Hadoop：The Definitive Guid 总结 Chapter 5 MapReduce应用开发

用MapReduce来编写程序，有几个主要的特定流程，首先写map函数和reduce函数，最好使用单元测试来确保函数的运行符合预期，然后，写一个驱动程序来运行作业，要看这个驱动程序是否可以运行，之后利用本地IDE调试，修改程序

实际上权威指南的一些配置已经过时 所以这里很多地方不做介绍

1.配置API
Hadoop拥有很多xml配置文件，格式遵从一般xml的要求 见实例

<!--Example:5-1. A simple configuration file, configuration-1.xml-->
<?xml version="1.0"?>
<configuration>
<property>
<name>color</name>
<value>yellow</value>
<description>Color</description>
</property>
<property>
<name>size</name>
<value>10</value>
<description>Size</description>
</property>
<property>
<name>weight</name>
<value>heavy</value>
<final>true</final>
<description>Weight</description>
</property>
<property>
<name>size-weight</name>
<value>${size},${weight}</value>
<description>Size and weight</description>
</property>
</configuration>

访问属性的方法：

Configuration conf = new Configuration();
conf.addResource("configuration-1.xml");
assertThat(conf.get("color"), is("yellow"));
assertThat(conf.getInt("size", 0), is(10));
assertThat(conf.get("breadth", "wide"), is("wide"));

Hadoop允许多个配置文件进行合并：

<!--Example 5-2. A second configuration file, configuration-2.xml -->
<?xml version="1.0"?>
<configuration>
<property>
<name>size</name>
<value>12</value>
</property>
<property>
<name>weight</name>
<value>light</value>
</property>
</configuration>

源文件按顺序填到Configuration：

Configuration conf = new Configuration();
conf.addResource("configuration-1.xml");
conf.addResource("configuration-2.xml");

后来添加到源文件的属性会覆盖之前定义的属性，另外在上面的配置文件中，如果覆盖设置fina为true的property，则会出现配置错误，标记final为true的属性说明不希望客户端更改这个属性
关于可变的扩展：配置属性可以用其他属性或系统属性进行定义，而且系统属性的优先级高于源文件中定义的属性：

System.setProperty("size", "14");
assertThat(conf.get("size-weight"), is("14,heavy"));

该特性用于使用JVM参数-Dproperty=value来覆盖命令方式下的属性

2.配置开发环境
1).配置管理
权威指南给出了示例，实际上hadoop官方网站更具有权威性 如欲了解Hadoop2.0的配置参考示例请见：http://hadoop.apache.org/common/docs/r2.0.0-alpha/
2).辅助类GenericOptionsParser, Tool和ToolRunner
Hadoop提供了辅助类，GenericOptionsParser：用来解释常用的Hadoop命令选项，但是一般更常用的方式:实现Tool接口，通过ToolsRunner来运行程序，ToolRunner内部调用GenericOptionsParser
Tool实现示例用于打印一个Configuration对象的属性：

public interface Tool extends Configurable {
int run(String[] args) throws Exception;
}

public class ConfigurationPrinter extends Configured implements Tool {
static {
Configuration.addDefaultResource("hdfs-default.xml");
Configuration.addDefaultResource("hdfs-site.xml");
Configuration.addDefaultResource("mapred-default.xml");
Configuration.addDefaultResource("mapred-site.xml");
}

@Override
public int run(String[] args) throws Exception {
Configuration conf = getConf();
for (Entry<String, String> entry : conf) {
System.out.printf("%s=%s\n", entry.getKey(), entry.getValue());
}
return 0;
}

public static void main(String[] args) throws Exception {
int exitCode = ToolRunner.run(new ConfigurationPrinter(), args);
System.exit(exitCode);
}
}

在Hadoop中 -D选项可以把默认属性放入配置文件中，然后在需要时，用-D选项来覆盖它们，注意的是，这个不同于JVM系统属性设置Java命令 -Dproperty=value，JVM中的-D与属性没有空格

下面给出GenericOptionsParser选项和ToolRunner选项



3).编写单元测试
以下程序可以在IDE Eclipse中运行
[注意：mruint到0.9版本仍然只是支持的是0.20版本之前的mapred包中的Mapper和Reducer，并不支持mapreducer包中的Mapper和Reducer]
mapper的测试实例：

import java.io.IOException;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mrunit.mapreduce.MapDriver;
import org.junit.*;

public class MaxTemperatureMapperTest {
@Test
public void processesValidRecord() throws IOException, InterruptedException {
Text value = new Text(
"0043011990999991950051518004+68750+023550FM-12+0382" +
// Year ^^^^
"99999V0203201N00261220001CN9999999N9-00111+99999999999");
// Temperature ^^^^^
new MapDriver<LongWritable, Text, Text, IntWritable>()
.withMapper(new MaxTemperatureMapper()).withInputValue(value)
.withOutput(new Text("1950"), new IntWritable(-11)).runTest();
}
}

