSink(new FlinkKafkaProducer[String](kafkaDimTopic,new KafkaSerializationSchema[String] {
      override def serialize(element: String, timestamp: java.lang.Long): ProducerRecord[Array[Byte], Array[Byte]] = {
        new ProducerRecord[Array[Byte],Array[Byte]](kafkaDimTopic,null,element.getBytes())
      }
    },props,FlinkKafkaProducer.Semantic.AT_LEAST_ONCE))//暂时使用at_least_once语义，exactly_once语义有些bug问题

    env.execute()

  }

}

三、​​​​​​​​​​​​​​创建Iceberg-ODS层表

代码在执行之前需要在Hive中预先创建对应的Iceberg表，创建Icebreg表方式如下：

1、在Hive中添加Iceberg表格式需要的包

启动HDFS集群，node1启动Hive metastore服务，在Hive客户端启动Hive添加Iceberg依赖包：

#node1节点启动Hive metastore服务
[root@node1 ~]# hive --service metastore &

#在hive客户端node3节点加载两个jar包
add jar /software/hive-3.1.2/lib/iceberg-hive-runtime-0.12.1.jar;
add jar /software/hive-3.1.2/lib/libfb303-0.9.3.jar;

2、创建Iceberg表

这里创建Iceberg表有“ODS_MEMBER_INFO”、“ODS_MEMBER_ADDRESS”、“ODS_USER_LOGIN”，创建语句如下：

#在Hive客户端执行以下建表语句
CREATE TABLE ODS_MEMBER_INFO  (
id string,
user_id string,
member_growth_score string,
member_level string,
balance string,
gmt_create string,
gmt_modified string
)STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler' 
LOCATION 'hdfs://mycluster/lakehousedata/icebergdb/ODS_MEMBER_INFO/' 
TBLPROPERTIES ('iceberg.catalog'='location_based_table',
'write.metadata.delete-after-commit.enabled'= 'true',
'write.metadata.previous-versions-max' = '3'
);


CREATE TABLE ODS_MEMBER_ADDRESS  (
id string,
user_id string,
province string,
city string,
area string,
address string,
log string,
lat string,
phone_number string,
consignee_name string,
gmt_create string,
gmt_modified string
)STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler' 
LOCATION 'hdfs://mycluster/lakehousedata/icebergdb/ODS_MEMBER_ADDRESS/' 
TBLPROPERTIES ('iceberg.catalog'='location_based_table',
'write.metadata.delete-after-commit.enabled'= 'true',
'write.metadata.previous-versions-max' = '3'
);

CREATE TABLE ODS_USER_LOGIN (
id string,
user_id string,
ip string,
login_tm string,
logout_tm string
)STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler' 
LOCATION 'hdfs://mycluster/lakehousedata/icebergdb/ODS_USER_LOGIN/' 
TBLPROPERTIES ('iceberg.catalog'='location_based_table',
'write.metadata.delete-after-commit.enabled'= 'true',
'write.metadata.previous-versions-max' = '3'
);

以上语句在Hive客户端执行完成之后，在HDFS中可以看到对应的Iceberg数据目录：

四、代码测试

以上代码编写完成后，代码执行测试步骤如下：

1、在Kafka中创建对应的topic

#在Kafka 中创建 KAFKA-ODS-TOPIC topic
./kafka-topics.sh --zookeeper node3:2181,node4:2181,node5:2181 --create --topic KAFKA-ODS-TOPIC --partitions 3 --replication-factor 3

#在Kafka 中创建 KAFKA-DIM-TOPIC topic
./kafka-topics.sh --zookeeper node3:2181,node4:2181,node5:2181 --create --topic KAFKA-DIM-TOPIC --partitions 3 --replication-factor 3

#监控以上两个topic数据
[root@node1 bin]# ./kafka-console-consumer.sh --bootstrap-server node1:9092,node2:9092,node3:9092 --topic KAFKA-ODS-TOPIC

[root@node1 bin]# ./kafka-console-consumer.sh --bootstrap-server node1:9092,node2:9092,node3:9092 --topic KAFKA-DIM-TOPIC

2、将代码中消费Kafka数据改成从头开始消费

代码中Kafka Connector中属性“scan.startup.mode”设置为“earliest-offset”，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，而是直接启动实时向MySQL表中写入数据代码“RTMockDBData.java”代码，实时向MySQL对应的表中写入数据，这里需要启动maxwell监控数据，代码才能实时监控到写入MySQL的业务数据。

3、执行代码，查看对应topic中的结果

以上代码执行后在，在对应的Kafka “KAFKA-DIM-TOPIC”和“KAFKA-ODS-TOPIC”中都有对应的数据。在Iceberg-ODS层中对应的表中也有数据。

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湖仓一体电商项目（八）：业务实现之编写写入ODS层业务代码
ODS层在湖仓一体架构中主要是存储原始数据，这里主要是读取Kafka “KAFKA-DB-BUSSINESS-DATA”topic中的数据实现如下两个方面功能:
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