查看“Flink实时数据处理”的源代码

=Flink实时统计目标:=
1.编程读取kafka缓存数据

2.商品实时统计:

项目一:每日销售额,每日访问流量实时统计

项目二:每日每家门店总销售额实时统计

项目三:每日商品销量实时统计,并选出10大热销商品

=项目开发一=
a)设计与创建mysql结果存储表

b)编程读取kafka缓存数据

c)每日'''访问流量'''||每日'''销售额'''实时统计

d)在IDEA中运行程序



==创建mysql数据表==
我们会将处理后的数据存入mysql数据表中,所以现在进入master节点下

(1)查看mysql是否是启动状态 

<nowiki>#service mysqld status</nowiki>

<nowiki>mysqld (pid  1409) is running.</nowiki>

(2)输入以下代码进入mysql中（密码：root)

 <nowiki>mysql -uroot -proot</nowiki>

 create database fk_shop;  //创建数据存放库
 use fk_shop; //进入数据库
 create table visitcount_everyday(datetime varchar(20) ,visicount double);//创建点击流表
 create table salevolume(datetime varchar(20),salevolume double);//创建销售额表
 show tables; //查看表

==定义mysql sink==

在项目中创建个scala文件夹,再根据mysql数据表结构在scala文件中创建类似以下'''包'''(名称可自定义)

[[文件:Scalaflies.png]]

===otherclass===

里面存放的是第一个scala文件:主要功能是定义需要处理的数据字段

因为创建的mysql表中销售额和点击流都属于double类型,日期为string类型

所以项目一中我们需要定义'''每日的点击流'''与'''每日的销售额'''

(1)在otherclass里面创建第一个scala文件,名为StringAndDouble(自定义)

[[文件:Otherclass.png]]

(2)更改StringAndDouble.class

 <nowiki>package objectclass

class StringAndDouble(datetime:String,sale:Double) {
    def getDate()={datetime}
    def getSale()={sale}
}</nowiki>

===mysqlsink===

(1)在mysqlsink里面创建第二个scala文件,名为MySqlSinkToSale(自定义)

[[文件:Mysqlsink.png]]

更改MySqlSinkToSale

 package mysqlsink
 import java.sql.DriverManager
 import java.sql._
 import org.apache.flink.streaming.api.functions.sink.RichSinkFunction
 import otherclass.StringAndDouble
 class MySqlSinkToSale(table:String) extends RichSinkFunction[StringAndDouble] with Serializable {
   var conn:Connection=_
   var ps:PreparedStatement=_
   val user="root"
   val password="root"
   val driver="com.mysql.jdbc.Driver"
   val url="jdbc:mysql://192.168.128.130:3306/fk_shop"
   override def invoke(value: StringAndDouble): Unit = {
     Class.forName(driver)
     conn=DriverManager.getConnection(url,user,password)
     val sql = "insert into "+ table +" values(?,?)"
     ps=conn.prepareStatement(sql)
     ps.setString(1,value.getDate())
     ps.setDouble(2,value.getSale())
     ps.executeUpdate()
     if(ps!=null) ps.close()
     if(conn!=null) conn.close()
   }
 }


===flinkclass===

实现flink数据处理

(1)在flinkclass里面创建一个'''object对象'''

[[文件:flinkclass1.png]]

Flink实时处理编程过程

 <nowiki>package flinkclass
import java.util.Properties

import mysqlsink.SaleSQLSink
import objectclass.StringAndDouble
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer
import org.apache.flink.api.common.serialization.SimpleStringSchema
object SaleVolumn {
  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    //配置Kafka参数
    val pro = new Properties()
    pro.setProperty("bootstrap.servers","192.168.128.130:9092")
    pro.setProperty("group.id","test")
    //创建数据源
    val stream  = env.addSource(new FlinkKafkaConsumer[String]("shop",new SimpleStringSchema(),pro))
    //数据处理过程
    val data = stream.map(x=>x.split(",")).filter(x=>x.nonEmpty && x.length==11)
      .map{x=>if(x(7).contains("buy")) (x(x.length-1),(x(5).toDouble,1.0)) else (x(x.length-1),(0.0,1.0))}.keyBy(0).timeWindow(Time.minutes(1)).reduce((x,y)=>(x._1,(x._2._1+y._2._1,x._2._2+y._2._2)))
    //保存每日销售额到MYSQL表
    data.map(x=>new StringAndDouble(x._1,x._2._1)).addSink(new SaleSQLSink("salevolume"))
    //保存每日点击流到mysql表
    data.map(x=>new StringAndDouble(x._1,x._2._2)).addSink(new SaleSQLSink("visitcount_everyday"))
    env.execute()
  }
}</nowiki>

