【从0开始の全记录】Flume+Kafka+Spark+Spring Boot 统计网页访问量项目

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1.需求说明

1.1 需求

到现在为止的网页访问量

到现在为止从搜索引擎引流过来的网页访问量

项目总体框架如图所示：

1.2 用户行为日志内容

2.模拟日志数据制作

用Python制作模拟数据，数据包含：

• 不同的URL地址->url_paths

• 不同的跳转链接地址->http_refers

• 不同的搜索关键词->search_keyword

• 不同的状态码->status_codes

• 不同的IP地址->ip_slices

#coding=UTF-8import randomimport time
url_paths = [    "class/112.html",    "class/128.html",    "class/145.html",    "class/146.html",    "class/131.html",    "class/130.html",    "class/145.html",    "learn/821.html",    "learn/825.html",    "course/list"]
http_refers=[    "http://www.baidu.com/s?wd={query}",    "https://www.sogou.com/web?query={query}",    "http://cn.bing.com/search?q={query}",    "http://search.yahoo.com/search?p={query}",]
search_keyword = [    "Spark+Sql",    "Hadoop",    "Storm",    "Spark+Streaming",    "大数据",    "面试"]
status_codes = ["200","404","500"]
ip_slices = [132,156,132,10,29,145,44,30,21,43,1,7,9,23,55,56,241,134,155,163,172,144,158]
def sample_url():    return random.sample(url_paths,1)[0]
def sample_ip():    slice = random.sample(ip_slices,4)    return ".".join([str(item) for item in slice])
def sample_refer():    if random.uniform(0,1) > 0.2:        return "-"    refer_str = random.sample(http_refers,1)    query_str = random.sample(search_keyword,1)    return refer_str[0].format(query=query_str[0])
def sample_status():    return random.sample(status_codes,1)[0]
def generate_log(count = 10):    time_str = time.strftime("%Y-%m-%d %H:%M:%S",time.localtime())
    f = open("/home/hadoop/tpdata/project/logs/access.log","w+")    while count >= 1:        query_log = "{ip}\t{local_time}\t\"GET /{url} HTTP/1.1\"\t{status}\t{refer}".format(            local_time=time_str,            url=sample_url(),            ip=sample_ip(),            refer=sample_refer(),            status=sample_status())        print(query_log)        f.write(query_log + "\n")        count = count - 1
if __name__ == '__main__':    generate_log(100)

使用Linux Crontab定时调度工具，使其每一分钟产生一批数据。

表达式：

*/1 * * * *

编写python运行脚本：

vi log_generator.shpython /home/hadoop/tpdata/log.pychmod u+x log_generator.sh

配置Crontab： 

crontab -e*/1 * * * * /home/hadoop/tpdata/project/log_generator.sh

2.Flume实时收集日志信息

开发时选型：

编写streaming_project.conf：

vi streaming_project.conf

exec-memory-logger.sources = exec-sourceexec-memory-logger.sinks = logger-sinkexec-memory-logger.channels = memory-channel
exec-memory-logger.sources.exec-source.type = execexec-memory-logger.sources.exec-source.command = tail -F /home/hadoop/tpdata/project/logs/access.logexec-memory-logger.sources.exec-source.shell = /bin/sh -c
exec-memory-logger.channels.memory-channel.type = memory
exec-memory-logger.sinks.logger-sink.type = logger
exec-memory-logger.sources.exec-source.channels = memory-channelexec-memory-logger.sinks.logger-sink.channel = memory-channel

启动Flume测试：

flume-ng agent \--name exec-memory-logger \--conf $FLUME_HOME/conf \--conf-file /home/hadoop/tpdata/project/streaming_project.conf \-Dflume.root.logger=INFO,console

启动Zookeeper:

./zkServer.sh start

启动Kafka Server：

./kafka-server-start.sh -daemon $KAFKA_HOME/config/server.properties

其中server.properties：

broker.id=0############################# Socket Server Settings #############################listeners=PLAINTEXT://:9092host.name=hadoop000advertised.host.name=192.168.1.9advertised.port=9092num.network.threads=3num.io.threads=8socket.send.buffer.bytes=102400socket.receive.buffer.bytes=102400socket.request.max.bytes=104857600############################# Log Basics #############################log.dirs=/home/hadoop/app/tmp/kafka-logsnum.partitions=1num.recovery.threads.per.data.dir=1############################# Log Retention Policy #############################log.retention.hours=168log.segment.bytes=1073741824log.retention.check.interval.ms=300000log.cleaner.enable=false############################# Zookeeper #############################zookeeper.connect=hadoop000:2181zookeeper.connection.timeout.ms=6000

启动一个Kafka的消费者(topic用的之前的，没有的话可以新建一个)：

kafka-console-consumer.sh --zookeeper hadoop000:2181 --topic streamingtopic

修改Flume配置文件，使得Flume的sink链接到Kafka：

vi streaming_project2.conf

exec-memory-kafka.sources = exec-sourceexec-memory-kafka.sinks = kafka-sinkexec-memory-kafka.channels = memory-channel
exec-memory-kafka.sources.exec-source.type = execexec-memory-kafka.sources.exec-source.command = tail -F /home/hadoop/tpdata/project/logs/access.logexec-memory-kafka.sources.exec-source.shell = /bin/sh -c
exec-memory-kafka.channels.memory-channel.type = memory
exec-memory-kafka.sinks.kafka-sink.type = org.apache.flume.sink.kafka.KafkaSinkexec-memory-kafka.sinks.kafka-sink.brokerList = hadoop000:9092exec-memory-kafka.sinks.kafka-sink.topic = streamingtopicexec-memory-kafka.sinks.kafka-sink.batchSize = 5exec-memory-kafka.sinks.kafka-sink.requiredAcks = 1
exec-memory-kafka.sources.exec-source.channels = memory-channelexec-memory-kafka.sinks.kafka-sink.channel = memory-channel

