1.写入数据到hbase

首先要保证：
hadoop，zookeeper，hbase，flume，kafka都启动了
（1）进入hadoop的sbin目录启动hdfs

./start-dfs.sh

jps查看：

进入到hbase的bin目录下：

start-hbase.sh

jps查看：

2.创建hbase表

进入hbase命令行

hbase shell

list查看表单

list

数据库中设计好了一个表：‘imooc_course_clickcount’

# 创建表
create 'imooc_course_clickcount','info'

#显示表信息
desc 'imooc_course_clickcount'

#查看表中数据
scan 'imooc_course_clickcount'

3.scala操作hbase

新建一个pojo样例类：CourseClickCount

这里使用java开发了一个工具类访问hbase：

package com.qianliu.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工具类建议采用单例模式封装 */
public class HBaseUtils {


    HBaseAdmin admin = null;
    Configuration configuration = null;


    /** * 私有改造方法 */
    private HBaseUtils(){
        configuration = new Configuration();
        configuration.set("hbase.zookeeper.quorum", "192.168.48.138:2181");
        configuration.set("hbase.rootdir", "hdfs://192.168.48.138:8020/hbase");

        try {
            admin = new HBaseAdmin(configuration);
        } catch (IOException e) {
            e.printStackTrace();
        }
    }

    private static HBaseUtils instance = null;

    public  static synchronized HBaseUtils getInstance() {
        if(null == instance) {
            instance = new HBaseUtils();
        }
        return instance;
    }


    /** * 根据表名获取到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 HBase表名 * @param rowkey HBase表的rowkey * @param cf HBase表的columnfamily * @param column HBase表的列 * @param value 写入HBase表的值 */
    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 table = HBaseUtils.getInstance().getTable("imooc_course_clickcount");
        //System.out.println(table.getName().getNameAsString());

        String tableName = "imooc_course_clickcount" ;
        String rowkey = "20171111_88";
        String cf = "info" ;
        String column = "click_count";
        String value = "2";

        HBaseUtils.getInstance().put(tableName, rowkey, cf, column, value);
    }

}

scala可以借助这个java的HBaseUtils来访问数据库：

package com.qianliu.dao

import com.qianliu.domain.{CourseClickCount, CourseSearchClickCount}
import com.qianliu.utils.HBaseUtils
import org.apache.hadoop.hbase.client.Get
import org.apache.hadoop.hbase.util.Bytes

import scala.collection.mutable.ListBuffer

/** * 从搜索引擎过来的实战课程点击数-数据访问层 */
object CourseSearchClickCountDAO {

  val tableName = "imooc_course_search_clickcount"
  val cf = "info"
  val qualifer = "click_count"


  /** * 保存数据到HBase * * @param list CourseSearchClickCount集合 */
  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 */
  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("20171111_www.baidu.com_8",8))
    list.append(CourseSearchClickCount("20171111_cn.bing.com_9",9))

    save(list)

    println(count("20171111_www.baidu.com_8") + " : " + count("20171111_cn.bing.com_9"))
  }

}

测试HBaseUtils.java的访问类是否可以正常使用。

前后的两次：scan ‘imooc_course_clickcount’
表明数据hbase插入数据成功！

4.sparkstreaming的清洗的数据写入hbase

package com.qianliu

import com.qianliu.dao.CourseClickCountDAO
import com.qianliu.domain.{ClickLog, CourseClickCount}
import com.qianliu.utils.DateUtils
import org.apache.spark.SparkConf
import org.apache.spark.api.java.JavaSparkContext
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}

import scala.collection.mutable.ListBuffer

/** * Spark Streaming对接Kafka */
object KafkaStreamingApp {

  def main(args: Array[String]): Unit = {
    //判断输入的数据长度是否符合要去
    if(args.length != 4) {
      System.err.println("Usage: KafkaStreamingApp <zkQuorum> <group> <topics> <numThreads>")
    }

    //初始化输入的数据为Array
    val Array(zkQuorum, group, topics, numThreads) = args

    //初始化sparkcontext
    val sparkConf = new SparkConf().setAppName("KafkaReceiverWordCount")
      .setMaster("local[2]")
    sparkConf.set("spark.testing.memory", "512000000")
    //sparkstreamingcontext
    val ssc = new StreamingContext(sparkConf, Seconds(60))

    //将多个topic输入
    val topicMap = topics.split(",").map((_, numThreads.toInt)).toMap

    // TODO... Spark Streaming如何对接Kafka
    val messages = KafkaUtils.createStream(ssc, zkQuorum, group,topicMap)

    // TODO... 自己去测试为什么要取第二个
    //messages.map(_._2).count().print()

    //第一步：数据清洗：清洗出来的数据为(ip,时间戳，课程号，http状态码，url)
    val logs = messages.map(_._2)
    val cleanData = logs.map(line => {
      val infos = line.split("\t")

      // infos(2) = "GET /class/130.html HTTP/1.1"
      // url = /class/130.html
      val url = infos(2).split(" ")(1)
      var courseId = 0

      // 把实战课程的课程编号拿到了
      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)

    //打印清洗出来的日志
    //cleanData.print()

    //第二步，将清洗出来的数据拼接成数据库的数据类型
    //统计今天到现在为止实战课程的访问量

    cleanData.map(x => {

      // HBase rowkey设计： 20171111_88

      (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)
      })
    })


    //启动spark
    ssc.start()
    ssc.awaitTermination()
  }
}

运行前后的数据hbase中的数据：

表明已经写入到了hbase，而且数据为6条是因为我们伪造的课程只有六个。