//向Iceberg ods 层 ODS_PRODUCT_CATEGORY 表插入数据
tblEnv.executeSql(
  """
    |insert into hadoop_iceberg.icebergdb.ODS_PRODUCT_CATEGORY
    |select
    |   data['id'] as id ,
    |   data['p_id'] as p_id,
    |   data['name'] as name,
    |   data['pic_url'] as pic_url,
    |   data['gmt_create'] as gmt_create
    | from kafka_db_bussiness_tbl where `table` = 'pc_product_category'
  """.stripMargin)

//向Iceberg ods 层 ODS_PRODUCT_INFO 表插入数据
tblEnv.executeSql(
  """
    |insert into hadoop_iceberg.icebergdb.ODS_PRODUCT_INFO
    |select
    |   data['product_id'] as product_id ,
    |   data['category_id'] as category_id,
    |   data['product_name'] as product_name,
    |   data['gmt_create'] as gmt_create
    | from kafka_db_bussiness_tbl where `table` = 'pc_product'
  """.stripMargin)
处理用户日志的代码需要自己编写，代码中的业务逻辑主要是读取存储用户浏览日志数据topic “KAFKA-USER-LOG-DATA”中的数据，通过Flink代码处理将不同类型用户日志处理成json类型数据，将该json结果后续除了存储在Iceberg-ODS层对应的表之外还要将数据存储在Kafka topic “KAFKA-ODS-TOPIC” 中方便后续的业务处理。具体代码参照“ProduceKafkaLogDataToODS.scala”，主要代码逻辑如下：
object ProduceKafkaLogDataToODS 
  private val kafkaBrokers: String = ConfigUtil.KAFKA_BROKERS
  private val kafkaOdsTopic: String = ConfigUtil.KAFKA_ODS_TOPIC
  private val kafkaDwdBrowseLogTopic: String = ConfigUtil.KAFKA_DWD_BROWSELOG_TOPIC

  def main(args: Array[String]): Unit = 
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    val tblEnv: StreamTableEnvironment = StreamTableEnvironment.create(env)
    env.enableCheckpointing(5000)

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

    /**
      * 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)

    /**
      *  
      *    "logtype": "browselog",
      *    "data": 
      *    "browseProductCode": "eSHd1sFat9",
      *    "browseProductTpCode": "242",
      *    "userIp": "251.100.236.37",
      *    "obtainPoints": 32,
      *    "userId": "uid208600",
      *    "frontProductUrl": "https://f/dcjp/nVnE",
      *    "logTime": 1646980514321,
      *    "browseProductUrl": "https://kI/DXSNBeP/"
      *   
      * 
      */

    /**
      * 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_log_data_tbl(
        |   logtype string,
        |   data map<string,string>
        |) with (
        | 'connector' = 'kafka',
        | 'topic' = 'KAFKA-USER-LOG-DATA',
        | '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.将不同的业务库数据存入各自的Iceberg表
      */
    tblEnv.executeSql(
      """
        |insert into hadoop_iceberg.icebergdb.ODS_BROWSELOG
        |select
        |   data['logTime'] as log_time ,
        |   data['userId'] as user_id,
        |   data['userIp'] as user_ip,
        |   data['frontProductUrl'] as front_product_url,
        |   data['browseProductUrl'] as browse_product_url,
        |   data['browseProductTpCode'] as browse_product_tpcode,
        |   data['browseProductCode'] as browse_product_code,
        |   data['obtainPoints'] as  obtain_points
        | from kafka_log_data_tbl where `logtype` = 'browselog'
      """.stripMargin)


    //4.将用户所有日志数据组装成Json数据存入 kafka topic ODS-TOPIC 中
    //读取 Kafka 中的数据，将维度数据另外存储到 Kafka 中
    val kafkaLogTbl: Table = tblEnv.sqlQuery("select logtype,data from kafka_log_data_tbl")

    //将 kafkaLogTbl Table 转换成 DataStream 数据
    val userLogDS: DataStream[Row] = tblEnv.toAppendStream[Row](kafkaLogTbl)
    //将 userLogDS 数据转换成JSON 数据写出到 kafka topic ODS-TOPIC
    val odsSinkDS: DataStream[String] = userLogDS.map(row => 
      //最后返回给Kafka 日志数据的json对象
      val returnJsonObj = new JSONObject()
      val logType: String = row.getField(0).toString

      val data: String = row.getField(1).toString
      val nObject = new JSONObject()
      val arr: Array[String] = data.stripPrefix("").stripSuffix("").split(",")
      for (elem <- arr) 
        //有些数据 “data”中属性没有值，就没有“=”
        if (elem.contains("=") && elem.split("=").length == 2) 
          val split: Array[String] = elem.split("=")
          nObject.put(split(0).trim, split(1).trim)
         else 
          nObject.put(elem.stripSuffix("=").trim, "")
        
      

      if ("browselog".equals(logType)) 
        returnJsonObj.put("iceberg_ods_tbl_name", "ODS_BROWSELOG")
        returnJsonObj.put("kafka_dwd_topic",kafkaDwdBrowseLogTopic)
        returnJsonObj.put("data",nObject.toString)
       else 
        //其他日志，这里目前没有
      

      returnJsonObj.toJSONString
    )

    val props = new Properties()
    props.setProperty("bootstrap.servers",kafkaBrokers)

    odsSinkDS.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))

    env.execute()

二、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_PRODUCT_CATEGORY”、“ODS_PRODUCT_INFO”，创建语句如下：

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

CREATE TABLE ODS_PRODUCT_INFO (
product_id string,
category_id string,
product_name string,
gmt_create string
)STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler' 
LOCATION 'hdfs://mycluster/lakehousedata/icebergdb/ODS_PRODUCT_INFO/' 
TBLPROPERTIES ('iceberg.catalog'='location_based_table',
'write.metadata.delete-after-commit.enabled'= 'true',
'write.metadata.previous-versions-max' = '3'
);

CREATE TABLE ODS_BROWSELOG  (
 log_time string,
 user_id string,
 user_ip string,
 front_product_url string,
 browse_product_url string,
 browse_product_tpcode string,
 browse_product_code string,
 obtain_points string
)STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler' 
LOCATION 'hdfs://mycluster/lakehousedata/icebergdb/ODS_BROWSELOG/' 
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：

三、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-USER-LOG-DATA topic
./kafka-topics.sh --zookeeper node3:2181,node4:2181,node5:2181 --create --topic KAFKA-USER-LOG-DATA --partitions 3 --replication-factor 3

#在Kafka 中创建 KAFKA-ODS-TOPIC topic（第一个业务已创建可忽略）
./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（第一个业务已创建可忽略）
./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。

针对用户日志数据可以启动代码“RTMockUserLogData.java”，实时向日志采集接口写入数据。

3、启动日志采集接口，启动Flume监控

如果上个步骤中设置从“earliest-offset”消费kafka数据，可以暂时不启动日志采集接口和Flume

#在node5节点上启动日志采集接口
[root@node5 ~]# cd /software/
[root@node5 software]# java -jar logcollector-0.0.1-SNAPSHOT.jar


#在node5节点上启动Flume
[root@node5 software]# flume-ng agent --name a -f /software/a.properties -Dflume.root.logger=INFO,console