成都创新互联始终坚持【策划先行，效果至上】的经营理念，通过多达十载累计超上千家客户的网站建设总结了一套系统有效的全网营销推广解决方案，现已广泛运用于各行各业的客户，其中包括：成都阳台护栏等企业，备受客户赞许。
鲁春利的工作笔记，谁说程序员不能有文艺范？
环境：
hadoop-2.6.0
hbase-1.0.1
zookeeper-3.4.6
1、Hadoop集群配置过程略；
2、Zookeeper集群配置过程略；
3、HBase集群配置过程略；
4、HBase作为输入源示例
查看当前hbase表m_domain中的数据
[hadoop@dnode1 conf]$ hbase shell
HBase Shell; enter 'help' for list of supported commands.
Type "exit" to leave the HBase Shell
Version 1.0.1, r66a93c09df3b12ff7b86c39bc8475c60e15af82d, Fri Apr 17 22:14:06 PDT 2015
hbase(main):001:0> list
TABLE
m_domain
t_domain
2 row(s) in 0.9270 seconds
=> ["m_domain", "t_domain"]
hbase(main):002:0> scan 'm_domain'
ROW COLUMN+CELL
alibaba.com_19990415_20220523 column=cf:access_server, timestamp=1440947490018, value=\xE6\x9D\xAD\xE5\xB7\x9E
alibaba.com_19990415_20220523 column=cf:exp_date, timestamp=1440947490018, value=2022\xE5\xB9\xB405\xE6\x9C\x8823\xE6\x97\xA5
alibaba.com_19990415_20220523 column=cf:ipstr, timestamp=1440947490018, value=205.204.101.42
alibaba.com_19990415_20220523 column=cf:owner, timestamp=1440947490018, value=Hangzhou Alibaba Advertising Co.
alibaba.com_19990415_20220523 column=cf:reg_date, timestamp=1440947490018, value=1999\xE5\xB9\xB404\xE6\x9C\x8815\xE6\x97\xA5
baidu.com_19991011_20151011 column=cf:access_server, timestamp=1440947489956, value=\xE5\x8C\x97\xE4\xBA\xAC
baidu.com_19991011_20151011 column=cf:exp_date, timestamp=1440947489956, value=2015\xE5\xB9\xB410\xE6\x9C\x8811\xE6\x97\xA5
baidu.com_19991011_20151011 column=cf:ipstr, timestamp=1440947489956, value=220.181.57.217
baidu.com_19991011_20151011 column=cf:reg_date, timestamp=1440947489956, value=1999\xE5\xB9\xB410\xE6\x9C\x8811\xE6\x97\xA5
2 row(s) in 1.4560 seconds
hbase(main):003:0> quit
实现Mapper端
package com.invic.mapreduce.hbase.source;
import java.io.IOException;
import java.util.Map;
import java.util.Map.Entry;
import java.util.NavigableMap;
import java.util.Set;
import org.apache.hadoop.hbase.Cell;
import org.apache.hadoop.hbase.CellUtil;
import org.apache.hadoop.hbase.client.Result;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableMapper;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
/**
*
* @author lucl
* TableMapper扩展自Mapper类，所有以HBase作为输入源的Mapper类都需要继承该类
*/
public class HBaseReaderMapper extends TableMapper {
private Text key = new Text();
private Text value = new Text();
@Override
protected void setup(Context context) throws IOException, InterruptedException {
super.setup(context);
}
@Override
protected void map(ImmutableBytesWritable row, Result result,Context context)
throws IOException, InterruptedException {
// 可以明确给定family
{
NavigableMap map = result.getFamilyMap("cf".getBytes());
Set> values = map.entrySet();
for (Entry entry : values) {
String columnQualifier = new String(entry.getKey());
String cellValue = new String(entry.getValue());
System.out.println(columnQualifier + "\t" + cellValue);
//
}
}
// 存在多个列族或者不确定列族名字
{
String rowKey = new String(row.get());
byte [] columnFamily = null;
byte [] columnQualifier = null;
byte [] cellValue = null;
StringBuffer sbf = new StringBuffer(1024);
for (Cell cell : result.listCells()) {
columnFamily = CellUtil.cloneFamily(cell);
columnQualifier = CellUtil.cloneQualifier(cell);
cellValue = CellUtil.cloneValue(cell);
sbf.append(Bytes.toString(columnFamily));
sbf.append(".");
sbf.append(Bytes.toString(columnQualifier));
sbf.append(":");
