使用BuckLoad向HBase中导入数据

上一篇文章简单介绍了, 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,了解更多内容请戳使用ImportTSV向HBase中导入数据

今天我们来搞一下Buckload

这里有一张图,很好的解释了BuckLoad的导入原理,通过MapReduce程序在HDFS直接生成HFile文件,将HFile文件移动到HBase中对应表的HDFS目录中


其实ImortTSV生成HFile,再导入HBase的方式也是BuckLoad

但与BuckLoad方式不同的是,ImportTSV的导入方式,是在命令行进行导入的,不需要我们编写程序,仅需要确定数据文件的格式与HBase表中对应的列维度即可,如果我们没法确认,则需要对ImportTSV进行自定义改造

ImportTSV这种方式,是比较友好的,数据格式定义好了,列族规划好了,直接导入就行

但有些场景,还是需要我们自定义导入程序,这时使用ImportTSV就不太方便了,自定义改造如果不熟,还是比较麻烦的

这里,我们就可以选择使用BuckLoad方式进行导入,BuckLoad的优势是:通过自定义程序生成HFile,再进行导入即可,比较灵活

BuckLoad程序编写步骤:

1.编写mapper程序,注意无论是map还是reduce,其输出类型必须是:< 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

4.配置MapReduce任务的输入,输出格式,类型,目录等

5.使用BuckLoad方式导入数据,有两种方法:

(1)代码创建LoadIncrementalHFiles对象,调用doBulkLoad方法,加载刚才MapReduce程序生成的HFile到表中即可

doBulkLoad有两种,HTable那种,已经过时了,推荐使用第一种毕竟现在HBase都已经使用新的API了


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

(2)命令行:在命令行中使用如下命令

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

如果在导入中发生异常:java.lang.NoClassDefFoundError: org/apache/hadoop/hbase/filter/Filter,原因是Hadoop的运行环境中缺少HBase支持的jar包

解决办法:

在命令前添加如下命令:

HADOOP_CLASSPATH=`$HBASE_HOME/bin/hbase classpath`

完整命令:

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> 

实例

版本:大数据平台基于HDP,版本2.6.0.3-8

HBase  1.1.2Hadoop 2.7.3

背景这有一个用户浏览网站的记录文件,分隔符为逗号

共有四列:1.手机号反转(避免Region反转),2.手机号,3.Mac地址,4.用户访问记录(用&&分隔)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

数据大小:12.48GB,共1999940条数据

需求手机号反转做为Rowkey,将手机号,Mac地址,用户访问地址分别插入到INFO列族的phoneNumber,macAddress,userView列中,并且将用户访问记录转化为json格式

1.下面开始编写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);
            }
        }
    }

mapper程序编写好后,编写MapReduce任务配置

这里我把输入,输出目录写死了,大家写的时候进行传参即可

注意:大家导入包的时候,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;

要不然会报错:


老版本的包是

import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;

要使用新版本的hadoop接口

 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);

如果选择使用命令行方式导入,这里请忽略

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)));

2.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']

3.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

4.在HDFS上查看生成的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


查看info目录下的内容,生成的是Region文件


5.使用BuckLoad方式想表中导入数据:

我这里使用的是命令行方式导入,命令如下:

hadoop jar hbase-server-1.1.2.2.6.0.3-8.jar completebulkload /louisvv/HFileOutput user-view

发生了异常,异常如下:

Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/hadoop/hbase/filter/Filter
	at java.lang.Class.getDeclaredMethods0(Native Method)
	at java.lang.Class.privateGetDeclaredMethods(Class.java:2701)
	at java.lang.Class.privateGetMethodRecursive(Class.java:3048)
	at java.lang.Class.getMethod0(Class.java:3018)
	at java.lang.Class.getMethod(Class.java:1784)
	at org.apache.hadoop.util.ProgramDriver$ProgramDescription.<init>(ProgramDriver.java:59)
	at org.apache.hadoop.util.ProgramDriver.addClass(ProgramDriver.java:103)
	at org.apache.hadoop.hbase.mapreduce.Driver.main(Driver.java:42)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:498)
	at org.apache.hadoop.util.RunJar.run(RunJar.java:233)
	at org.apache.hadoop.util.RunJar.main(RunJar.java:148)
Caused by: java.lang.ClassNotFoundException: org.apache.hadoop.hbase.filter.Filter
	at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
	at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
	at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
	... 14 more

根据前面介绍过的方法,在命令前添加HADOOP_CLASSPATH

 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

异常解决,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

6.验证:

使用hbase shell 查看数据是否存在,就拿这条数据进行测试

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

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

查到了,说明数据导入成功

至此,整个实例演示完毕


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赫墨拉

我是一个喜爱大数据的小菜鸡,这里是我分享我的成长和经历的博客

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