基于Hadoop平台的电信客服数据的处理与分析④项目实现:任务16:数据采集/消费/存储

任务描述

"数据生产"的程序启动后,会持续向callLog.csv文件中写入模拟的通话记录。接下来,我们需要将这些实时的数据通过Flume采集到Kafka集群中,然后提供给HBase消费。
Flume :是Cloudera提供的一个高可用的,高可靠的,分布式的海量日志采集、聚合和传输的系统,Flume支持在日志系统中定制各类数据发送方,用于收集数据;同时,Flume提供对数据进行简单处理,并写到各种数据接受方(可定制)的能力。适合下游数据消费者不多的情况,适合数据安全性要求不高的操作,适合与Hadoop生态圈对接的操作。
Kafka :由Apache软件基金会开发的一个开源流处理平台。Kafka是一种高吞吐量的分布式发布订阅消息系统,它可以处理消费者规模的网站中的所有动作流数据。适合下游数据消费者较多的情况,适合数据安全性要求较高的操作。

在这里采用一种常见的组合模型:线上数据----Flume----Kafka----HBase(HDFS)。

任务指导

"数据生产"的程序启动后,会持续向callLog.csv文件中写入模拟的通话记录。接下来将这些实时的数据通过Flume采集到Kafka集群中,然后消费Kafka中的数据并存储到HBase中。

"数据采集/消费/存储"模块的流程图:

思路: 数据采集

  1. 启动ZooKeeper和Kafka集群;

  2. 创建Kafka主题;

  3. 配置Flume,监控日志文件或目录;

  4. 启动Flume收集数据发送到Kafka;

  5. 运行在"生产数据"小节中创建的日志生产脚本;

  6. 启动Kafka控制台消费者,用于测试对Kafka数据的消费;

  7. 编写Kafka的消费者代码,将Kafka中的数据存储到HBase中涉及的类:

1))HBaseConsumer消费Kafka中的数据存储到HBase

2)PropertiesUtil提取项目所需参数的辅助类

3)HBaseUtil封装HBase的DDL操作

4)HBaseDAO执行HBase具体执行DDL操作

5)ConnectionInstance与HBase建立连接

任务实现

1、启动ZooKeeper和Kafka集群

确定在master1、slave1、slave2启动ZooKeeper集群,如未启动通过以下命令启动:

[root@master1 ~]# zkServer.sh start
[root@slave1 ~]# zkServer.sh start
[root@slave2 ~]# zkServer.sh start

确定Kafka在master1已启动,如未启动通过以下命令启动:

[root@master1 ~]# kafka-server-start.sh -daemon $KAFKA_HOME/config/server.properties

2、创建Kafka主题:

[root@master1 ~]# kafka-topics.sh --create --zookeeper master1:2181,slave1:2181,slave2:2181 --replication-factor 1 --partitions 4 --topic calllog

查看创建的Kafka主题:

[root@master1 ~]# kafka-topics.sh --describe --zookeeper slave1:2181,slave2:2181,slave3:2181

3、配置Flume监控数据传送至Kafka主题

在master1进入Flume配置目录新建kafka-conf.properties文件

[root@master1 ~]# cd $FLUME_HOME/conf
[root@master1 conf]# touch kafka-conf.properties 

编辑kafka-conf.properties文件,配置内容如下:

exec-memory-kafka.sources = exec-source
exec-memory-kafka.channels = memory-channel
exec-memory-kafka.sinks = kafka-sink

exec-memory-kafka.sources.exec-source.type=exec
exec-memory-kafka.sources.exec-source.command=tail -F /opt/app/callLog.csv
exec-memory-kafka.sources.exec-source.channels=memory-channel

exec-memory-kafka.channels.memory-channel.type=memory
exec-memory-kafka.channels.memory-channel.capacity=10000
exec-memory-kafka.channels.memory-channel.transactionCapacity=100

exec-memory-kafka.sinks.kafka-sink.type= org.apache.flume.sink.kafka.KafkaSink
exec-memory-kafka.sinks.kafka-sink.brokerList=master1:9092
exec-memory-kafka.sinks.kafka-sink.topic=calllog
exec-memory-kafka.sinks.kafka-sink.serializer.class=kafka.serializer.StringEncoder
exec-memory-kafka.sinks.kafka-sink.channel=memory-channel

