基于Flume+Kafka+Hbase+Flink+FineBI的实时综合案例(五)FineBI可视化

22：FineBI配置数据集

• 目标：实现FineBI访问MySQL结果数据集的配置

• 实施

  • 安装FineBI

    • 参考《FineBI Windows版本安装手册.docx》安装FineBI

      

  • 配置连接

    

    

    

    数据连接名称：Momo
    用户名：root
    密码：自己MySQL的密码
    数据连接URL：jdbc:mysql://node1:3306/momo?useUnicode=true&characterEncoding=utf8
    

    

    

  • 数据准备

    

    

    

    SELECT  id, momo_totalcount,momo_province,momo_username,momo_msgcount,CASE momo_grouptype WHEN '1' THEN '总消息量' WHEN '2' THEN '各省份发送量'  WHEN '3' THEN '各省份接收量'WHEN '4' THEN '各用户发送量' WHEN '5' THEN '各用户接收量' END AS momo_grouptype
    FROM  momo_count
    

• 小结

  • 实现FineBI访问MySQL结果数据集的配置

23：FineBI构建报表

• 目标：实现FineBI实时报表构建

• 路径

  • step1：实时报表构建
  • step2：实时报表配置
  • step3：实时刷新测试

• 实施

  • 实时报表构建

    • 新建仪表盘

      

      

    • 添加标题

      

      

    • 实时总消息数

      

    • 发送消息最多的Top10用户

      

      

      

      

      

      

      

      

    • 接受消息最多的Top10用户

      

      

      

      

    • 各省份发送消息Top10

      

      

      

    • 各省份接收消息Top10

      

      

      

      

      

      

    • 各省份总消息量

      

      

      

      

• 小结

  • 实现FineBI实时报表构建

24：FineBI实时配置测试

• 目标：实现实时报表测试

• 实施

  • 实时报表配置

    • 官方文档：https://help.fanruan.com/finebi/doc-view-363.html

    • 添加jar包：将jar包放入FineBI安装目录的 webapps\webroot\WEB-INF\lib目录下

      

      • 注意：如果提示已存在，就选择覆盖

    • 添加JS文件

      • 创建js文件：refresh.js

        setTimeout(function () {var b =document.title;var a =BI.designConfigure.reportId;//获取仪表板id//这里要指定自己仪表盘的idif (a=="d574631848bd4e33acae54f986d34e69") {setInterval(function () {BI.SharingPool.put("controlFilters", BI.Utils.getControlCalculations());//Data.SharingPool.put("controlFilters", BI.Utils.getControlCalculations());BI.Utils.broadcastAllWidgets2Refresh(true);}, 3000);//5000000为定时刷新的频率，单位ms}
        }, 2000)
        

      • 将创建好的refresh.js文件放至 FineBI 安装目录%FineBI%/webapps/webroot中

        

      • 关闭FineBI缓存，然后关闭FineBI

        

      • 修改jar包，添加js

        

        

        

        

        <!-- 增加刷新功能 --> 
        <script type="text/javascript" src="/webroot/refresh.js"></script>
        

    • 重启FineBI

• 实时刷新测试

  • 清空MySQL结果表

  • 启动Flink程序：运行MoMoFlinkCount

  • 启动Flume程序

    cd /export/server/flume-1.9.0-bin
    bin/flume-ng agent -c conf/ -n a1 -f usercase/momo_mem_kafka.properties -Dflume.root.logger=INFO,console
    

  • 启动模拟数据

    java -jar /export/data/momo_init/MoMo_DataGen.jar \
    /export/data/momo_init/MoMo_Data.xlsx \
    /export/data/momo_data/ \
    10
    

