HBase导入海量数据、使用BuckLoa向HBase中导入数据 13

- 日理万妓 2022-04-24 12:12 328阅读 0赞

# 前言 #

博主之前简单介绍了，HBase写入数据的原理与HBase表在hdfs上的存储结构，还搞了搞HBase提供的导入工具ImportTsv， 想了解更多ImportTsv使用，请戳[HBase导入海量数据之使用ImportTsv向HBase中导入大量数据][HBase_ImportTsv_HBase]

今天咱们了解下Buckload

如下图示，充分解释了Buckload的导入原理，通过MapReduce程序在hdfs中直接生成HFlie文件，然后将HFile文件移动到HBase中对应表的HDFS目录下  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjY0MTkwOQ_size_16_color_FFFFFF_t_70]

*  ImportTsv是命令行导入，Buckload可以自定义程序生成HFile，再进行导入，由此可见，BuckLoad比较灵活

# 1. BuckLoad导入数据到HBase，程序编写步骤 #

1.  编写mapper程序，**无论是map还是reduce，输出类型必须是<ImmutableBytesWritable,Put>或者<ImmutableBytesWritable,Keyvalue>**
2.  编写map方法，包含处理数据的逻辑。
3.  将处理后的数据写到hdfs中
4.  配置MapReduce任务的输入/输出格式，输入/输出类型，输入/输出目录等
5.  使用BuckLoad方式导入数据，有两种方法：
    
     *  代码： 创建LoadIncrementalHFiles对象，调用doBulkLoad方法，加载刚才MapReduce程序生成HFile到表中即可。  
        doBulkLoad有两种，HTable已经过时了，现在推荐使用第一种，

LoadIncrementalHFiles loader = new LoadIncrementalHFiles(conf);
    loader.doBulkLoad(new Path(OUTPUT_PATH),admin,table,connection,getRegionLocator(tableName.valueOf(tableName)))

*  命令行：在命令行中使用如下命令

HADOOP_CLASSPATH=`$HBASE_HOME/bin/hbase classpath` hadoop jar $HBASE_HOME/lib/hbase-server-version.jar completebulkload <生成的HFile路径> <表名称>

# 2. 实例展示 #

## 2.1 背景 ##

这有一个用户浏览网站的日志，分隔符为**逗号**，

共有四列：

1.  手机号反转（避免Region热点）
2.  手机号
3.  Mac地址
4.  用户访问记录（用&&分割）访问记录内容：时间戳-agent-访问目录-上行流量-下行流量

56279618741,14781697265,65:85:36:f9:b1:c0,
    1539787307-Mozilla/5.0 Macintosh; Intel Mac OS X 10_10_1 AppleWebKit/537.36 KHTML like Gecko Chrome/37.0.2062.124 Safari/537.36-https://dl.lianjia.com/ershoufang/102100802770.html-13660-6860
    &&1539786398-Mozilla/5.0 Windows NT 5.1 AppleWebKit/537.36 KHTML like Gecko Chrome/36.0.1985.67 Safari/537.36-https://dl.lianjia.com/ershoufang/102100576898.html-1959-91040
    &&1539785462-Mozilla/5.0 Windows NT 10.0 AppleWebKit/537.36 KHTML like Gecko Chrome/40.0.2214.93 Safari/537.36-https://dl.lianjia.com/ershoufang/102100762258.html-12177-53132

## 2.2 数据大小 ##

12.48GB，共1999940条数据

## 2.3 需求 ##

*  手机号反转做Rowkey,
 *  将手机号，Mac地址，用户访问地址插入到Info列簇的phoneNumber，macAddress，userView列中，
 *  将用户访问记录转为json格式