启动Flume：

flume-ng agent \--name exec-memory-kafka \--conf $FLUME_HOME/conf \--conf-file /home/hadoop/tpdata/project/streaming_project2.conf \-Dflume.root.logger=INFO,console

kafka消费者拿到数据：

4.Spark Streaming对接Kafka对数据消费

4.1 pom.xml：

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">  <modelVersion>4.0.0</modelVersion>  <groupId>com.taipark.spark</groupId>  <artifactId>sparktrain</artifactId>  <version>1.0</version>  <inceptionYear>2008</inceptionYear>  <properties>    <scala.version>2.11.8</scala.version>    <kafka.version>0.9.0.0</kafka.version>    <spark.version>2.2.0</spark.version>    <hadoop.version>2.6.0-cdh5.7.0</hadoop.version>    <hbase.version>1.2.0-cdh5.7.0</hbase.version>  </properties>
  <repositories>    <repository>      <id>cloudera</id>      <url>https://repository.cloudera.com/artifactory/cloudera-repos</url>    </repository>  </repositories>
  <dependencies>    <dependency>      <groupId>org.scala-lang</groupId>      <artifactId>scala-library</artifactId>      <version>${scala.version}</version>    </dependency>
    <!--    <dependency>      <groupId>org.apache.kafka</groupId>      <artifactId>kafka_2.11</artifactId>      <version>${kafka.version}</version>    </dependency>    -->
    <dependency>      <groupId>org.apache.hadoop</groupId>      <artifactId>hadoop-client</artifactId>      <version>${hadoop.version}</version>    </dependency>
    <dependency>      <groupId>org.apache.hbase</groupId>      <artifactId>hbase-client</artifactId>      <version>${hbase.version}</version>    </dependency>
    <dependency>      <groupId>org.apache.hbase</groupId>      <artifactId>hbase-server</artifactId>      <version>${hbase.version}</version>    </dependency>
    <dependency>      <groupId>org.apache.spark</groupId>      <artifactId>spark-streaming_2.11</artifactId>      <version>${spark.version}</version>    </dependency>
    <dependency>      <groupId>org.apache.spark</groupId>      <artifactId>spark-streaming-kafka-0-8_2.11</artifactId>      <version>2.2.0</version>    </dependency>
    <!--SS整合Flume依赖-->    <dependency>      <groupId>org.apache.spark</groupId>      <artifactId>spark-streaming-flume_2.11</artifactId>      <version>${spark.version}</version>    </dependency>
    <!--SS整合sink依赖-->    <dependency>      <groupId>org.apache.spark</groupId>      <artifactId>spark-streaming-flume-sink_2.11</artifactId>      <version>${spark.version}</version>    </dependency>
    <dependency>      <groupId>org.apache.commons</groupId>      <artifactId>commons-lang3</artifactId>      <version>3.5</version>    </dependency>
    <dependency>      <groupId>org.apache.spark</groupId>      <artifactId>spark-sql_2.11</artifactId>      <version>${spark.version}</version>    </dependency>
    <dependency>      <groupId>mysql</groupId>      <artifactId>mysql-connector-java</artifactId>      <version>8.0.13</version>    </dependency>
    <dependency>      <groupId>com.fasterxml.jackson.module</groupId>      <artifactId>jackson-module-scala_2.11</artifactId>      <version>2.6.5</version>    </dependency>
    <dependency>      <groupId>net.jpountz.lz4</groupId>      <artifactId>lz4</artifactId>      <version>1.3.0</version>    </dependency>
    <dependency>      <groupId>org.apache.flume.flume-ng-clients</groupId>      <artifactId>flume-ng-log4jappender</artifactId>      <version>1.6.0</version>    </dependency>  </dependencies>
  <build>    <sourceDirectory>src/main/scala</sourceDirectory>    <testSourceDirectory>src/test/scala</testSourceDirectory>    <plugins>      <plugin>        <groupId>org.scala-tools</groupId>        <artifactId>maven-scala-plugin</artifactId>        <executions>          <execution>            <goals>              <goal>compile</goal>              <goal>testCompile</goal>            </goals>          </execution>        </executions>        <configuration>          <scalaVersion>${scala.version}</scalaVersion>          <args>            <arg>-target:jvm-1.5</arg>          </args>        </configuration>      </plugin>      <plugin>        <groupId>org.apache.maven.plugins</groupId>        <artifactId>maven-eclipse-plugin</artifactId>        <configuration>          <downloadSources>true</downloadSources>          <buildcommands>            <buildcommand>ch.epfl.lamp.sdt.core.scalabuilder</buildcommand>          </buildcommands>          <additionalProjectnatures>            <projectnature>ch.epfl.lamp.sdt.core.scalanature</projectnature>          </additionalProjectnatures>          <classpathContainers>            <classpathContainer>org.eclipse.jdt.launching.JRE_CONTAINER</classpathContainer>            <classpathContainer>ch.epfl.lamp.sdt.launching.SCALA_CONTAINER</classpathContainer>          </classpathContainers>        </configuration>      </plugin>    </plugins>  </build>  <reporting>    <plugins>      <plugin>        <groupId>org.scala-tools</groupId>        <artifactId>maven-scala-plugin</artifactId>        <configuration>          <scalaVersion>${scala.version}</scalaVersion>        </configuration>      </plugin>    </plugins>  </reporting></project>