4、启动Flume收集数据后发送至Kafka

[root@master1 conf]# cd $FLUME_HOME
[root@master1 apache-flume-1.9.0-bin]# flume-ng agent -c ./conf/ -f ./conf/kafka-conf.properties -n exec-memory-kafka -Dflume.root.logger=INFO,console

5、运行在任务15创建的日志生产脚本

[root@master1 ~]# cd /opt/app/
[root@master1 app]# nohup sh productlog.sh &

6、启动Kafka控制台消费者,测试Flume信息的输入:

[root@slave1 ~]# kafka-console-consumer.sh --bootstrap-server master1:9092 --from-beginning --topic calllog

7、接下来编写操作HBase代码,用于消费Kafka数据,并将数据实时存储在HBase中,思路如下:

a) 编写Kafka消费者,读取Kafka集群中缓存的信息,并打印到控制台;

b) 如果读取Kafka中的数据可以打印到控制台,那么就可以编写调用HBase API的方法,将从Kafka中读取出来的数据写入到HBase;

c) 编写一些通用类,用于封装HBase操作的一些通用方法。

  1. 创建新的Maven项目

参考任务"数据生产"创建Maven项目ct_consumer

pom.xml文件配置:

<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/xsd/maven-4.0.0.xsd">
      <modelVersion>4.0.0</modelVersion>
      <groupId>com.qst </groupId>
      <artifactId>ct_consumer</artifactId>
      <version>1.0-SNAPSHOT</version>
        
    <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
    </properties>

    <dependencies>
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.12</version>
            <scope>test</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>2.4.1</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-client</artifactId>
            <version>2.3.5</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase</artifactId>
            <version>2.3.5</version>
            <type>pom</type>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-server</artifactId>
            <version>2.3.5</version>
        </dependency>

    </dependencies>

    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-surefire-plugin</artifactId>
                <version>2.12.4</version>
                <configuration>
                    <skipTests>true</skipTests>
                </configuration>
            </plugin>
        </plugins>
    </build>
</project>

如图为项目创建对应的包和类

  1. HBaseConsumer类:主要用于消费Kafka中缓存的数据,然后调用HBase API持久化数据,将数据保存到HBase

    package kafka;

    import hbase.HBaseDAO;
    import org.apache.kafka.clients.consumer.ConsumerRecord;
    import org.apache.kafka.clients.consumer.ConsumerRecords;
    import org.apache.kafka.clients.consumer.KafkaConsumer;
    import utils.PropertiesUtil;

    import java.util.Arrays;

    public class HBaseConsumer {
    public static void main(String[] args) {
    KafkaConsumer<String, String> kafkaConsumer = new KafkaConsumer<String, String>(PropertiesUtil.properties);
    kafkaConsumer.subscribe(Arrays.asList(PropertiesUtil.getProperty("kafka.topics")));

         HBaseDAO hd = new HBaseDAO();
         while(true){
             ConsumerRecords<String, String> records = kafkaConsumer.poll(100);
             for(ConsumerRecord<String, String> cr : records){
                 String oriValue = cr.value();
                 System.out.println(oriValue);
                 hd.put(oriValue);
             }
         }
     }
    

    }

  2. PropertiesUtil类:以解耦合的方式,从文件中读取项目所需的参数,方便进行配置

    package utils;

    import java.io.IOException;
    import java.io.InputStream;
    import java.util.Properties;

    public class PropertiesUtil {
    public static Properties properties = null;

     static{
         InputStream is = ClassLoader.getSystemResourceAsStream("hbase_consumer.properties");
         properties = new Properties();
         try {
             properties.load(is);
         } catch (IOException e) {
             e.printStackTrace();
         }
     }
    
     public static String getProperty(String key){
         return properties.getProperty(key);
     }
    