sbf.append(new String(cellValue, "UTF-8"));
}
key.set(rowKey);
value.set(sbf.toString());
context.write(key, value);
}
}
@Override
protected void cleanup(Context context) throws IOException, InterruptedException{
super.cleanup(context);
}
}
实现MapReduce的Driver类
package com.invic.mapreduce.hbase.source;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
/**
*
* @author lucl
* HBase作为输入源示例
*
*/
public class HBaseASDataSourceDriver extends Configured implements Tool {
/**
*
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
// System.setProperty("hadoop.home.dir", "E:\\hadoop-2.6.0\\hadoop-2.6.0\\");
int exit = ToolRunner.run(new HBaseASDataSourceDriver(), args);
System.out.println("receive exit : " + exit);
}
@Override
public int run(String[] args) throws Exception {
Configuration conf = HBaseConfiguration.create();
// hadoop的参数配置
/*conf.set("fs.defaultFS", "hdfs://cluster");
conf.set("dfs.nameservices", "cluster");
conf.set("dfs.ha.namenodes.cluster", "nn1,nn2");
conf.set("dfs.namenode.rpc-address.cluster.nn1", "nnode:8020");
conf.set("dfs.namenode.rpc-address.cluster.nn2", "dnode1:8020");
conf.set("dfs.client.failover.proxy.provider.cluster",
"org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider");*/
// hbase master
// property "hbase.master" has been deprecated since 0.90
// Just passing the ZK configuration makes your client auto-discover the master
// conf.set("hbase.master", "nnode:60000");
// zookeeper quorum
getConf().set("hbase.zookeeper.property.clientport", "2181");
getConf().set("hbase.zookeeper.quorum", "nnode,dnode1,dnode2");
// 是否对Map Task启用推测执行机制
getConf().setBoolean("mapreduce.map.speculative", false);
// 是否对Reduce Task启用推测执行机制
getConf().setBoolean("mapreduce.reduce.speculative", false);
Job job = Job.getInstance(conf);
job.setJobName("MyBaseReaderFromHBase");
job.setJarByClass(HBaseASDataSourceDriver.class);
job.setOutputFormatClass(TextOutputFormat.class);
/**
* 从HBase读取数据时数据会传给下面定义的Mapper来，在Mapper类中进行了数据的处理
* 由于在job中未指定Reducer类，会调用默认的Reducer类来将Mapper的输出原封不动的写入；
* 如果需要在Reducer中再做些其他的单独的处理，则可以自定义Reducer类再做些处理。
*/
Scan scan = new Scan();
// scan.addFamily(family);
// scan.addColumn(family, qualifier);
byte [] tableName = Bytes.toBytes("m_domain");
TableMapReduceUtil.initTableMapperJob(tableName, scan, HBaseReaderMapper.class, Text.class, Text.class, job);
Path path = new Path("/" + System.currentTimeMillis());
FileOutputFormat.setOutputPath(job, path);
return job.waitForCompletion(true) ? 0 : 1;
}
} 查看结果：
问题记录：
a. 通过Eclipse执行时报错，但未分析出原因
b. 放到集群环境中运行时Mapper类如果定义在Driver类中，则报错
ClassNotFound for HBaseASDataSourceDriver$HBaseReaderMapper init()
c. zookeeper连接符总是显示连接的为127.0.0.1而非配置的zookeeper.quorum
如果zookeeper集群环境与hbase环境在不同的机器不知道是否会出现问题。
5、Hbase作为输出源示例
文本文件内容如下：
2013-09-13 16:04:08 www.subnetc1.com 192.168.1.7 80 192.168.1.139 18863 HTTP www.subnetc1.com/index.html
2013-09-13 16:04:08 www.subnetc2.com 192.168.1.7 80 192.168.1.159 14100 HTTP www.subnetc2.com/index.html
2013-09-13 16:04:08 www.subnetc3.com 192.168.1.7 80 192.168.1.130 4927 HTTP www.subnetc3.com/index.html
2013-09-13 16:04:08 www.subnetc4.com 192.168.1.7 80 192.168.1.154 39044 HTTP www.subnetc4.com/index.html
Map端代码：
package com.invic.mapreduce.hbase.target;
import java.io.IOException;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class MyMapper extends Mapper {
@Override