    
  - 观察报表

• 小结

  • 实现FineBI实时测试

## 附录一：Maven依赖​```xml<!--远程仓库--><repositories><repository><id>aliyun</id><url>http://maven.aliyun.com/nexus/content/groups/public/</url><releases><enabled>true</enabled></releases><snapshots><enabled>false</enabled><updatePolicy>never</updatePolicy></snapshots></repository></repositories><dependencies><!--Hbase 客户端--><dependency><groupId>org.apache.hbase</groupId><artifactId>hbase-client</artifactId><version>2.1.0</version></dependency><!--kafka 客户端--><dependency><groupId>org.apache.kafka</groupId><artifactId>kafka-clients</artifactId><version>2.4.1</version></dependency><!--JSON解析工具包--><dependency><groupId>com.alibaba</groupId><artifactId>fastjson</artifactId><version>1.2.62</version></dependency><!--Flink依赖--><dependency><groupId>org.apache.flink</groupId><artifactId>flink-java</artifactId><version>1.10.0</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-java_2.11</artifactId><version>1.10.0</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-runtime-web_2.11</artifactId><version>1.10.0</version></dependency><!-- flink操作hdfs、Kafka、MySQL、Redis，所需要导入该包--><dependency><groupId>org.apache.flink</groupId><artifactId>flink-shaded-hadoop-2-uber</artifactId><version>2.7.5-10.0</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-kafka_2.11</artifactId><version>1.10.0</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-jdbc_2.11</artifactId><version>1.10.0</version></dependency><dependency><groupId>org.apache.bahir</groupId><artifactId>flink-connector-redis_2.11</artifactId><version>1.0</version></dependency><!--HTTP请求的的依赖--><dependency><groupId>org.apache.httpcomponents</groupId><artifactId>httpclient</artifactId><version>4.5.4</version></dependency><!--MySQL连接驱动--><dependency><groupId>mysql</groupId><artifactId>mysql-connector-java</artifactId><version>5.1.38</version></dependency></dependencies><build><plugins><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-compiler-plugin</artifactId><version>3.1</version><configuration><target>1.8</target><source>1.8</source></configuration></plugin></plugins></build>