    }

在resources目录下创建hbase_consumer.properties文件,并配置如下

# 设置kafka的brokerlist
bootstrap.servers=master1:9092
# 设置消费者所属的消费组
group.id=hbase_consumer_group
# 设置是否自动确认offset
enable.auto.commit=true
# 自动确认offset的时间间隔
auto.commit.interval.ms=30000
# 设置key,value的反序列化类的全名
key.deserializer=org.apache.kafka.common.serialization.StringDeserializer
value.deserializer=org.apache.kafka.common.serialization.StringDeserializer

# 以下为自定义属性设置
# 设置本次消费的主题
kafka.topics=calllog

# 设置HBase的一些变量
hbase.zookeeper.quorum=slave1:2181,slave2:2181,slave3:2181
hbase.calllog.regions=3
hbase.calllog.namespace=ns_ct
hbase.calllog.tablename=ns_ct:calllog
hbase.regions.count=3

在resources目录下创建log4j.properties文件,并配置如下

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Define some default values that can be overridden by system properties
hbase.root.logger=INFO,console
hbase.security.logger=INFO,console
hbase.log.dir=.
hbase.log.file=hbase.log

# Define the root logger to the system property "hbase.root.logger".
log4j.rootLogger=${hbase.root.logger}

# Logging Threshold
log4j.threshold=ALL

#
# Daily Rolling File Appender
#
log4j.appender.DRFA=org.apache.log4j.DailyRollingFileAppender
log4j.appender.DRFA.File=${hbase.log.dir}/${hbase.log.file}

# Rollver at midnight
log4j.appender.DRFA.DatePattern=.yyyy-MM-dd

# 30-day backup
#log4j.appender.DRFA.MaxBackupIndex=30
log4j.appender.DRFA.layout=org.apache.log4j.PatternLayout

# Pattern format: Date LogLevel LoggerName LogMessage
log4j.appender.DRFA.layout.ConversionPattern=%d{ISO8601} %-5p [%t] %c{2}: %m%n

# Rolling File Appender properties
hbase.log.maxfilesize=256MB
hbase.log.maxbackupindex=20

# Rolling File Appender
log4j.appender.RFA=org.apache.log4j.RollingFileAppender
log4j.appender.RFA.File=${hbase.log.dir}/${hbase.log.file}

log4j.appender.RFA.MaxFileSize=${hbase.log.maxfilesize}
log4j.appender.RFA.MaxBackupIndex=${hbase.log.maxbackupindex}

log4j.appender.RFA.layout=org.apache.log4j.PatternLayout
log4j.appender.RFA.layout.ConversionPattern=%d{ISO8601} %-5p [%t] %c{2}: %m%n

#
# Security audit appender
#
hbase.security.log.file=SecurityAuth.audit
hbase.security.log.maxfilesize=256MB
hbase.security.log.maxbackupindex=20
log4j.appender.RFAS=org.apache.log4j.RollingFileAppender
log4j.appender.RFAS.File=${hbase.log.dir}/${hbase.security.log.file}
log4j.appender.RFAS.MaxFileSize=${hbase.security.log.maxfilesize}
log4j.appender.RFAS.MaxBackupIndex=${hbase.security.log.maxbackupindex}
log4j.appender.RFAS.layout=org.apache.log4j.PatternLayout
log4j.appender.RFAS.layout.ConversionPattern=%d{ISO8601} %p %c: %m%n
log4j.category.SecurityLogger=${hbase.security.logger}
log4j.additivity.SecurityLogger=false
#log4j.logger.SecurityLogger.org.apache.hadoop.hbase.security.access.AccessController=TRACE
#log4j.logger.SecurityLogger.org.apache.hadoop.hbase.security.visibility.VisibilityController=TRACE

#
# Null Appender
#
log4j.appender.NullAppender=org.apache.log4j.varia.NullAppender

#
# console
# Add "console" to rootlogger above if you want to use this
#
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.err
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{ISO8601} %-5p [%t] %c{2}: %m%n

log4j.appender.asyncconsole=org.apache.hadoop.hbase.AsyncConsoleAppender
log4j.appender.asyncconsole.target=System.err