## 2.4 代码实现 ##

1.  编写Mapper程序和map方法

需要注意的是，要对rowkey的长度进行判断，筛选出rowkey长度大于0的，否则会报错

public static class BuckLoadMap extends Mapper<LongWritable, Text, ImmutableBytesWritable, Put> { 
            public void map(LongWritable key, Text value, Context context)
                    throws IOException, InterruptedException { 
                String[] dataLine = value.toString().split(",");
                // 手机号反转
                String phoneNumberReverse = dataLine[0];
                // 手机号
                String phoneNumber = dataLine[1];
                // mac地址
                String macAddress = dataLine[2];
                // 用户访问浏览历史
                String userViewHistory = dataLine[3];
                // 解析用户访问浏览历史
                String[] userViewHistoryParse = userViewHistory.split("&&");
                // 创建StringBuffer用户拼接json
                StringBuffer stringBuffer = new StringBuffer();
                stringBuffer.append("[");
                for (String view : userViewHistoryParse) { 
                    // 拼接json
                    String[] viewDetail = view.split("-");
                    String time = viewDetail[0];
                    String userAgent = viewDetail[1];
                    String visitUrl = viewDetail[2];
                    String upFlow = viewDetail[3];
                    String downFlow = viewDetail[4];
                    String json = "{\"time\":\"" + time + "\",\"userAgent\":\"" + userAgent + "\",\"visitUrl\":\"" + visitUrl + "\",\"upflow\":\"" + upFlow + "\",\"downFlow\":\"" + downFlow + "\"}";
                    stringBuffer.append(json);
                    stringBuffer.append(",");
                }
                stringBuffer.append("]");
                stringBuffer.deleteCharAt(stringBuffer.lastIndexOf(","));
                userViewHistory = stringBuffer.toString();
                // 将手机号反转作为rowkey
                ImmutableBytesWritable rowkey = new ImmutableBytesWritable(phoneNumberReverse.getBytes());
                // 筛选出rowkey为0的rowkey，某则导入的时候会报错
                if (rowkey.getLength()>0){ 
                    // 将其他列数据插入到对应列族中
                    Put put = new Put(phoneNumberReverse.getBytes());
                    put.addColumn("info".getBytes(), "phoneNumber".getBytes(), phoneNumber.getBytes());
                    put.addColumn("info".getBytes(), "macAddress".getBytes(), macAddress.getBytes());
                    put.addColumn("info".getBytes(), "userViewHistory".getBytes(), userViewHistory.getBytes());
                    context.write(rowkey, put);
                }
            }
        }

1.  编写Reduce任务配置

*  导入包的时候，注意导入的FileInputFormat和FileOutputFormat是下面这两个包  
    **import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;  
    import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;**

public static void main(String[] args) throws Exception { 
            final String INPUT_PATH= "hdfs://cluster/louisvv/weblog-20181121.txt";
            final String OUTPUT_PATH= "hdfs://cluster/louisvv/HFileOutput";
            Configuration conf=HBaseConfiguration.create();
            conf.set("hbase.zookeeper.quorum", "192.168.1.22,192.168.1.50,192.168.1.51");
            conf.set("hbase.zookeeper.property.clientPort", "2181");
            conf.set("zookeeper.znode.parent", "/hbase-unsecure");
            conf.set("hbase.master", "192.168.1.22:16010");
            String tableName="user-view";
            Connection connection = null;
            try { 
                // 创建hbase connection
                connection = ConnectionFactory.createConnection(conf);
                // 获取hbase admin
                Admin admin=connection.getAdmin();
                // 创建hbase table
                Table table = connection.getTable(TableName.valueOf(tableName));
                // 设置mapreduce job相关内容
                Job job=Job.getInstance(conf);
                job.setJarByClass(BuckLoadImport.class);
                // 设置mapper class
                job.setMapperClass(BuckLoadImport.BuckLoadMap.class);
                // 设置map输出key类型为ImmutableBytesWritable
                job.setMapOutputKeyClass(ImmutableBytesWritable.class);
                // 设置map输出value类型为put
                job.setMapOutputValueClass(Put.class);
    
                // 设置job的输出格式为HFileOutputFormat2
                job.setOutputFormatClass(HFileOutputFormat2.class);
    