附录二：离线消费者完整代码

package bigdata.itcast.cn.momo.offline;import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.Connection;
import org.apache.hadoop.hbase.client.ConnectionFactory;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.client.Table;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.hbase.util.MD5Hash;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.clients.consumer.OffsetAndMetadata;
import org.apache.kafka.common.TopicPartition;import java.io.IOException;
import java.text.ParseException;
import java.text.SimpleDateFormat;
import java.time.Duration;
import java.util.*;/*** @ClassName MomoKafkaToHbase* @Description TODO 离线场景：消费Kafka的数据写入Hbase* @Create By     Maynor*/
public class MomoKafkaToHbase {private  static SimpleDateFormat format = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");private static Connection conn;private static Table table;private static TableName tableName = TableName.valueOf("MOMO_CHAT:MOMO_MSG");//表名private static byte[] family = Bytes.toBytes("C1");//列族//todo:2-构建Hbase连接//静态代码块: 随着类的加载而加载,一般只会加载一次,避免构建多个连接影响性能static{try {//构建配置对象Configuration conf = HBaseConfiguration.create();conf.set("hbase.zookeeper.quorum","node1:2181,node2:2181,node3:2181");//构建连接conn = ConnectionFactory.createConnection(conf);//获取表对象table = conn.getTable(tableName);} catch (IOException e) {e.printStackTrace();}}public static void main(String[] args) throws Exception {//todo:1-构建消费者，获取数据consumerKafkaToHbase();
//        String momoRowkey = getMomoRowkey("2020-08-13 12:30:00", "13071949728", "17719988692");
//        System.out.println(momoRowkey);}/*** 用于消费Kafka的数据，将合法数据写入Hbase*/private static void consumerKafkaToHbase() throws Exception {//构建配置对象Properties props = new Properties();//指定服务端地址props.setProperty("bootstrap.servers", "node1:9092,node2:9092,node3:9092");//指定消费者组的idprops.setProperty("group.id", "momo1");//关闭自动提交props.setProperty("enable.auto.commit", "false");//指定K和V反序列化的类型props.setProperty("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");props.setProperty("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");//构建消费者的连接KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);//指定订阅哪些Topicconsumer.subscribe(Arrays.asList("MOMO_MSG"));//持续拉取数据while (true) {//向Kafka请求拉取数据，等待Kafka响应，在100ms以内如果响应，就拉取数据，如果100ms内没有响应，就提交下一次请求： 100ms为等待Kafka响应时间//拉取到的所有数据：多条KV数据都在ConsumerRecords对象，类似于一个集合ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));//todo:3-处理拉取到的数据：打印//取出每个分区的数据进行处理Set<TopicPartition> partitions = records.partitions();//获取本次数据中所有分区//对每个分区的数据做处理for (TopicPartition partition : partitions) {List<ConsumerRecord<String, String>> partRecords = records.records(partition);//取出这个分区的所有数据//处理这个分区的数据long offset = 0;for (ConsumerRecord<String, String> record : partRecords) {//获取TopicString topic = record.topic();//获取分区int part = record.partition();//获取offsetoffset = record.offset();//获取KeyString key = record.key();//获取ValueString value = record.value();System.out.println(topic + "\t" + part + "\t" + offset + "\t" + key + "\t" + value);//将Value数据写入Hbaseif(value != null && !"".equals(value) && value.split("\001").length == 20 ){writeToHbase(value);}}//手动提交分区的commit offsetMap<TopicPartition, OffsetAndMetadata> offsets = Collections.singletonMap(partition,new OffsetAndMetadata(offset+1));consumer.commitSync(offsets);}}}/*** 用于实现具体的写入Hbase的方法* @param value*/private static void writeToHbase(String value) throws Exception {//todo:3-写入Hbase//切分数据String[] items = value.split("\001");String stime = items[0];String sender_accounter = items[2];String receiver_accounter = items[11];//构建rowkeyString rowkey = getMomoRowkey(stime,sender_accounter,receiver_accounter);//构建PutPut put = new Put(Bytes.toBytes(rowkey));//添加列put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("msg_time"),Bytes.toBytes(items[0]));put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("sender_nickyname"),Bytes.toBytes(items[1]));put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("sender_account"),Bytes.toBytes(items[2]));put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("sender_sex"),Bytes.toBytes(items[3]));put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("sender_ip"),Bytes.toBytes(items[4]));put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("sender_os"),Bytes.toBytes(items[5]));put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("sender_phone_type"),Bytes.toBytes(items[6]));put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("sender_network"),Bytes.toBytes(items[7]));put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("sender_gps"),Bytes.toBytes(items[8]));put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("receiver_nickyname"),Bytes.toBytes(items[9]));put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("receiver_ip"),Bytes.toBytes(items[10]));put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("receiver_account"),Bytes.toBytes(items[11]));put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("receiver_os"),Bytes.toBytes(items[12]));put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("receiver_phone_type"),Bytes.toBytes(items[13]));put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("receiver_network"),Bytes.toBytes(items[14]));put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("receiver_gps"),Bytes.toBytes(items[15]));put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("receiver_sex"),Bytes.toBytes(items[16]));put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("msg_type"),Bytes.toBytes(items[17]));put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("distance"),Bytes.toBytes(items[18]));put.addColumn(Bytes.toBytes("C1"),Bytes.toBytes("message"),Bytes.toBytes(items[19]));//执行写入table.put(put);}/*** 基于消息时间、发送人id、接受人id构建rowkey* @param stime* @param sender_accounter* @param receiver_accounter* @return* @throws Exception*/private static String getMomoRowkey(String stime, String sender_accounter, String receiver_accounter) throws Exception {//转换时间戳long time = format.parse(stime).getTime();String suffix = sender_accounter+"_"+receiver_accounter+"_"+time;//构建MD5String prefix = MD5Hash.getMD5AsHex(Bytes.toBytes(suffix)).substring(0,8);//合并返回return prefix+"_"+suffix;}
}