# Custom Logging levels

log4j.logger.org.apache.zookeeper=INFO
#log4j.logger.org.apache.hadoop.fs.FSNamesystem=DEBUG
log4j.logger.org.apache.hadoop.hbase=INFO
# Make these two classes INFO-level. Make them DEBUG to see more zk debug.
log4j.logger.org.apache.hadoop.hbase.zookeeper.ZKUtil=INFO
log4j.logger.org.apache.hadoop.hbase.zookeeper.ZooKeeperWatcher=INFO
#log4j.logger.org.apache.hadoop.dfs=DEBUG
# Set this class to log INFO only otherwise its OTT
# Enable this to get detailed connection error/retry logging.
# log4j.logger.org.apache.hadoop.hbase.client.HConnectionManager$HConnectionImplementation=TRACE


# Uncomment this line to enable tracing on _every_ RPC call (this can be a lot of output)
#log4j.logger.org.apache.hadoop.ipc.HBaseServer.trace=DEBUG

# Uncomment the below if you want to remove logging of client region caching'
# and scan of hbase:meta messages
# log4j.logger.org.apache.hadoop.hbase.client.HConnectionManager$HConnectionImplementation=INFO
# log4j.logger.org.apache.hadoop.hbase.client.MetaScanner=INFO

# Prevent metrics subsystem start/stop messages (HBASE-17722)
log4j.logger.org.apache.hadoop.metrics2.impl.MetricsConfig=WARN
log4j.logger.org.apache.hadoop.metrics2.impl.MetricsSinkAdapter=WARN
log4j.logger.org.apache.hadoop.metrics2.impl.MetricsSystemImpl=WARN
  1. HBaseUtil类:主要用于封装HBase的常用DDL操作,如:创建命名空间、创建表等

    package utils;

    import org.apache.hadoop.conf.Configuration;
    import org.apache.hadoop.hbase.HColumnDescriptor;
    import org.apache.hadoop.hbase.HTableDescriptor;
    import org.apache.hadoop.hbase.NamespaceDescriptor;
    import org.apache.hadoop.hbase.TableName;
    import org.apache.hadoop.hbase.client.Admin;
    import org.apache.hadoop.hbase.client.Connection;
    import org.apache.hadoop.hbase.client.ConnectionFactory;
    import org.apache.hadoop.hbase.util.Bytes;

    import java.io.IOException;
    import java.text.DecimalFormat;
    import java.util.Arrays;
    import java.util.Iterator;
    import java.util.TreeSet;

    public class HBaseUtil {
    /**
    * 判断表是否存在
    * @param conf HBaseConfiguration
    * @param tableName
    * @return
    */
    public static boolean isExistTable(Configuration conf, String tableName) throws IOException {
    Connection connection = ConnectionFactory.createConnection(conf);
    Admin admin = connection.getAdmin();
    boolean result = admin.tableExists(TableName.valueOf(tableName));

         admin.close();
         connection.close();
    
         return result;
     }
    
     /**
      * 初始化命名空间
      * @param conf
      * @param namespace
      */
     public static void initNamespace(Configuration conf, String namespace) throws IOException {
         Connection connection = ConnectionFactory.createConnection(conf);
         Admin admin = connection.getAdmin();
    
         NamespaceDescriptor nd = NamespaceDescriptor
                 .create(namespace)
                 .addConfiguration("CREATE_TIME", String.valueOf(System.currentTimeMillis()))
                 .addConfiguration("AUTHOR", "Zhang San")
                 .build();
    
         admin.createNamespace(nd);
    
         admin.close();
         connection.close();
     }
    
     /**
      * 创建表:协处理器
      * @param conf
      * @param tableName
      * @param columnFamily
      * @throws IOException
      */
     public static void createTable(Configuration conf, String tableName, int regions, String... columnFamily) throws IOException {
         Connection connection = ConnectionFactory.createConnection(conf);
         Admin admin = connection.getAdmin();
    
         if(isExistTable(conf, tableName)) return;
    