flink中的时间窗口：https://blog.csdn.net/zg_hover/article/details/87592060

==运行flink程序==
===关闭flink===
[root@master local]# cd /usr/local/flink-1.10.1/bin

[root@master bin]# stop-cluster.sh

 <nowiki>Stopping taskexecutor daemon (pid: 7986) on host slave1.centos.com.
Stopping taskexecutor daemon (pid: 5760) on host slave2.centos.com.
Stopping taskexecutor daemon (pid: 3219) on host slave3.centos.com.
Stopping standalonesession daemon (pid: 11586) on host master.centos.com.</nowiki>

===关闭kafka===
①在所有节点查看kafka的状态,如果运行则kill掉

[[文件:Killkafka.png]]

 jps

 kill -9 3507

===关闭zookeeper===

===启动zookeeper===

必须先启动zookeeper 再启动kafka,不然启动无效,启动kafka里面的zookeeper

nohup bin/zookeeper-server-start.sh config/zookeeper.properties >log/zookeeper/zookeeper.log 2>1 & 

===启动kafka===

③在所有节点启动zookeeper后在master,slave1,slave2上启动kafka

 /usr/local/kafka/bin/kafka-server-start.sh /usr/local/kafka/config/server.propertiec >/tmp/klog &

④Jps查看进程

[[文件:jincheng.png]]

===创建kafka主题===
查看主题是否创建没有则创建shop主题

[root@master kafka_2.11-2.3.1]# bin/kafka-topics.sh --list --zookeeper 192.168.128.130:2181 
  
 <nowiki>__consumer_offsets
shop
test</nowiki>

[root@master kafka_2.11-2.3.1]#bin/kafka-topics.sh  --zookeeper 192.168.128.130:2181  --delete  --topic shop

[root@master kafka_2.11-2.3.1]# bin/kafka-topics.sh -create --zookeeper 192.168.128.130:2181 -replication-factor 1 --partitions 1 --topic shop 

[root@master kafka_2.11-2.3.1]#  bin/kafka-topics.sh --list --zookeeper 192.168.128.130:2181 

 <nowiki>__consumer_offsets
shop
test</nowiki>

===启动flink集群===

⑤在master启动flink集群

 /usr/local/flink/start-cluster.sh

===启动flume采集===
在IDEA中启动flume采集方案时,把之间采集的数据删除掉, 检查模拟采集是否启动

 crontab -e

查看采集的数据：

[root@master bin]# rm -rf /opt/flinkproject/

[root@master bin]# mkdir /opt/flinkproject

[root@master bin]# ls /opt/flinkproject/

 2020-07-25-20-05-01.log

启动之前写好的flume采集方案

/usr/local/flume/bin/flume-ng agent -c /usr/local/flume/conf/ -f /usr/local/flume/conf/spool-memory-kafka.conf -n client -Dflume.root.logger=INFO,console 

===运行flink客户端程序-SaleVolumn===

终于到了IDEA中本地运行项目SaleVolumn,无报错后,进入mysql中查看最开始定义的两个表,mysql表里面就有每日的访问流量和每日的销售额了

[[文件:bd20-6-16.png|600px]]

[[文件:mysqlsale1.png]]
[[文件:Msyqlsale1-1.png]]

=项目开发二=
a)设计与创建mysql结果存储表

b)编程读取kafka缓存数据

c)每日 门店 销售额 实时统计

d)IDEA中运行程序

==创建mysql数据表==
我们会将处理后的数据存入mysql数据表中,所以现在进入master节点下

(1)查看mysql是否是启动状态 

<nowiki>#service mysqld status</nowiki>

<nowiki>mysqld (pid  1409) is running.</nowiki>

(2)输入以下代码进入mysql中

 <nowiki>mysql -uroot -proot</nowiki>

 create database fk_shop;  //创建数据存放库
 use fk_shop; //进入数据库
 create table store_sale( datetime varchar(20), store_id varchar(20), sale double);//创建门店日销售额表

 show tables; //查看表

==定义mysql sink==

在项目中已经创建好了scala文件夹以及下面的包,下面就只需要写入scala程序即可

[[文件:Scalaflies.png]]

===otherclass===

里面存放的是第一个scala文件:主要功能是定义需要处理的数据字段

因为需要统计的是每日(string)店铺(string)销售额(double)