4.2 连通Kafka

新建Scala文件——WebStatStreamingApp.scala，首先使用Direct模式连通Kafka：

package com.taipark.spark.project
import kafka.serializer.StringDecoderimport org.apache.spark.SparkConfimport org.apache.spark.streaming.kafka.KafkaUtilsimport org.apache.spark.streaming.{Seconds, StreamingContext}
/**  * 使用Spark Streaming消费Kafka的数据  */object WebStatStreamingApp {  def main(args: Array[String]): Unit = {
    if(args.length != 2){      System.err.println("Userage:WebStatStreamingApp <brokers> <topics>");      System.exit(1);    }    val Array(brokers,topics) = args
    val sparkConf = new SparkConf()      .setAppName("WebStatStreamingApp")      .setMaster("local[2]")    val ssc = new StreamingContext(sparkConf,Seconds(60))
    val kafkaParams = Map[String,String]("metadata.broker.list"-> brokers)    val topicSet = topics.split(",").toSet    val messages = KafkaUtils      .createDirectStream[String,String,StringDecoder,StringDecoder](      ssc,kafkaParams,topicSet    )
    messages.map(_._2).count().print()
    ssc.start()    ssc.awaitTermination()
  }}

设定参数：

hadoop000:9092 streamingtopic

在本地测试是否连通：

连通成功，可以开始编写业务代码完成数据清洗(ETL)。

4.3 ETL

新建工具类DateUtils.scala：

package com.taipark.spark.project.utils
import java.util.Date
import org.apache.commons.lang3.time.FastDateFormat
/**  * 日期时间工具类  */object DateUtils {  val YYYYMMDDHHMMSS_FORMAT = FastDateFormat.getInstance("yyyy-MM-dd HH:mm:ss")  val TARGET_FORMAT = FastDateFormat.getInstance("yyyyMMddHHmmss")
  def getTime(time:String)={    YYYYMMDDHHMMSS_FORMAT.parse(time).getTime  }
  def parseToMinute(time:String)={    TARGET_FORMAT.format(new Date(getTime(time)))  }
  def main(args: Array[String]): Unit = {//    println(parseToMinute("2020-03-10 15:00:05"))  }}

新建ClickLog.scala：

package com.taipark.spark.project.domian
/**  * 清洗后的日志信息  */case class ClickLog(ip:String,time:String,courseId:Int,statusCode:Int,referer:String)

修改WebStatStreamingApp.scala：

package com.taipark.spark.project.spark
import com.taipark.spark.project.domian.ClickLogimport com.taipark.spark.project.utils.DateUtilsimport kafka.serializer.StringDecoderimport org.apache.spark.SparkConfimport org.apache.spark.streaming.kafka.KafkaUtilsimport org.apache.spark.streaming.{Seconds, StreamingContext}
/**  * 使用Spark Streaming消费Kafka的数据  */object WebStatStreamingApp {  def main(args: Array[String]): Unit = {
    if(args.length != 2){      System.err.println("Userage:WebStatStreamingApp <brokers> <topics>");      System.exit(1);    }    val Array(brokers,topics) = args
    val sparkConf = new SparkConf()      .setAppName("WebStatStreamingApp")      .setMaster("local[2]")    val ssc = new StreamingContext(sparkConf,Seconds(60))
    val kafkaParams = Map[String,String]("metadata.broker.list"-> brokers)    val topicSet = topics.split(",").toSet    val messages = KafkaUtils      .createDirectStream[String,String,StringDecoder,StringDecoder](      ssc,kafkaParams,topicSet    )
    //messages.map(_._2).count().print()
    //ETL//    30.163.55.7  2020-03-10 14:32:01  "GET /class/112.html HTTP/1.1"  404  http://www.baidu.com/s?wd=Hadoop    val logs = messages.map(_._2)    val cleanData = logs.map(line => {      val infos = line.split("\t")      //infos(2) = "GET /class/112.html HTTP/1.1"      val url = infos(2).split(" ")(1)      var courseId = 0
      //拿到课程编号      if(url.startsWith("/class")){        val courseIdHTML = url.split("/")(2)        courseId = courseIdHTML.substring(0,courseIdHTML.lastIndexOf(".")).toInt      }
      ClickLog(infos(0),DateUtils.parseToMinute(infos(1)),courseId,infos(3).toInt,infos(4))    }).filter(clicklog => clicklog.courseId != 0)
    cleanData.print()
    ssc.start()    ssc.awaitTermination()
  }}

run起来测试一下：

ETL完成。

4.4 功能一：到现在为止某网站的访问量

使用数据库来存储统计结果，可视化前端根据yyyyMMdd courseid把数据库里的结果展示出来。

选择HBASE作为数据库。要启动HDFS与Zookeeper。

启动HDFS：

./start-dfs.sh

启动HBASE：

./start-hbase.sh

./hbase shelllist

HBASE表设计：

create 'web_course_clickcount','info'