                // 设置文件输入输出路径
                FileInputFormat.addInputPath(job,new Path(INPUT_PATH));
                FileOutputFormat.setOutputPath(job,new Path(OUTPUT_PATH));
    
                // 设置HFileOutputFormat2
                HFileOutputFormat2.configureIncrementalLoad(job,table,connection.getRegionLocator(TableName.valueOf(tableName)));
                // 等待程序退出
                job.waitForCompletion(true);

1.  编写好job的配置后，等待MapReduce程序运行完毕，创建LoadIncrementalHFiles，调用doBulkLoad方法

// 使用buckload方式导入刚才MapReduce程序生成的HFile
    LoadIncrementalHFiles loader = new LoadIncrementalHFiles(conf);
    loader.doBulkLoad(new Path(OUTPUT_PATH),admin,table,connection.getRegionLocator(TableName.valueOf(tableName)));

1.  程序编写好了后，打包，上传到服务器上

在执行程序之前，需要创建表，如果不创建，则会自动创建  
建表语句：

create 'user-view',
     { NAME => 'desc', BLOOMFILTER => 'ROWCOL', COMPRESSION => 'SNAPPY', BLOCKCACHE => 'false', REPLICATION_SCOPE => '1'}, 
    { NAME => 'info', BLOOMFILTER => 'ROWCOL', COMPRESSION => 'SNAPPY', BLOCKCACHE => 'false', REPLICATION_SCOPE => '1'},SPLITS => ['0','1', '2', '3', '4','5','6','7','8','9']

1.  运行程序

hadoop jar /louisvv/HBase-test.jar cn.louisvv.weblog.hbase.BuckLoadImport

截取部分MapReduce日志如下：

通过日志，可以看到，一共输入1999940条数据，输出1999936条数据，过滤了4条有问题的数据

18/11/23 13:30:43 INFO mapreduce.Job: Running job: job_1542881108771_0004
    18/11/23 13:31:30 INFO mapreduce.Job:  map 0% reduce 0%
    省略....
    18/11/23 14:07:33 INFO mapreduce.Job:  map 100% reduce 100%
    18/11/23 14:07:37 INFO mapreduce.Job: Job job_1542881108771_0004 completed successfully
    18/11/23 14:07:38 INFO mapreduce.Job: Counters: 49
    	File System Counters
    		FILE: Number of bytes read=18234502087
    		FILE: Number of bytes written=36506399063
    		FILE: Number of read operations=0
    		FILE: Number of large read operations=0
    		FILE: Number of write operations=0
    		HDFS: Number of bytes read=13423862333
    		HDFS: Number of bytes written=3778584104
    		HDFS: Number of read operations=1051
    		HDFS: Number of large read operations=0
    		HDFS: Number of write operations=30
    	Job Counters 
    		Launched map tasks=200
    		Launched reduce tasks=11
    		Data-local map tasks=200
    		Total time spent by all maps in occupied slots (ms)=4528492
    		Total time spent by all reduces in occupied slots (ms)=3817650
    		Total time spent by all map tasks (ms)=2264246
    		Total time spent by all reduce tasks (ms)=1908825
    		Total vcore-milliseconds taken by all map tasks=2264246
    		Total vcore-milliseconds taken by all reduce tasks=1908825
    		Total megabyte-milliseconds taken by all map tasks=9274351616
    		Total megabyte-milliseconds taken by all reduce tasks=7818547200
    	Map-Reduce Framework
    		Map input records=1999940
    		Map output records=1999936
    		Map output bytes=18226502217
    		Map output materialized bytes=18234515161
    		Input split bytes=20400
    		Combine input records=0
    		Combine output records=0
    		Reduce input groups=1927972
    		Reduce shuffle bytes=18234515161
    		Reduce input records=1999936
    		Reduce output records=5783916
    		Spilled Records=3999872
    		Shuffled Maps =2200
    		Failed Shuffles=0
    		Merged Map outputs=2200
    		GC time elapsed (ms)=365192
    		CPU time spent (ms)=5841130
    		Physical memory (bytes) snapshot=570273415168
    		Virtual memory (bytes) snapshot=1170857234432
    		Total committed heap usage (bytes)=627039010816
    	Shuffle Errors
    		BAD_ID=0
    		CONNECTION=0
    		IO_ERROR=0
    		WRONG_LENGTH=0
    		WRONG_MAP=0
    		WRONG_REDUCE=0
    	File Input Format Counters 
    		Bytes Read=13423769333
    	File Output Format Counters 
    		Bytes Written=3778584104