         HTableDescriptor htd = new HTableDescriptor(TableName.valueOf(tableName));
         for(String cf: columnFamily){
             htd.addFamily(new HColumnDescriptor(cf));
         }
         htd.addCoprocessor("hbase.CalleeWriteObserver");
         admin.createTable(htd, genSplitKeys(regions));
         admin.close();
         connection.close();
     }
    
     private static byte[][] genSplitKeys(int regions){
         //定义一个存放分区键的数组
         String[] keys = new String[regions];
         //目前推算,region个数不会超过2位数,所以region分区键格式化为两位数字所代表的字符串
         DecimalFormat df = new DecimalFormat("00");
         for(int i = 0; i < regions; i ++){
             keys[i] = df.format(i) + "|";
         }
    
         byte[][] splitKeys = new byte[regions][];
         //生成byte[][]类型的分区键的时候,一定要保证分区键是有序的
         TreeSet<byte[]> treeSet = new TreeSet<byte[]>(Bytes.BYTES_COMPARATOR);
         for(int i = 0; i < regions; i++){
             treeSet.add(Bytes.toBytes(keys[i]));
         }
    
         Iterator<byte[]> splitKeysIterator = treeSet.iterator();
         int index = 0;
         while(splitKeysIterator.hasNext()){
             byte[] b = splitKeysIterator.next();
             splitKeys[index ++] = b;
         }
         return splitKeys;
     }
    
     /**
      * 生成rowkey
      * regionCode_call1_buildTime_call2_flag_duration
      * @return
      */
     public static String genRowKey(String regionCode, String call1, String buildTime, String call2, String flag, String duration){
         StringBuilder sb = new StringBuilder();
         sb.append(regionCode + "_")
                 .append(call1 + "_")
                 .append(buildTime + "_")
                 .append(call2 + "_")
                 .append(flag + "_")
                 .append(duration);
         return sb.toString();
     }
    
     /**
      * 手机号:15837312345
      * 通话建立时间:2023-01-10 11:20:30 -> 20170110112030
      * @param call1
      * @param buildTime
      * @param regions
      * @return
      */
     public static String genRegionCode(String call1, String buildTime, int regions){
         int len = call1.length();
         //取出后4位号码
         String lastPhone = call1.substring(len - 4);
         //取出年月
         String ym = buildTime
                 .replaceAll("-", "")
                 .replaceAll(":", "")
                 .replaceAll(" ", "")
                 .substring(0, 6);
         //离散操作1
         Integer x = Integer.valueOf(lastPhone) ^ Integer.valueOf(ym);
    
         //离散操作2
         int y = x.hashCode();
         //生成分区号
         int regionCode = y % regions;
         //格式化分区号
         DecimalFormat df = new DecimalFormat("00");
         return  df.format(regionCode);
     }
    

    }

上面代码中的HBase协处理器"htd.addCoprocessor("hbase.CalleeWriteObserver");",具体代码在下面步骤中给出。

  1. HBaseDAO类:主要用于执行具体的DML操作,如保存数据、查询数据、Rowkey生成规则等

    package hbase;

    import org.apache.hadoop.conf.Configuration;
    import org.apache.hadoop.hbase.HBaseConfiguration;
    import org.apache.hadoop.hbase.TableName;
    import org.apache.hadoop.hbase.client.*;
    import org.apache.hadoop.hbase.util.Bytes;
    import utils.ConnectionInstance;
    import utils.HBaseUtil;
    import utils.PropertiesUtil;

    import java.io.IOException;
    import java.text.ParseException;
    import java.text.SimpleDateFormat;
    import java.util.ArrayList;
    import java.util.List;

    public class HBaseDAO {
    private int regions;
    private String namespace;
    private String tableName;
    public static final Configuration conf;
    private HTable table;
    private Connection connection;
    private SimpleDateFormat sdf1 = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
    private SimpleDateFormat sdf2 = new SimpleDateFormat("yyyyMMddHHmmss");

     private List<Put> cacheList = new ArrayList<Put>();
     static {
         conf = HBaseConfiguration.create();
         String zookeeperQuorum = PropertiesUtil.getProperty("hbase.zookeeper.quorum");
         conf.set("hbase.zookeeper.quorum", zookeeperQuorum);
     }
    
     public HBaseDAO() {
         try {
             regions = Integer.valueOf(PropertiesUtil.getProperty("hbase.calllog.regions"));
             namespace = PropertiesUtil.getProperty("hbase.calllog.namespace");
             tableName = PropertiesUtil.getProperty("hbase.calllog.tablename");
    
             if (!HBaseUtil.isExistTable(conf, tableName)) {
                 HBaseUtil.initNamespace(conf, namespace);
                 HBaseUtil.createTable(conf, tableName, regions, "f1", "f2");
             }
         } catch (IOException e) {
             e.printStackTrace();
         }
     }
    