hbase(main):008:0> desc 'web_course_clickcount'Table web_course_clickcount is ENABLED                                                                 web_course_clickcount                                                                                  COLUMN FAMILIES DESCRIPTION                                                                            {NAME => 'info', BLOOMFILTER => 'ROW', VERSIONS => '1', IN_MEMORY => 'false', KEEP_DELETED_CELLS => 'FALSE', DATA_BLOCK_ENCODING => 'NONE', TTL => 'FOREVER', COMPRESSION => 'NONE', MIN_VERSIONS => '0', BLOCKCACHE => 'true', BLOCKSIZE => '65536', REPLICATION_SCOPE => '0'}                                      1 row(s) in 0.1650 seconds

Rowkey设计：

day_courseid

使用Scala来操作HBASE：

新建网页点击数实体类 CourseClickCount.scala：

package com.taipark.spark.project.domian
/**  * 课程网页点击数  * @param day_course HBASE中的rowkey  * @param click_count 对应的点击总数  */case class CourseClickCount(day_course:String,click_count:Long)

新建数据访问层 CourseClickCountDAO.scala：

package com.taipark.spark.project.dao
import com.taipark.spark.project.domian.CourseClickCount
import scala.collection.mutable.ListBuffer
object CourseClickCountDAO {  val tableName = "web_course_clickcount"  val cf = "info"  val qualifer = "click_count"
  /**    * 保存数据到HBASE    * @param list    */  def save(list:ListBuffer[CourseClickCount]): Unit ={
  }
  /**    * 根据rowkey查询值    * @param day_course    * @return    */  def count(day_course:String):Long={    0l  }}

利用Java实现HBaseUtils打通其与HBASE：

package com.taipark.spark.project.utils;
import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.hbase.client.HBaseAdmin;import org.apache.hadoop.hbase.client.HTable;import org.apache.hadoop.hbase.client.Put;import org.apache.hadoop.hbase.util.Bytes;
import java.io.IOException;
/** * HBase操作工具类:Java工具类采用单例模式封装 */public class HBaseUtils {    HBaseAdmin admin = null;    Configuration configuration = null;
    //私有构造方法(单例模式)    private HBaseUtils(){        configuration = new Configuration();        configuration.set("hbase.zookeeper.quorum",                "hadoop000:2181");        configuration.set("hbase.rootdir",                "hdfs://hadoop000:8020/hbase");
        try {            admin = new HBaseAdmin(configuration);        } catch (IOException e) {            e.printStackTrace();        }    }    private static HBaseUtils instance = null;
    public static synchronized HBaseUtils getInstance(){        if(instance == null){            instance = new HBaseUtils();        }        return instance;    }
    //根据表名获取HTable实例    public HTable getTable(String tableName){        HTable table = null;        try {            table = new HTable(configuration,tableName);        } catch (IOException e) {            e.printStackTrace();        }        return table;    }
    /**     * 添加一条记录到HBASE表     * @param tableName 表名     * @param rowkey    表rowkey     * @param cf    表的columnfamily     * @param column    表的列     * @param value     写入HBASE的值     */    public void put(String tableName,String rowkey,String cf,String column,String value){        HTable table = getTable(tableName);        Put put = new Put(Bytes.toBytes(rowkey));        put.add(Bytes.toBytes(cf),Bytes.toBytes(column),Bytes.toBytes(value));        try {            table.put(put);        } catch (IOException e) {            e.printStackTrace();        }    }
    public static void main(String[] args) {//        HTable hTable = HBaseUtils.getInstance().getTable("web_course_clickcount");//        System.out.println(hTable.getName().getNameAsString());        String tableName = "web_course_clickcount";        String rowkey = "20200310_88";        String cf = "info";        String column = "click_count";        String value = "2";        HBaseUtils.getInstance().put(tableName,rowkey,cf,column,value);
    }}

测试运行：

测试工具类成功后继续编写DAO的代码：

package com.taipark.spark.project.dao
import com.taipark.spark.project.domian.CourseClickCountimport com.taipark.spark.project.utils.HBaseUtilsimport org.apache.hadoop.hbase.client.Getimport org.apache.hadoop.hbase.util.Bytes
import scala.collection.mutable.ListBuffer
object CourseClickCountDAO {  val tableName = "web_course_clickcount"  val cf = "info"  val qualifer = "click_count"
  /**    * 保存数据到HBASE    * @param list    */  def save(list:ListBuffer[CourseClickCount]): Unit ={    val table = HBaseUtils.getInstance().getTable(tableName)
    for(ele <- list){      table.incrementColumnValue(        Bytes.toBytes(ele.day_course),        Bytes.toBytes(cf),        Bytes.toBytes(qualifer),        ele.click_count)    }  }
  /**    * 根据rowkey查询值    * @param day_course    * @return    */  def count(day_course:String):Long={    val table = HBaseUtils.getInstance().getTable(tableName)
    val get = new Get(Bytes.toBytes(day_course))    val value = table.get(get).getValue(cf.getBytes,qualifer.getBytes)
    if (value == null){      0L    }else{      Bytes.toLong(value)    }  }
  def main(args: Array[String]): Unit = {    val list = new ListBuffer[CourseClickCount]    list.append(CourseClickCount("2020311_8",8))    list.append(CourseClickCount("2020311_9",9))    list.append(CourseClickCount("2020311_10",1))    list.append(CourseClickCount("2020311_2",15))
    save(list)  }}