public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
// 用来实现wordcount功能，示例程序, Mapper
/*{
IntWritable one = new IntWritable(1);
Text word = new Text();
StringTokenizer token = new StringTokenizer(value.toString());
while (token.hasMoreTokens()) {
word.set(token.nextToken());
context.write(word, one);
}
}*/
// 将多列数据写入hbase, Mapper
{
String [] temps = value.toString().split("\t");
if (null != temps && temps.length == 8) {
Text word = new Text();
word.set(temps[1]);
context.write(word, value);
}
}
}
}
Reducer端代码：
package com.invic.mapreduce.hbase.target;
import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableReducer;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
/**
*
* @author lucl
*
*/
public class MyReducer extends TableReducer {
@Override
public void reduce(Text key, Iterable value, Context context) throws IOException, InterruptedException {
// for wordcount
// TableReducer
// Iterable
/*{
int sum = 0;
for (Iterator it = value.iterator(); it.hasNext(); ) {
IntWritable val = it.next();
sum += val.get();
}
Put put = new Put(key.getBytes());
// sum为Integer类型，需要先转为S他ring，然后再取byte值，否则查看数据时无法显示sum的值
byte [] datas = Bytes.toBytes(String.valueOf(sum));
put.addColumn(Bytes.toBytes("cf"), Bytes.toBytes("count"), datas);
context.write(new ImmutableBytesWritable(key.getBytes()), put);
}*/
// 需要将多列写入HBase
// TableReducer
// Iterable value
{
byte [] family = "cf".getBytes();
Put put = new Put(key.getBytes());
StringBuffer sbf = new StringBuffer();
for (Text text : value) {
sbf.append(text.toString());
}
put.addColumn(family, Bytes.toBytes("detail"), Bytes.toBytes(sbf.toString()));
context.write(new ImmutableBytesWritable(key.getBytes()), put);
}
}
}
Driver驱动类：
package com.invic.mapreduce.hbase.target;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.HColumnDescriptor;
import org.apache.hadoop.hbase.HTableDescriptor;
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.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
/**
*
* @author lucl
* HBase作为输出源示例
*
*/
public class HBaseASDataTargetDriver extends Configured implements Tool {
private static final String TABLE_NAME = "t_inter_log";
private static final String COLUMN_FAMILY_NAME = "cf";
/**
*
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
// for eclipse
// System.setProperty("hadoop.home.dir", "E:\\hadoop-2.6.0\\hadoop-2.6.0\\");
int exit = ToolRunner.run(new HBaseASDataTargetDriver(), args);
System.out.println("receive exit : " + exit);
}
@Override
public int run(String[] args) throws Exception {
Configuration conf = HBaseConfiguration.create(getConf());
// hadoop的参数配置
conf.set("fs.defaultFS", "hdfs://cluster");
conf.set("dfs.nameservices", "cluster");
conf.set("dfs.ha.namenodes.cluster", "nn1,nn2");
conf.set("dfs.namenode.rpc-address.cluster.nn1", "nnode:8020");
conf.set("dfs.namenode.rpc-address.cluster.nn2", "dnode1:8020");
conf.set("dfs.client.failover.proxy.provider.cluster",
"org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider");
// hbase master
// property "hbase.master" has been deprecated since 0.90
// Just passing the ZK configuration makes your client auto-discover the master
// conf.set("hbase.master", "nnode:60000");
// zookeeper quorum
conf.set("hbase.zookeeper.property.clientport", "2181");
conf.set("hbase.zookeeper.quorum", "nnode,dnode1,dnode2");
// 是否对Map Task启用推测执行机制
