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 shellHBase 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();@Overrideprotected void setup(Context context) throws IOException, InterruptedException {super.setup(context);}@Overrideprotected 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);}}@Overrideprotected 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);}@Overridepublic 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 quorumgetConf().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 16:04:08www.subnetc1.com192.168.1.780192.168.1.13918863HTTPwww.subnetc1.com/index.html2013-09-13 16:04:08www.subnetc2.com192.168.1.780192.168.1.15914100HTTPwww.subnetc2.com/index.html2013-09-13 16:04:08www.subnetc3.com192.168.1.780192.168.1.1304927HTTPwww.subnetc3.com/index.html2013-09-13 16:04:08www.subnetc4.com192.168.1.780192.168.1.15439044HTTPwww.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> {@Overridepublic 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> {@Overridepublic 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);}@Overridepublic 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 quorumconf.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 : FAILEDError: 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$2.run(YarnChild.java:163) at java.security.AccessController.doPrivileged(Native Method) at javax.security.auth.Subject.doAs(Subject.java:415) at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1628) at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:158) 第三次设置为与Mapper类中一致的，程序执行正确。*/job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(IntWritable.class);// 下面这句话不能加，在测试中发现加了这句话竟然报错找不到MyReducer类了。// job.setReducerClass(MyReducer.class);Path path = new Path(fileName);FileInputFormat.addInputPath(job, path);TableMapReduceUtil.initTableReducerJob(TABLE_NAME, MyReducer.class, job);// for wordcount// job.setOutputKeyClass(Text.class);// job.setOutputValueClass(IntWritable.class);// for multi columnsjob.setOutputKeyClass(Text.class);job.setOutputValueClass(Text.class);return job.waitForCompletion(true) ? 0 : 1;}}
未设置Map输出的key-value的类型时报错如下(wordcount的示例未报错，在Hadoop应用开发技术详解中说map端输出的key-value默认类型为：LongWritable.class和Text.class，但是wordcount示例中map端输出的key-value类型却为Text.class和IntWritable）：
15/09/04 21:15:54 INFO mapreduce.Job: map 0% reduce 0%15/09/04 21:16:27 INFO mapreduce.Job: Task Id : attempt_1441346242717_0011_m_000000_0, Status : FAILEDError: java.io.IOException: Type mismatch in value from map: expected org.apache.hadoop.io.IntWritable, received org.apache.hadoop.io.Text at org.apache.hadoop.mapred.MapTask$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+CELLwww.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.html4 row(s) in 3.3280 seconds
6、HBase作为共享源示例
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