测试运行一下，用hbase shell查看：

scan 'web_course_clickcount'

将Spark Streaming处理结果写到HBASE中：

package com.taipark.spark.project.spark
import com.taipark.spark.project.dao.CourseClickCountDAOimport com.taipark.spark.project.domian.{ClickLog, CourseClickCount}import com.taipark.spark.project.utils.DateUtilsimport kafka.serializer.StringDecoderimport org.apache.spark.SparkConfimport org.apache.spark.streaming.kafka.KafkaUtilsimport org.apache.spark.streaming.{Seconds, StreamingContext}
import scala.collection.mutable.ListBuffer
/**  * 使用Spark Streaming消费Kafka的数据  */object WebStatStreamingApp {  def main(args: Array[String]): Unit = {
    if(args.length != 2){      System.err.println("Userage:WebStatStreamingApp <brokers> <topics>");      System.exit(1);    }    val Array(brokers,topics) = args
    val sparkConf = new SparkConf()      .setAppName("WebStatStreamingApp")      .setMaster("local[2]")    val ssc = new StreamingContext(sparkConf,Seconds(60))
    val kafkaParams = Map[String,String]("metadata.broker.list"-> brokers)    val topicSet = topics.split(",").toSet    val messages = KafkaUtils      .createDirectStream[String,String,StringDecoder,StringDecoder](      ssc,kafkaParams,topicSet    )
    //messages.map(_._2).count().print()
    //ETL//    30.163.55.7  2020-03-10 14:32:01  "GET /class/112.html HTTP/1.1"  404  http://www.baidu.com/s?wd=Hadoop    val logs = messages.map(_._2)    val cleanData = logs.map(line => {      val infos = line.split("\t")      //infos(2) = "GET /class/112.html HTTP/1.1"      val url = infos(2).split(" ")(1)      var courseId = 0
      //拿到课程编号      if(url.startsWith("/class")){        val courseIdHTML = url.split("/")(2)        courseId = courseIdHTML.substring(0,courseIdHTML.lastIndexOf(".")).toInt      }
      ClickLog(infos(0),DateUtils.parseToMinute(infos(1)),courseId,infos(3).toInt,infos(4))    }).filter(clicklog => clicklog.courseId != 0)
//    cleanData.print()
    cleanData.map(x => {      //HBase rowkey设计：20200311_9      ((x.time.substring(0,8)) + "_" + x.courseId,1)    }).reduceByKey(_+_).foreachRDD(rdd =>{      rdd.foreachPartition(partitionRecords =>{        val list = new ListBuffer[CourseClickCount]
        partitionRecords.foreach(pair =>{          list.append(CourseClickCount(pair._1,pair._2))        })
        CourseClickCountDAO.save(list)      })    })
    ssc.start()    ssc.awaitTermination()
  }}

测试：

4.5 功能二：到现在为止某网站的搜索引擎引流访问量

HBASE表设计：

create 'web_course_search_clickcount','info'

设计rowkey：

day_search_1

确定实体类：

package com.taipark.spark.project.domian
/**  * 网站从搜索引擎过来的点击数实体类  * @param day_search_course  * @param click_count  */case class CourseSearchClickCount (day_search_course:String,click_count:Long)

开发DAO CourseSearchClickCountDAO.scala：

package com.taipark.spark.project.dao
import com.taipark.spark.project.domian.{CourseClickCount, CourseSearchClickCount}import com.taipark.spark.project.utils.HBaseUtilsimport org.apache.hadoop.hbase.client.Getimport org.apache.hadoop.hbase.util.Bytes
import scala.collection.mutable.ListBuffer
object CourseSearchClickCountDAO {  val tableName = "web_course_search_clickcount"  val cf = "info"  val qualifer = "click_count"
  /**    * 保存数据到HBASE    * @param list    */  def save(list:ListBuffer[CourseSearchClickCount]): Unit ={    val table = HBaseUtils.getInstance().getTable(tableName)
    for(ele <- list){      table.incrementColumnValue(        Bytes.toBytes(ele.day_search_course),        Bytes.toBytes(cf),        Bytes.toBytes(qualifer),        ele.click_count)    }  }
  /**    * 根据rowkey查询值    * @param day_search_course    * @return    */  def count(day_search_course:String):Long={    val table = HBaseUtils.getInstance().getTable(tableName)
    val get = new Get(Bytes.toBytes(day_search_course))    val value = table.get(get).getValue(cf.getBytes,qualifer.getBytes)
    if (value == null){      0L    }else{      Bytes.toLong(value)    }  }
  def main(args: Array[String]): Unit = {    val list = new ListBuffer[CourseSearchClickCount]    list.append(CourseSearchClickCount("2020311_www.baidu.com_8",8))    list.append(CourseSearchClickCount("2020311_cn.bing.com_9",9))
    save(list)    println(count("020311_www.baidu.com_8"))  }}