     /**
      * ori数据样式: 18576581848,17269452013,2017-08-14 13:38:31,1761
      * rowkey样式:01_18576581848_20170814133831_17269452013_1_1761
      * HBase表的列:call1  call2   build_time   build_time_ts   flag   duration
      * @param ori
      */
     public void put(String ori) {
         try {
             if(cacheList.size() == 0){
                 connection = ConnectionInstance.getConnection(conf);
                 table = (HTable) connection.getTable(TableName.valueOf(tableName));
    

    // table.setAutoFlushTo(false);
    // table.setWriteBufferSize(2 * 1024 * 1024);
    }

             String[] splitOri = ori.split(",");
             String caller = splitOri[0];
             String callee = splitOri[1];
             String buildTime = splitOri[2];
             String duration = splitOri[3];
             String regionCode = HBaseUtil.genRegionCode(caller, buildTime, regions);
    
             String buildTimeReplace = sdf2.format(sdf1.parse(buildTime));
             String buildTimeTs = String.valueOf(sdf1.parse(buildTime).getTime());
    
             //生成rowkey
             String rowkey = HBaseUtil.genRowKey(regionCode, caller, buildTimeReplace, callee, "1", duration);
             //向表中插入该条数据
             Put put = new Put(Bytes.toBytes(rowkey));
             put.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("call1"), Bytes.toBytes(caller));
             put.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("call2"), Bytes.toBytes(callee));
             put.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("build_time"), Bytes.toBytes(buildTime));
             put.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("build_time_ts"), Bytes.toBytes(buildTimeTs));
             put.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("flag"), Bytes.toBytes("1"));
             put.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("duration"), Bytes.toBytes(duration));
    
             cacheList.add(put);
    
             if(cacheList.size() >= 30){
                 table.put(cacheList);
    
                 table.close();
                 cacheList.clear();
             }
         } catch (IOException e) {
             e.printStackTrace();
         } catch (ParseException e) {
             e.printStackTrace();
         }
     }
    

    }

6)ConnectionInstance类:主要负责建立HBase连接

package utils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.client.Connection;
import org.apache.hadoop.hbase.client.ConnectionFactory;
import java.io.IOException;
public class ConnectionInstance {
   private static Connection conn;
   public static synchronized Connection getConnection(Configuration conf) {
       try {
           if(conn == null || conn.isClosed()){
               conn = ConnectionFactory.createConnection(conf);
           }
       } catch (IOException e) {
           e.printStackTrace();
       }
       return conn;
   }
}

7)优化HBase数据存储方案,编写协处理器

在使用HBase查询数据时,尽量使用RowKey去定位数据,而非使用ColumnValueFilter或者SingleColumnValueFilter,因为在数据量较大的情况下Filter如果涉及到全表扫描时,效率是非常低的,所以在范围查询时尽量不要使用Filter,而是使用RowKey。

在项目中为了能够让数据尽量离散化,从而避免数据倾斜的发生,我们指定了若干的分区键,为了能让数据根据行键尽可能的分散到各分区当中,在这里行键的生成规则为:regionCode_call1_buildTime_call2_flag_duration,其中regionCode是分区号决定了数据会落入哪一个分区,也决定了是否会发生数据倾斜,regionCode的生成是使用第一个手机号(call1)的后4位和通话时间(年/月)经过两次离散操作,然后再和分区数取余生成的,这样就能保证每个月的通话记录都保存在同一个分区中。

在执行HBase数据查询,设置查询范围时,实际上regionCode生成规则是通过第一个手机号和通话时间(年/月)生成的,所以如果一个人在一个月内如果只接电话,而没有打电话的话,实际上是查不到通话记录的。解决这个问题的方法很多,现在用的比较多的方式是:以存储换效率,也就是说为了保证查询效率可以牺牲一定的存储空间。具体的做法是:将一条记录的第一个号码(call1)和第二个号码(call2)换一个位置,再次插入到数据表中,这样一条记录就会变成两条记录,即一条主叫记录和一条被叫记录。