所以项目一中我们需要定义三个字段的值

(1)在otherclass里面创建第一个scala文件,名为Store_SaleF(自定义)

[[文件:Store_SaleF.png]]

(2)更改Store_SaleF.class

 package otherclass

 class Store_SaleF (datetime:String,store:String,sale:Double){
   def getDate()={
     datetime
   }
   def getStore()={
     store
   }
   def getSale()={
     sale
   }
 }

===mysqlsink===

(1)在mysqlsink里面创建第二个scala文件,名为SaleSQLSink自定义)

[[文件:Mysql_StoreSale.png]]

更改SaleSQLSink

 <nowiki>package mysqlsink

import org.apache.flink.streaming.api.functions.sink.RichSinkFunction
import java.sql._

import objectclass.StringAndDouble
class SaleSQLSink(table:String) extends RichSinkFunction[StringAndDouble] with Serializable {
  //定义连接的MYSQL配置参数
  var conn:Connection=_
  var ps:PreparedStatement=_
  val user="root"
  val password="root"
  val driver="com.mysql.jdbc.Driver"
  val url="jdbc:mysql://192.168.128.130:3306/fk_shop"

  override def invoke(value: StringAndDouble): Unit = {
    Class.forName(driver)
    conn = DriverManager.getConnection(url,user,password)
    val sql="insert into "+table+" values(?,?)"
    ps = conn.prepareStatement(sql)
    ps.setString(1,value.getDate())
    ps.setDouble(2,value.getSale())
    ps.executeUpdate()
    if(ps!=null) ps.close()
    if(conn!=null) conn.close()
  }
}
</nowiki>

===flinkclass===

实现flink数据处理

(1)在flinkclass里面创建一个object对象,名为Store_Saleflink

[[文件:Store_Saleflink.png]]

Flink实时处理编程过程

 package flinkclass
 import java.util.Properties
 import mysqlsink.Mysql_StoreSale
 import java.util.Properties
 import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
 import org.apache.flink.streaming.api.scala._
 import org.apache.flink.streaming.api.windowing.time.Time
 import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer
 import org.apache.flink.api.common.serialization.SimpleStringSchema
 import otherclass.Store_SaleF
 object Store_Saleflink {
   def main(args: Array[String]): Unit = {
     val env = StreamExecutionEnvironment.getExecutionEnvironment
     //kafka集群参数配置
     val pro = new Properties()
     pro.setProperty("bootstrap.servers","192.168.128.130:9092")
     pro.setProperty("group.id","test")
     //创建源
     val stream = env.addSource(new FlinkKafkaConsumer[String]("shop",new SimpleStringSchema(),pro))
     //数据转换
     val data=stream.map(x=>x.split(",")).filter(x=>x.nonEmpty && x.length==11 && x(7).contains("buy")).map(x=>((x(x.length- 
 1),x(6)),x(5).toDouble)).keyBy(0).timeWindow(Time.minutes(1)).sum(1).map(x=>new Store_SaleF(x._1._1,x._1._2,x._2))
     //数据存储
     data.addSink(new Mysql_StoreSale("store_sale"))
     env.execute()
   }
 }


==运行flink程序==

①在所有节点查看kafka的状态,如果运行则kill掉

[[文件:Killkafka.png]]

 jps
 kill -9 3507

②必须先启动zookeeper 再启动kafka,不然启动无效,启动kafka里面的zookeeper

 usr/local/kafka_2.11-2.3.1/bin/zookeeper-server-start.sh /usr/local/kafka_2.11-2.3.1/config/zookeeper.properties >/tmp/startzklog &

③在所有节点启动zookeeper后在master,slave1,slave2,slave3上启动kafka

 /usr/local/kafka/bin/kafka-server-start.sh /usr/local/kafka/config/server.propertiec >/tmp/klog &

④Jps查看进程

[[文件:jincheng.png]]

⑤在master启动flink集群

 /usr/local/flink/start-cluster.sh

⑥查看主题是否创建没有则创建shop主题

 kafka-topics.sh –zookeeper 192.168.128.130:2181–list  查看已创建主题
 kafka-topics.sh –zookeeper 192.168.128.130:2181 –delete –topic shop 删除shop主题
 kafka-topics.sh –zookeeper 192.168.128.130:2181 –create –topic shop –partitions 1 
 –replication -factor 1  创建shop主题