测试：

在Spark Streaming中写到HBASE：

package com.taipark.spark.project.spark
import com.taipark.spark.project.dao.{CourseClickCountDAO, CourseSearchClickCountDAO}import com.taipark.spark.project.domian.{ClickLog, CourseClickCount, CourseSearchClickCount}import com.taipark.spark.project.utils.DateUtilsimport kafka.serializer.StringDecoderimport org.apache.spark.SparkConfimport org.apache.spark.streaming.kafka.KafkaUtilsimport org.apache.spark.streaming.{Seconds, StreamingContext}
import scala.collection.mutable.ListBuffer
/**  * 使用Spark Streaming消费Kafka的数据  */object WebStatStreamingApp {  def main(args: Array[String]): Unit = {
    if(args.length != 2){      System.err.println("Userage:WebStatStreamingApp <brokers> <topics>");      System.exit(1);    }    val Array(brokers,topics) = args
    val sparkConf = new SparkConf()      .setAppName("WebStatStreamingApp")      .setMaster("local[2]")    val ssc = new StreamingContext(sparkConf,Seconds(60))
    val kafkaParams = Map[String,String]("metadata.broker.list"-> brokers)    val topicSet = topics.split(",").toSet    val messages = KafkaUtils      .createDirectStream[String,String,StringDecoder,StringDecoder](      ssc,kafkaParams,topicSet    )
    //messages.map(_._2).count().print()
    //ETL//    30.163.55.7  2020-03-10 14:32:01  "GET /class/112.html HTTP/1.1"  404  http://www.baidu.com/s?wd=Hadoop    val logs = messages.map(_._2)    val cleanData = logs.map(line => {      val infos = line.split("\t")      //infos(2) = "GET /class/112.html HTTP/1.1"      val url = infos(2).split(" ")(1)      var courseId = 0
      //拿到课程编号      if(url.startsWith("/class")){        val courseIdHTML = url.split("/")(2)        courseId = courseIdHTML.substring(0,courseIdHTML.lastIndexOf(".")).toInt      }
      ClickLog(infos(0),DateUtils.parseToMinute(infos(1)),courseId,infos(3).toInt,infos(4))    }).filter(clicklog => clicklog.courseId != 0)
//    cleanData.print()
    //需求一    cleanData.map(x => {      //HBase rowkey设计：20200311_9      ((x.time.substring(0,8)) + "_" + x.courseId,1)    }).reduceByKey(_+_).foreachRDD(rdd =>{      rdd.foreachPartition(partitionRecords =>{        val list = new ListBuffer[CourseClickCount]
        partitionRecords.foreach(pair =>{          list.append(CourseClickCount(pair._1,pair._2))        })
        CourseClickCountDAO.save(list)      })    })
    //需求二    cleanData.map(x =>{      //http://www.baidu.com/s?wd=Spark+Streaming      val referer = x.referer.replaceAll("//","/")      //http:/www.baidu.com/s?wd=Spark+Streaming      val splits = referer.split("/")      var host = ""      //splits.length == 1 => -      if(splits.length > 2){        host = splits(1)      }
      (host,x.courseId,x.time)    }).filter(_._1 != "").map(x =>{      (x._3.substring(0,8) + "_" + x._1 + "_" + x._2,1)    }).reduceByKey(_+_).foreachRDD(rdd =>{      rdd.foreachPartition(partitionRecords =>{        val list = new ListBuffer[CourseSearchClickCount]
        partitionRecords.foreach(pair =>{          list.append(CourseSearchClickCount(pair._1,pair._2))        })
        CourseSearchClickCountDAO.save(list)      })    })
    ssc.start()    ssc.awaitTermination()
  }}

测试：

5.生产环境部署

不要硬编码，把setAppName和setMaster注释掉：

  val sparkConf = new SparkConf()//      .setAppName("WebStatStreamingApp")//      .setMaster("local[2]")

Maven打包部署前，需要将pom中指定build目录的两行注释掉，以防报错：

    <!--    <sourceDirectory>src/main/scala</sourceDirectory>    <testSourceDirectory>src/test/scala</testSourceDirectory>    -->

Maven打包传到服务器：

利用spark-submit提交：

./spark-submit \--master local[5] \--name WebStatStreamingApp \--class com.taipark.spark.project.spark.WebStatStreamingApp \/home/hadoop/tplib/sparktrain-1.0.jar \hadoop000:9092 streamingtopic

报错：

Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/spark/streaming/kafka/KafkaUtils$

修改，添加jar包spark-streaming-kafka-0-8_2.11：

./spark-submit \--master local[5] \--name WebStatStreamingApp \--class com.taipark.spark.project.spark.WebStatStreamingApp \--packages org.apache.spark:spark-streaming-kafka-0-8_2.11:2.2.0 \/home/hadoop/tplib/sparktrain-1.0.jar \hadoop000:9092 streamingtopic

报错：

java.lang.NoClassDefFoundError: org/apache/hadoop/hbase/client/HBaseAdmin

修改，增加HBASE的jar包：

./spark-submit \--master local[5] \--name WebStatStreamingApp \--class com.taipark.spark.project.spark.WebStatStreamingApp \--packages org.apache.spark:spark-streaming-kafka-0-8_2.11:2.2.0 \--jars $(echo /home/hadoop/app/hbase-1.2.0-cdh5.7.0/lib/*.jar | tr ' ' ',') \/home/hadoop/tplib/sparktrain-1.0.jar \hadoop000:9092 streamingtopic

