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HBase-1.0.1学习笔记（四）MapReduce操作HBase

2015-09-04 12:59 736 查看
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-3.4.61、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<RETURN>' for list of supported commands.
Type "exit<RETURN>" 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<Writable, Writable> {
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<byte[], byte[]> map = result.getFamilyMap("cf".getBytes());
Set<Entry<byte[], byte[]>> values = map.entrySet();
for (Entry<byte[], byte[]> 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<Object, Text, Text, Text> {
@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<Object, Text, Text, IntWritable>
/*{
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<Object, Text, Text, Text>
{
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<Text, Text, ImmutableBytesWritable> {
@Override
public void reduce(Text key, Iterable<Text> value, Context context) throws IOException, InterruptedException {
// for wordcount
// TableReducer<Text, IntWritable, ImmutableBytesWritable>
// Iterable<IntWritable>
/*{
int sum = 0;
for (Iterator<IntWritable> 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<Text, Text, ImmutableBytesWritable>
// Iterable<Text> 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

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