1.  在hdfs上查看生产的HFile文件

*  生成的HFile目录，发现其中有一个info目录，是生成的列族目录  
    ![在这里插入图片描述][20190425094734841.png]
 *  查看info目录下的内容，生产的是Region文件  
    ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjY0MTkwOQ_size_16_color_FFFFFF_t_70 1]

1.  使用BuckLoad方法向表中导入数据：  
    我这里使用的是命令行方式导入，命令如下：

HADOOP_CLASSPATH=`/usr/hdp/2.6.0.3-8/hbase/bin/hbase classpath` hadoop jar hbase-server-1.1.2.2.6.0.3-8.jar completebulkload /yw/HFileOutput user-view

数据导入成功，部分日志如下：

18/11/23 16:29:55 INFO zookeeper.ZooKeeper: Client environment:java.library.path=:/usr/hdp/2.6.0.3-8/hadoop/lib/native/Linux-amd64-64:/usr/lib/hadoop/lib/native/Linux-amd64-64:/usr/hdp/current/hadoop-client/lib/native/Linux-amd64-64:/usr/hdp/2.6.0.3-8/hadoop/lib/native
    18/11/23 16:29:55 INFO zookeeper.ZooKeeper: Client environment:java.io.tmpdir=/tmp
    18/11/23 16:29:55 INFO zookeeper.ZooKeeper: Client environment:java.compiler=<NA>
    18/11/23 16:29:55 INFO zookeeper.ZooKeeper: Client environment:os.name=Linux
    18/11/23 16:29:55 INFO zookeeper.ZooKeeper: Client environment:os.arch=amd64
    18/11/23 16:29:55 INFO zookeeper.ZooKeeper: Client environment:os.version=3.10.0-514.el7.x86_64
    18/11/23 16:29:55 INFO zookeeper.ZooKeeper: Client environment:user.name=hdfs
    18/11/23 16:29:55 INFO zookeeper.ZooKeeper: Client environment:user.home=/home/hdfs
    18/11/23 16:29:55 INFO zookeeper.ZooKeeper: Client environment:user.dir=/usr/hdp/2.6.0.3-8/hbase/lib
    18/11/23 16:29:55 INFO zookeeper.ZooKeeper: Initiating client connection, connectString=ai-main:2181,ai-node3:2181,ai-node4:2181 sessionTimeout=90000 watcher=org.apache.hadoop.hbase.zookeeper.PendingWatcher@757f675c
    18/11/23 16:29:55 INFO zookeeper.ClientCnxn: Opening socket connection to server ai-node4/192.168.1.51:2181. Will not attempt to authenticate using SASL (unknown error)
    18/11/23 16:29:55 INFO zookeeper.ClientCnxn: Socket connection established to ai-node4/192.168.1.51:2181, initiating session
    18/11/23 16:29:55 INFO zookeeper.ClientCnxn: Session establishment complete on server ai-node4/192.168.1.51:2181, sessionid = 0x366665b1dbf0295, negotiated timeout = 60000
    18/11/23 16:29:57 INFO zookeeper.RecoverableZooKeeper: Process identifier=hconnection-0x46c3a14d connecting to ZooKeeper ensemble=ai-main:2181,ai-node3:2181,ai-node4:2181
    18/11/23 16:29:57 INFO zookeeper.ZooKeeper: Initiating client connection, connectString=ai-main:2181,ai-node3:2181,ai-node4:2181 sessionTimeout=90000 watcher=org.apache.hadoop.hbase.zookeeper.PendingWatcher@38fc5554
    18/11/23 16:29:57 INFO zookeeper.ClientCnxn: Opening socket connection to server ai-node3/192.168.1.50:2181. Will not attempt to authenticate using SASL (unknown error)
    18/11/23 16:29:57 INFO zookeeper.ClientCnxn: Socket connection established to ai-node3/192.168.1.50:2181, initiating session
    18/11/23 16:29:57 INFO zookeeper.ClientCnxn: Session establishment complete on server ai-node3/192.168.1.50:2181, sessionid = 0x2673ae5cb901733, negotiated timeout = 60000
    18/11/23 16:29:57 WARN mapreduce.LoadIncrementalHFiles: Skipping non-directory hdfs://cluster/yw/HFileOutput/_SUCCESS
    18/11/23 16:29:58 INFO hfile.CacheConfig: CacheConfig:disabled
    18/11/23 16:29:58 INFO hfile.CacheConfig: CacheConfig:disabled
    18/11/23 16:29:58 INFO hfile.CacheConfig: CacheConfig:disabled
    18/11/23 16:29:58 INFO hfile.CacheConfig: CacheConfig:disabled