运行：

后台运行成功

6.Spring Boot开发

6.1 测试ECharts

新建一个Spring Boot项目，下载ECharts，利用其在线编译，获得echarts.min.js，放在resources/static/js下

pox.xml添加一个依赖：

        <dependency>            <groupId>org.springframework.boot</groupId>            <artifactId>spring-boot-starter-thymeleaf</artifactId>        </dependency>

resources/templates里做一个test.html：

<!DOCTYPE html><html lang="en"><head>    <meta charset="UTF-8">    <title>test</title>    <!-- 引入 ECharts 文件 -->    <script src="js/echarts.min.js"></script></head><body><!-- 为 ECharts 准备一个具备大小（宽高）的 DOM --><div id="main" style="width: 600px;height:400px;"></div><script type="text/javascript">    // 基于准备好的dom，初始化echarts实例    var myChart = echarts.init(document.getElementById('main'));
    // 指定图表的配置项和数据    var option = {        title: {            text: 'ECharts 入门示例'        },        tooltip: {},        legend: {            data:['销量']        },        xAxis: {            data: ["衬衫","羊毛衫","雪纺衫","裤子","高跟鞋","袜子"]        },        yAxis: {},        series: [{            name: '销量',            type: 'bar',            data: [5, 20, 36, 10, 10, 20]        }]    };
    // 使用刚指定的配置项和数据显示图表。    myChart.setOption(option);</script></body></html>

新建java文件：

package com.taipark.spark.web;
import org.springframework.web.bind.annotation.RequestMapping;import org.springframework.web.bind.annotation.RequestMethod;import org.springframework.web.bind.annotation.RestController;import org.springframework.web.servlet.ModelAndView;
/** * 测试 */@RestControllerpublic class HelloBoot {    @RequestMapping(value = "/hello",method = RequestMethod.GET)    public String sayHello(){        return "HelloWorld!";    }
    @RequestMapping(value = "/first",method = RequestMethod.GET)    public ModelAndView firstDemo(){        return new ModelAndView("test");    }}

测试一下：

成功

6.2 动态实现ECharts

添加repository：

    <repositories>        <repository>            <id>cloudera</id>            <url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>        </repository>    </repositories>

添加依赖：

        <dependency>            <groupId>org.apache.hbase</groupId>            <artifactId>hbase-client</artifactId>            <version>1.2.0-cdh5.7.0</version>        </dependency>

创建HBaseUtils.java：

package com.taipark.spark.web.utils;
import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.hbase.client.*;import org.apache.hadoop.hbase.filter.Filter;import org.apache.hadoop.hbase.filter.PrefixFilter;import org.apache.hadoop.hbase.util.Bytes;

import java.io.IOException;import java.util.HashMap;import java.util.Map;
public class HBaseUtils {    HBaseAdmin admin = null;    Configuration configuration = null;
    //私有构造方法(单例模式)    private HBaseUtils(){        configuration = new Configuration();        configuration.set("hbase.zookeeper.quorum",                "hadoop000:2181");        configuration.set("hbase.rootdir",                "hdfs://hadoop000:8020/hbase");
        try {            admin = new HBaseAdmin(configuration);        } catch (IOException e) {            e.printStackTrace();        }    }    private static HBaseUtils instance = null;
    public static synchronized HBaseUtils getInstance(){        if(instance == null){            instance = new HBaseUtils();        }        return instance;    }
    //根据表名获取HTable实例    public HTable getTable(String tableName){        HTable table = null;        try {            table = new HTable(configuration,tableName);        } catch (IOException e) {            e.printStackTrace();        }        return table;    }
    /**     * 根据表名和输入条件获取HBASE的记录数     * @param tableName     * @param dayCourse     * @return     */    public Map<String,Long> query(String tableName,String condition) throws Exception{        Map<String,Long> map = new HashMap<>();
        HTable table = getTable(tableName);        String cf ="info";        String qualifier = "click_count";
        Scan scan = new Scan();        Filter filter = new PrefixFilter(Bytes.toBytes(condition));        scan.setFilter(filter);        ResultScanner rs = table.getScanner(scan);        for(Result result:rs){            String row = Bytes.toString(result.getRow());            long clickCount = Bytes.toLong(result.getValue(cf.getBytes(), qualifier.getBytes()));            map.put(row,clickCount);        }        return map;    }
    public static void main(String[] args) throws Exception{        Map<String, Long> map = HBaseUtils.getInstance().query("web_course_clickcount", "20200311");
        for(Map.Entry<String,Long> entry:map.entrySet()){            System.out.println(entry.getKey() + ":" + entry.getValue());        }    }}

测试通过：

定义网页访问数量Bean：

package com.taipark.spark.web.domain;
import org.springframework.stereotype.Component;
/** * 网页访问数量实体类 */@Componentpublic class CourseClickCount {
    private String name;    private long value;
    public String getName() {        return name;    }
    public void setName(String name) {        this.name = name;    }
    public long getValue() {        return value;    }
    public void setValue(long value) {        this.value = value;    }}