conf.setBoolean("mapreduce.map.speculative", false);
// 是否对Reduce Task启用推测执行机制
conf.setBoolean("mapreduce.reduce.speculative", false);
/**
* HBase创建表
*/
Connection connection = ConnectionFactory.createConnection(conf);
Admin admin = connection.getAdmin();
TableName tableName = TableName.valueOf(TABLE_NAME);
boolean exists = admin.tableExists(tableName);
if (exists) {
admin.disableTable(tableName);
admin.deleteTable(tableName);
}
HTableDescriptor tableDesc = new HTableDescriptor(tableName);
HColumnDescriptor columnDesc = new HColumnDescriptor(COLUMN_FAMILY_NAME);
tableDesc.addFamily(columnDesc);
admin.createTable(tableDesc);
/**
* 读取文件内容
*/
String fileName = "http_interceptor_20130913.txt";
Job job = Job.getInstance(conf);
job.setJobName("MyBaseWriterToHBase");
job.setJarByClass(HBaseASDataTargetDriver.class);
job.setMapperClass(MyMapper.class);
/**
* MapReduce读取文本文件时默认的的四个参数（KeyIn， ValueIn，KeyOut，ValueOut）
* 说明：
* 默认情况下KeyIn为IntWrite类型，为在文本文件中的偏移量，ValueIn为一行数据
* 第一次测试时未设置的设置map端输出的key-value类型，程序执行正常
* 第二次增加map端输出的key-value类型设置
* job.setMapOutputKeyClass
* job.setMapOutputValueClass
* Hadoop应用开发技术详解2015年1月第1版P191页写的：
* map端输出的key-value默认类型分别为LongWritable和Text
* 根据示例程序MyMapper中实现的map端输出的key-value实际为Text和IntWritable
*
// job.setMapOutputKeyClass(LongWritable.class);
// job.setMapOutputValueClass(Text.class);
// 设置后页面调用时报错如下：
15/09/04 22:19:06 INFO mapreduce.Job: Task Id : attempt_1441346242717_0014_m_000000_0, Status : FAILED
Error: java.io.IOException: Type mismatch in key from map: expected org.apache.hadoop.io.LongWritable, received org.apache.hadoop.io.Text
at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.collect(MapTask.java:1069)
at org.apache.hadoop.mapred.MapTask$NewOutputCollector.write(MapTask.java:712)
at org.apache.hadoop.mapreduce.task.TaskInputOutputContextImpl.write(TaskInputOutputContextImpl.java:89)
at org.apache.hadoop.mapreduce.lib.map.WrappedMapper$Context.write(WrappedMapper.java:112)
at com.invic.mapreduce.hbase.target.MyMapper.map(MyMapper.java:21)
at com.invic.mapreduce.hbase.target.MyMapper.map(MyMapper.java:1)
at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:145)
at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:784)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)
at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:163)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1628)
at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:158)
第三次设置为与Mapper类中一致的，程序执行正确。
*/
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
// 下面这句话不能加，在测试中发现加了这句话竟然报错找不到MyReducer类了。
// job.setReducerClass(MyReducer.class);
Path path = new Path(fileName);
FileInputFormat.addInputPath(job, path);
TableMapReduceUtil.initTableReducerJob(TABLE_NAME, MyReducer.class, job);
// for wordcount
// job.setOutputKeyClass(Text.class);
// job.setOutputValueClass(IntWritable.class);
// for multi columns
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
return job.waitForCompletion(true) ? 0 : 1;
}
} 未设置Map输出的key-value的类型时报错如下(wordcount的示例未报错，在Hadoop应用开发技术详解中说map端输出的key-value默认类型为：LongWritable.class和Text.class，但是wordcount示例中map端输出的key-value类型却为Text.class和IntWritable）：
15/09/04 21:15:54 INFO mapreduce.Job: map 0% reduce 0%
15/09/04 21:16:27 INFO mapreduce.Job: Task Id : attempt_1441346242717_0011_m_000000_0, Status : FAILED