    18/11/23 16:29:58 INFO hfile.CacheConfig: CacheConfig:disabled
    18/11/23 16:29:58 INFO hfile.CacheConfig: CacheConfig:disabled
    18/11/23 16:29:58 INFO hfile.CacheConfig: CacheConfig:disabled
    18/11/23 16:29:58 INFO hfile.CacheConfig: CacheConfig:disabled
    18/11/23 16:29:58 INFO hfile.CacheConfig: CacheConfig:disabled
    18/11/23 16:29:58 INFO hfile.CacheConfig: CacheConfig:disabled
    18/11/23 16:29:59 INFO compress.CodecPool: Got brand-new decompressor [.snappy]
    18/11/23 16:29:59 INFO compress.CodecPool: Got brand-new decompressor [.snappy]
    18/11/23 16:29:59 INFO compress.CodecPool: Got brand-new decompressor [.snappy]
    18/11/23 16:29:59 INFO compress.CodecPool: Got brand-new decompressor [.snappy]
    18/11/23 16:29:59 INFO compress.CodecPool: Got brand-new decompressor [.snappy]
    18/11/23 16:29:59 INFO compress.CodecPool: Got brand-new decompressor [.snappy]
    18/11/23 16:29:59 INFO compress.CodecPool: Got brand-new decompressor [.snappy]
    18/11/23 16:29:59 INFO compress.CodecPool: Got brand-new decompressor [.snappy]
    18/11/23 16:29:59 INFO compress.CodecPool: Got brand-new decompressor [.snappy]
    18/11/23 16:29:59 INFO mapreduce.LoadIncrementalHFiles: Trying to load hfile=hdfs://cluster/yw/HFileOutput/info/f20edfdb89fc4630ae8c3791887d4852 first=80000042581 last=89999917251
    18/11/23 16:29:59 INFO mapreduce.LoadIncrementalHFiles: Trying to load hfile=hdfs://cluster/yw/HFileOutput/info/fb6d6313abed41ef8fd5352442887031 first=00000006731 last=09999955271
    18/11/23 16:29:59 INFO mapreduce.LoadIncrementalHFiles: Trying to load hfile=hdfs://cluster/yw/HFileOutput/info/ffa5997038414dceb9eb3b42d67b8adc first=70000014781 last=79999981941
    18/11/23 16:29:59 INFO mapreduce.LoadIncrementalHFiles: Trying to load hfile=hdfs://cluster/yw/HFileOutput/info/4eaee167b73c41688d66440294a006d9 first=40000093231 last=49999941151
    18/11/23 16:29:59 INFO mapreduce.LoadIncrementalHFiles: Trying to load hfile=hdfs://cluster/yw/HFileOutput/info/0c71bccc45704d129e0d0f8afce6ae5f first=1 last=19999956131
    18/11/23 16:29:59 INFO mapreduce.LoadIncrementalHFiles: Trying to load hfile=hdfs://cluster/yw/HFileOutput/info/8b967a2cad6940619537382a2156a83c first=90000069581 last=99999997631
    18/11/23 16:29:59 INFO mapreduce.LoadIncrementalHFiles: Trying to load hfile=hdfs://cluster/yw/HFileOutput/info/2907e292f624470ca71e4253491563f2 first=30000029371 last=39999882551
    18/11/23 16:29:59 INFO mapreduce.LoadIncrementalHFiles: Trying to load hfile=hdfs://cluster/yw/HFileOutput/info/a67fd52d0125424b873c9ed49c0d8a7d first=20000123931 last=29999959681
    18/11/23 16:29:59 INFO mapreduce.LoadIncrementalHFiles: Trying to load hfile=hdfs://cluster/yw/HFileOutput/info/00dcb6dc63c74d9a86a8d1ca1802b681 first=50000024931 last=59999976981
    18/11/23 16:29:59 INFO mapreduce.LoadIncrementalHFiles: Trying to load hfile=hdfs://cluster/yw/HFileOutput/info/c95917012c834d7991bf77830806370e first=60000015751 last=69999815851
    18/11/23 16:29:59 INFO client.ConnectionManager$HConnectionImplementation: Closing master protocol: MasterService
    18/11/23 16:29:59 INFO client.ConnectionManager$HConnectionImplementation: Closing zookeeper sessionid=0x2673ae5cb901733
    18/11/23 16:29:59 INFO zookeeper.ZooKeeper: Session: 0x2673ae5cb901733 closed
    18/11/23 16:29:59 INFO zookeeper.ClientCnxn: EventThread shut down