DAO层：

package com.taipark.spark.web.dao;
import com.taipark.spark.web.domain.CourseClickCount;import com.taipark.spark.web.utils.HBaseUtils;import org.springframework.stereotype.Component;

import java.util.ArrayList;import java.util.List;import java.util.Map;
/** * 网页访问数量数据访问层 */@Componentpublic class CourseClickDAO {    /**     * 根据天查询     * @param day     * @return     * @throws Exception     */    public List<CourseClickCount> query(String day) throws Exception{
        List<CourseClickCount> list = new ArrayList<>();        //去HBase表中根据day获取对应网页的访问量        Map<String, Long> map = HBaseUtils.getInstance().query("web_course_clickcount", "20200311");        for(Map.Entry<String,Long> entry:map.entrySet()){            CourseClickCount model = new CourseClickCount();            model.setName(entry.getKey());            model.setValue(entry.getValue());
            list.add(model);        }        return list;    }
    public static void main(String[] args) throws Exception{        CourseClickDAO dao = new CourseClickDAO();        List<CourseClickCount> list = dao.query( "20200311");
        for(CourseClickCount model:list){            System.out.println(model.getName() + ":" + model.getValue());        }    }}

使用JSON需要引入：

        <dependency>            <groupId>net.sf.json-lib</groupId>            <artifactId>json-lib</artifactId>            <version>2.4</version>            <classifier>jdk15</classifier>        </dependency>

Web层：

package com.taipark.spark.web.spark;
import com.taipark.spark.web.dao.CourseClickDAO;import com.taipark.spark.web.domain.CourseClickCount;import net.sf.json.JSONArray;import org.springframework.beans.factory.annotation.Autowired;import org.springframework.web.bind.annotation.RequestMapping;import org.springframework.web.bind.annotation.RequestMethod;import org.springframework.web.bind.annotation.ResponseBody;import org.springframework.web.bind.annotation.RestController;import org.springframework.web.servlet.ModelAndView;
import java.util.HashMap;import java.util.List;import java.util.Map;
/** * web层 */@RestControllerpublic class WebStatApp {
    private static Map<String,String> courses = new HashMap<>();    static {        courses.put("112","某些外国人对中国有多不了解？");        courses.put("128","你认为有哪些失败的建筑？");        courses.put("145","为什么人类想象不出四维空间？");        courses.put("146","有什么一眼看上去很舒服的头像？");        courses.put("131","男朋友心情不好时女朋友该怎么办？");        courses.put("130","小白如何从零开始运营一个微信公众号？");        courses.put("821","为什么有人不喜欢极简主义？");        courses.put("825","有哪些书看完后会让人很后悔没有早看到？");    }
//    @Autowired//    CourseClickDAO courseClickDAO;//    @RequestMapping(value = "/course_clickcount_dynamic",method = RequestMethod.GET)//    public ModelAndView courseClickCount() throws Exception{//        ModelAndView view = new ModelAndView("index");//        List<CourseClickCount> list = courseClickDAO.query("20200311");////        for(CourseClickCount model:list){//            model.setName(courses.get(model.getName().substring(9)));//        }//        JSONArray json = JSONArray.fromObject(list);////        view.addObject("data_json",json);////        return view;//    }
    @Autowired    CourseClickDAO courseClickDAO;    @RequestMapping(value = "/course_clickcount_dynamic",method = RequestMethod.POST)    @ResponseBody    public List<CourseClickCount> courseClickCount() throws Exception{        ModelAndView view = new ModelAndView("index");        List<CourseClickCount> list = courseClickDAO.query("20200311");        for(CourseClickCount model:list){            model.setName(courses.get(model.getName().substring(9)));        }
        return list;    }
    @RequestMapping(value = "/echarts",method = RequestMethod.GET)    public ModelAndView echarts(){        return new ModelAndView("echarts");    }}

下载JQuery，并放到static/js下，新建echarts.html：

<!DOCTYPE html><html lang="en"><head>    <meta charset="UTF-8">    <title>web_stat</title>    <!-- 引入 ECharts 文件 -->    <script src="js/echarts.min.js"></script>    <script src="js/jquery-3.4.1.min.js"></script></head><body><!-- 为 ECharts 准备一个具备大小（宽高）的 DOM --><div id="main" style="width: 960px;height:540px;position: absolute;top:50%;left:50%;margin-top: -200px;margin-left: -300px;"></div><script type="text/javascript">    // 基于准备好的dom，初始化echarts实例    var myChart = echarts.init(document.getElementById('main'));
    option = {        title: {            text: '某站点实时流处理访问量统计',            subtext: '网页访问次数',            left: 'center'        },        tooltip: {            trigger: 'item',            formatter: '{a} <br/>{b} : {c} ({d}%)'        },        legend: {            orient: 'vertical',            left: 'left'        },        series: [            {                name: '访问次数',                type: 'pie',                radius: '55%',                center: ['50%', '60%'],                data: (function () {                   var datas = [];                   $.ajax({                       type: "POST",                       url: "/taipark/course_clickcount_dynamic",                       dataType: "json",                       async: false,                       success: function (result) {                           for(var i=0;i<result.length;i++){                               datas.push({"value":result[i].value,                               "name":result[i].name})                           }                       }                   })                    return datas;                })(),                emphasis: {                    itemStyle: {                        shadowBlur: 10,                        shadowOffsetX: 0,                        shadowColor: 'rgba(0, 0, 0, 0.5)'                    }                }            }        ]    };    // 使用刚指定的配置项和数据显示图表。    myChart.setOption(option);</script></body></html>

测试一下：

6.3 Spring的服务器部署

Maven打包并上传服务器

java -jar web-0.0.1.jar

完成~

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