Error: java.io.IOException: Type mismatch in value from map: expected org.apache.hadoop.io.IntWritable, received org.apache.hadoop.io.Text
at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.collect(MapTask.java:1074)
at org.apache.hadoop.mapred.MapTask$NewOutputCollector.write(MapTask.java:712)
at org.apache.hadoop.mapreduce.task.TaskInputOutputContextImpl.write(TaskInputOutputContextImpl.java:89)
at org.apache.hadoop.mapreduce.lib.map.WrappedMapper$Context.write(WrappedMapper.java:112)
at com.invic.mapreduce.hbase.target.MyMapper.map(MyMapper.java:29)
at com.invic.mapreduce.hbase.target.MyMapper.map(MyMapper.java:1)
at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:145)
at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:784)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)
at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:163)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1628)
# 由于出现错误时Map端为0%，所以分析问题出现在map端，且根据提示信息说明默认value应该是IntWritable，我第二次的示例与wordcount的差别主要在map端输出的value由IntWritabe->Text，设置了如下参数后问题解决。
# job.setMapOutputKeyClass(Text.class);
# job.setMapOutputValueClass(Text.class);
wordcount及数据入库示例程序执行结果验证：
hbase(main):005:0> scan 't_inter_log'
ROW COLUMN+CELL
14100 column=cf:count, timestamp=1441370812728, value=1
16:04:08 column=cf:count, timestamp=1441370812728, value=4
18863:08 column=cf:count, timestamp=1441370812728, value=1
192.168.1.130 column=cf:count, timestamp=1441370812728, value=1
192.168.1.139 column=cf:count, timestamp=1441370812728, value=1
192.168.1.154 column=cf:count, timestamp=1441370812728, value=1
192.168.1.159 column=cf:count, timestamp=1441370812728, value=1
192.168.1.759 column=cf:count, timestamp=1441370812728, value=4
2013-09-13759 column=cf:count, timestamp=1441370812728, value=4
3904409-13759 column=cf:count, timestamp=1441370812728, value=1
4927409-13759 column=cf:count, timestamp=1441370812728, value=1
8027409-13759 column=cf:count, timestamp=1441370812728, value=4
HTTP409-13759 column=cf:count, timestamp=1441370812728, value=4
www.subnetc1.com column=cf:count, timestamp=1441370812728, value=1
www.subnetc1.com/index.html column=cf:count, timestamp=1441370812728, value=1
www.subnetc2.com/index.html column=cf:count, timestamp=1441370812728, value=1
www.subnetc3.com/index.html column=cf:count, timestamp=1441370812728, value=1
www.subnetc4.com/index.html column=cf:count, timestamp=1441370812728, value=1
18 row(s) in 1.2290 seconds
# 每次执行时都会先删除t_inter_log表
hbase(main):007:0> scan 't_inter_log'
ROW COLUMN+CELL
www.subnetc1.com column=cf:detail, timestamp=1441373481468, value=2013-09-13 16:04:08\x09www.subnetc1.com\x09192.168.1.7\x0980\x09192.168.1.139\x0918863\x09HTTP\x09www.subnetc1.com/index.html
www.subnetc2.com column=cf:detail, timestamp=1441373481468, value=2013-09-13 16:04:08\x09www.subnetc2.com\x09192.168.1.7\x0980\x09192.168.1.159\x0914100\x09HTTP\x09www.subnetc2.com/index.html
www.subnetc3.com column=cf:detail, timestamp=1441373481468, value=2013-09-13 16:04:08\x09www.subnetc3.com\x09192.168.1.7\x0980\x09192.168.1.130\x094927\x09HTTP\x09www.subnetc3.com/index.html
www.subnetc4.com column=cf:detail, timestamp=1441373481468, value=2013-09-13 16:04:08\x09www.subnetc4.com\x09192.168.1.7\x0980\x09192.168.1.154\x0939044\x09HTTP\x09www.subnetc4.com/index.html
4 row(s) in 3.3280 seconds
6、HBase作为共享源示例
文章名称：HBase-1.0.1学习笔记（四）MapReduce操作HBase
网站URL：
http://kswsj.cn/article/jogjoi.html