我们可以在插入数据时,同时插入两条数据。在这里我们也可以使用协处理器为完成,即:当插入一条主叫记录时,会触发协处理器同时插入一条被叫记录。使用协处理器的目的一是可以启动两个线程来完成数据插入工作,第二、也可以让代码更加解耦、更清晰,方便管理。

在上面 步骤的HBaseUtil代码中 添加了协处理器。

  1. 什么是协处理器;
  2. 协处理器的特性;
  3. 协处理器的应用场景;
  4. 协处理器的分类;
  5. 怎么使用协处理器。

思路:

  1. 编写协处理器,用于协助处理HBase的相关操作,如增删改查等;
  2. 当一条主叫日志成功插入后,在协处理器中,将该日志切换为被叫视角再次插入一次,放到与主叫日志不同的列族中;
  3. 在创建HBase表时,为该表设置协处理器("htd.addCoprocessor("hbase.CalleeWriteObserver");");
  4. 上传协处理器的jar包到HDFS;
  5. 在HBase命令行中设置表的协处理器。

新建协处理器类:CalleeWriteObserver,并重写postPut方法,该方法会在数据成功插入之后被回调:

package hbase;

import org.apache.hadoop.hbase.CoprocessorEnvironment;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.Durability;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.client.Table;
import org.apache.hadoop.hbase.coprocessor.ObserverContext;
import org.apache.hadoop.hbase.coprocessor.RegionCoprocessor;
import org.apache.hadoop.hbase.coprocessor.RegionCoprocessorEnvironment;
import org.apache.hadoop.hbase.coprocessor.RegionObserver;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.hbase.wal.WALEdit;
import utils.HBaseUtil;
import utils.PropertiesUtil;

import java.io.IOException;
import java.text.ParseException;
import java.text.SimpleDateFormat;
import java.util.Optional;

public class CalleeWriteObserver implements RegionObserver, RegionCoprocessor {

    SimpleDateFormat sdf = new SimpleDateFormat("yyyyMMddHHmmss");
    private RegionCoprocessorEnvironment env = null;

    @Override
    public Optional<RegionObserver> getRegionObserver() {
        // Extremely important to be sure that the coprocessor is invoked as a RegionObserver
        return Optional.of(this);
    }

    @Override
    public void start(CoprocessorEnvironment e) throws IOException {
        env = (RegionCoprocessorEnvironment) e;
    }

    @Override
    public void stop(CoprocessorEnvironment e) throws IOException {
        // nothing to do here
    }

    @Override
    public void prePut(final ObserverContext<RegionCoprocessorEnvironment> e,
                       final Put put, final WALEdit edit, final Durability durability)
            throws IOException {

        //1、获取你想要操作的目标表的名称
//        String targetTableName = PropertiesUtil.getProperty("hbase.calllog.tablename");
        String targetTableName = "ns_ct:calllog";
        //2、获取当前成功Put了数据的表(不一定是我们当前业务想要操作的表)
        String currentTableName = e.getEnvironment().getRegionInfo().getTable().getNameAsString();

        if(!targetTableName.equals(currentTableName)) return;

        //01_18047140826_20180110154530_17864211243_1_0360
        String oriRowKey = Bytes.toString(put.getRow());
        String[] splitOriRowKey = oriRowKey.split("_");
        String oldFlag = splitOriRowKey[4];
        //如果当前插入的是被叫数据,则直接返回(因为默认提供的数据全部为主叫数据)
        if(oldFlag.equals("0")) return;

//        int regions = Integer.valueOf(PropertiesUtil.getProperty("hbase.calllog.regions"));
        int regions = 3;

        String caller = splitOriRowKey[1];
        String callee = splitOriRowKey[3];
        String buildTime = splitOriRowKey[2];
        String flag = "0";
        String duration = splitOriRowKey[5];
        String regionCode = HBaseUtil.genRegionCode(callee, buildTime, regions);