⑦在IDEA中启动flume采集方案时,把之间采集的数据删除掉, 检查模拟采集是否启动

 crontab -e

⑧启动之前写好的flume采集方案

 fume-ng agent -c /usr/local/flume/conf/ -f /usr/local/flume/conf/spool-memory- 
 kafka.conf -n client -Dflume.root.logger=INFO,console

⑨终于到了IDEA中本地运行项目Store_Saleflink,无报错后,进入mysql中查看最开始定义的一个表,mysql表里面就有每日商品销售额了

[[文件:mysqlsale2.png]]

=项目开发三=

商品销量实时统计,并选出10大热销商品

a)编程flink程序

b)启动flink集群

c)IDEA中运行程序


在项目中已经创建好了scala文件夹以及下面的包,下面就只需要写入scala程序即可

[[文件:Scalaflies.png]]


===flinkclass===

实现flink数据处理

(1)在flinkclass里面创建一个object对象,名为GoodsOrder

[[文件:GoodsOrder.png]]

Flink实时处理编程过程

 package flinkclass
 import java.util.Properties
 import objectclass.StringAndDouble
 import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
 import org.apache.flink.streaming.api.scala._
 import org.apache.flink.streaming.api.windowing.time.Time
 import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer
 import org.apache.flink.api.common.serialization.SimpleStringSchema
 import org.apache.flink.streaming.api.windowing.windows.TimeWindow
 import org.apache.flink.streaming.api.scala.function.ProcessWindowFunction
 import org.apache.flink.util.Collector
 object GoodsOrder {
   def main(args: Array[String]): Unit = {
     val env = StreamExecutionEnvironment.getExecutionEnvironment
     //kafka集群参数配置
     val pro = new Properties()
     pro.setProperty("bootstrap.servers","master:9092")
     pro.setProperty("group.id","test")
     //创建源
     val stream = env.addSource(new FlinkKafkaConsumer[String]("shop",new SimpleStringSchema(),pro))
 //数据转换
     // ProcessWindowFunction[In,Out,Key,Window]
     val data=stream.map(x=>x.split(",")).filter(x=>x.nonEmpty && x.length==11 && x(7).contains("buy")).map(x=>new 
 StringAndDouble(x(4),1.0)).keyBy(x=>x.getDate()).timeWindow(Time.minutes(1)).process(new ProcessWindowFunction[StringAndDouble,String,String,TimeWindow]{
       override def process(key: String, context: Context, elements: Iterable[StringAndDouble], out: Collector[String]): Unit = {
        val getsum = elements.toList.groupBy(x=>x.getDate()).map{tmp=>val ll=tmp._2.map(x=>x.getSale()).sum;(tmp._1,ll)}
         val e = getsum.toList.sortBy(x=>x._2).reverse.take(10)
         e.map(x=>out.collect(System.currentTimeMillis().toString()+":"+x._1+":"+x._2))
       }
     })
     //数据存储
     data.print()
     env.execute()
   }
 }


==运行flink程序==

①在所有节点查看kafka的状态,如果运行则kill掉

[[文件:Killkafka.png]]

 jps
 kill -9 3507

②必须先启动zookeeper 再启动kafka,不然启动无效,启动kafka里面的zookeeper

 usr/local/kafka_2.11-2.3.1/bin/zookeeper-server-start.sh /usr/local/kafka_2.11-2.3.1/config/zookeeper.properties >/tmp/startzklog &

③在所有节点启动zookeeper后在master,slave1,slave2,slave3上启动kafka

 /usr/local/kafka/bin/kafka-server-start.sh /usr/local/kafka/config/server.propertiec >/tmp/klog &

④Jps查看进程

[[文件:jincheng.png]]

⑤在master启动flink集群

 /usr/local/flink/start-cluster.sh

⑥查看主题是否创建没有则创建shop主题

 kafka-topics.sh –zookeeper 192.168.128.130:2181–list  查看已创建主题
 kafka-topics.sh –zookeeper 192.168.128.130:2181 –delete –topic shop 删除shop主题
 kafka-topics.sh –zookeeper 192.168.128.130:2181 –create –topic shop –partitions 1 
 –replication -factor 1  创建shop主题

⑦在IDEA中启动flume采集方案时,把之间采集的数据删除掉, 检查模拟采集是否启动

 crontab -e

⑧启动之前写好的flume采集方案

 fume-ng agent -c /usr/local/flume/conf/ -f /usr/local/flume/conf/spool-memory- 
 kafka.conf -n client -Dflume.root.logger=INFO,console

⑨终于到了IDEA中本地运行项目GoodsOrder,查看结果

[[文件:GoodsOrderflink.png]]