1.  验证  
    使用hbase shell 查看数据是否存在，就拿这条数据进行测试

进入hbase shell，查找该用户浏览信息

hbase(main):002:0> get 'user-view','56279618741'
    COLUMN                                          CELL                                                                                                                                       
     info:macAddress                                timestamp=1542953074902, value=65:85:36:f9:b1:c0                                                                                           
     info:phoneNumber                               timestamp=1542953074902, value=14781697265                                                                                                 
     info:userViewHistory                           timestamp=1542953074902, value=[{ "time":"1539787307","userAgent":"Mozilla/5.0 Macintosh; Intel Mac OS X 10_10_1 AppleWebKit/537.36 KHTML li ke Gecko Chrome/37.0.2062.124 Safari/537.36","visitUrl":"https://dl.lianjia.com/ershoufang/102100802770.html","upflow":"13660","downFlow":" 6860"},{ "time":"1539786398","userAgent":"Mozilla/5.0 Windows NT 5.1 AppleWebKit/537.36 KHTML like Gecko Chrome/36.0.1985.67 Safari/537.36",
                                                    "visitUrl":"https://dl.lianjia.com/ershoufang/102100576898.html","upflow":"1959","downFlow":"91040"},{ "time":"1539785462","userAgent":"Mozi lla/5.0 Windows NT 10.0 AppleWebKit/537.36 KHTML like Gecko Chrome/40.0.2214.93 Safari/537.36","visitUrl":"https://dl.lianjia.com/ershoufan g/102100762258.html","upflow":"12177","downFlow":"53132"}]                                                      
    3 row(s) in 0.0420 seconds

[HBase_ImportTsv_HBase]: https://blog.csdn.net/weixin_42641909/article/details/89467444
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjY0MTkwOQ_size_16_color_FFFFFF_t_70]: /images/20220215/edb2352212d04d3c8fbf175f58d35c4f.png
[20190425094734841.png]: /images/20220215/5fdb3a699eb04964b44c8f857b6e7c44.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjY0MTkwOQ_size_16_color_FFFFFF_t_70 1]: /images/20220215/54a1803f79744470af51b311cdffc847.png