        String calleeRowKey = HBaseUtil.genRowKey(regionCode, callee, buildTime, caller, flag, duration);
        //生成时间戳
        String buildTimeTs = "";
        try {
            buildTimeTs = String.valueOf(sdf.parse(buildTime).getTime());
        } catch (ParseException e1) {
            e1.printStackTrace();
        }

        // call1 call2 build_time build_time_ts
        Put calleePut = new Put(Bytes.toBytes(calleeRowKey));
        calleePut.addColumn(Bytes.toBytes("f2"), Bytes.toBytes("call1"), Bytes.toBytes(callee));
        calleePut.addColumn(Bytes.toBytes("f2"), Bytes.toBytes("call2"), Bytes.toBytes(caller));
        calleePut.addColumn(Bytes.toBytes("f2"),Bytes.toBytes("build_time"), Bytes.toBytes(buildTime));
        calleePut.addColumn(Bytes.toBytes("f2"), Bytes.toBytes("flag"), Bytes.toBytes(flag));
        calleePut.addColumn(Bytes.toBytes("f2"), Bytes.toBytes("duration"), Bytes.toBytes(duration));
        calleePut.addColumn(Bytes.toBytes("f2"),Bytes.toBytes("build_time_ts"),Bytes.toBytes(buildTimeTs));
        Bytes.toBytes(100L);


//        Table table = e.getEnvironment().getTable(TableName.valueOf(targetTableName));
        Table table = e.getEnvironment().getConnection().getTable(TableName.valueOf(targetTableName));
        table.put(calleePut);
        table.close();
    }
}
  1. 运行测试

a) 在项目中选择右侧的Maven标签,双击Lifecycle->package对项目进行打包,当现实"BUILD SUCESS"后,在项目的target文件下将会生成ct_consumer-1.0-SNAPSHOT.jar文件

b) 在HBase表中应用协处理器

将jar包上传到HDFS中(供协处理器使用),执行以下代码:(jar包在ct_consumer项目所在的target目录中,请根据真实情况进入项目目录,此处jar默认在/root/IdeaProjects/ct_consumer/target目录中)

[root@master1 ~]# cd /root/IdeaProjects/ct_consumer/target
[root@master1 target]# hdfs dfs -mkdir -p /hbase/coprocessor/
[root@master1 target]# hdfs dfs -put ct_consumer-1.0-SNAPSHOT.jar /hbase/coprocessor/

启动HBase,如未启动【master1】使用如下命令启动:

[root@master1 ~]# start-hbase.sh

进入HBase命令行将协处理器应用到表,执行【hbase shell】进入HBase命令行,在命令行中执行以下代码:

hbase(main):001:0>  disable 'ns_ct:calllog'
hbase(main):002:0>  alter 'ns_ct:calllog', METHOD => 'table_att', 'coprocessor'=>'/hbase/coprocessor/ct_consumer-1.0-SNAPSHOT.jar|hbase.CalleeWriteObserver|100'
hbase(main):003:0>  enable 'ns_ct:calllog'
hbase(main):003:0>  quit

运行HBaseConsumerl类测试

进入HBase观察【ns_ct:calllog】表的数据

hbase(main):001:0> scan 'ns_ct:calllog',{STARTROW=> '0' , LIMIT => 10}

或者通过count命令查看当前表的总记录数量

hbase(main):002:0> count 'ns_ct:calllog'

如回显所示,在程序运行一定时间后,总记录条数为6720(建议运行一段时间程序以便在"数据展示"任务重得到更好的展示效果)

hbase(main):003:0> count 'ns_ct:calllog'
Current count: 1000, row: 00_16264433631_20171024123701_17269452013_1_1179                                             
Current count: 2000, row: 00_18576581848_20170704010025_15542823911_1_0229                                             
Current count: 3000, row: 01_15978226424_20170731100126_18468618874_1_0795                                             
Current count: 4000, row: 01_18468618874_20171122084724_13980337439_0_0176                                             
Current count: 5000, row: 02_15714728273_20170420175940_17005930322_1_1437                                             
Current count: 6000, row: 02_17526304161_20170714141822_17078388295_1_0325                                             
6720 row(s)
Took 0.6766 seconds                                                                                                    
=> 6720
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