最终的Mapper函数：

public class MaxTemperatureMapper extends
Mapper<LongWritable, Text, Text, IntWritable> {
@Override
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String line = value.toString();
String year = line.substring(15, 19);
String temp = line.substring(87, 92);
if (!missing(temp)) {
int airTemperature = Integer.parseInt(temp);
context.write(new Text(year), new IntWritable(airTemperature));
}
}

private boolean missing(String temp) {
return temp.equals("+9999");
}
}

reducer的测试函数

import java.io.IOException;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mrunit.mapreduce.MapDriver;
import org.junit.*;

public class MaxTemperatureMapperTest {

@Test
public void returnsMaximumIntegerInValues() throws IOException,
InterruptedException {
new ReduceDriver<Text, IntWritable, Text, IntWritable>()
.withReducer(new MaxTemperatureReducer())
.withInputKey(new Text("1950"))
.withInputValues(
Arrays.asList(new IntWritable(10), new IntWritable(5)))
.withOutput(new Text("1950"), new IntWritable(10)).runTest();
}
}

最后的reducer函数实现

public class MaxTemperatureReducer extends
Reducer<Text, IntWritable, Text, IntWritable> {
@Override
public void reduce(Text key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
int maxValue = Integer.MIN_VALUE;
for (IntWritable value : values) {
maxValue = Math.max(maxValue, value.get());
}
context.write(key, new IntWritable(maxValue));
}
}

3.本地运行测试数据
1).本地运行Job
Job驱动程序查找最高气温

public class MaxTemperatureDriver extends Configured implements Tool {
@Override
public int run(String[] args) throws Exception {
if (args.length != 2) {
System.err.printf("Usage: %s [generic options] <input> <output>\n",
getClass().getSimpleName());
ToolRunner.printGenericCommandUsage(System.err);
return -1;
}
Job job = new Job(getConf(), "Max temperature");
job.setJarByClass(getClass());

FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));

job.setMapperClass(MaxTemperatureMapper.class);
job.setCombinerClass(MaxTemperatureReducer.class);
job.setReducerClass(MaxTemperatureReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);

return job.waitForCompletion(true) ? 0 : 1;
}

public static void main(String[] args) throws Exception {
int exitCode = ToolRunner.run(new MaxTemperatureDriver(), args);
System.exit(exitCode);
}
}

命令运行驱动程序：

% mvn compile

% export HADOOP_CLASSPATH=target/classes/

% hadoop v2.MaxTemperatureDriver -conf conf/hadoop-local.xml input/ncdc/micro output

这里给出权威指南上的parse函数

public class NcdcRecordParser {
private static final int MISSING_TEMPERATURE = 9999;
private String year;
private int airTemperature;
private String quality;

public void parse(String record) {
year = record.substring(15, 19);
String airTemperatureString;
// Remove leading plus sign as parseInt doesn't like them
if (record.charAt(87) == '+') {

airTemperatureString = record.substring(88, 92);
} else {
airTemperatureString = record.substring(87, 92);
}
airTemperature = Integer.parseInt(airTemperatureString);
quality = record.substring(92, 93);
}

public void parse(Text record) {
parse(record.toString());
}

public boolean isValidTemperature() {
return airTemperature != MISSING_TEMPERATURE
&& quality.matches("[01459]");
}

public String getYear() {
return year;
}

public int getAirTemperature() {
return airTemperature;
}
}

利用上面的parser函数mapper函数可以写成下面形式

public class MaxTemperatureMapper extends
Mapper<LongWritable, Text, Text, IntWritable> {
private NcdcRecordParser parser = new NcdcRecordParser();

@Override
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
parser.parse(value);
if (parser.isValidTemperature()) {
context.write(new Text(parser.getYear()),
new IntWritable(parser.getAirTemperature()));
}
}
}

2).测试驱动程序
需要关注的是 在下面程序中，checkOutput()方法被调用用以逐行对比实际输出与与其输出

@Test
public void test() throws Exception {
Configuration conf = new Configuration();
conf.set("fs.default.name", "file:///");
conf.set("mapred.job.tracker", "local");
Path input = new Path("input/ncdc/micro");
Path output = new Path("output");

FileSystem fs = FileSystem.getLocal(conf);
fs.delete(output, true); // delete old output
MaxTemperatureDriver driver = new MaxTemperatureDriver();
driver.setConf(conf);
int exitCode = driver.run(new String[] {
input.toString(), output.toString() });
assertThat(exitCode, is(0));
checkOutput(conf, output);
}

4.集群上的运行
以下会列出一些命令 但是最好还是参照Hadoop官方网站为佳
1).打包
新版的Hadoop 2.0用mvn对Hadoop进行打包 其实也可以用Eclipse打包 两者方法在实际中都可以，mav命令：

% mvn package -DskipTests

配置打包过程中注意对HADOOP_CLASSPATH的设置，和依赖包的导入等 详见上面 Hadoop官方网站

2).Job的启动
Job类中的waitForCompletion()启动Job并轮询检查Job的运行进程

3)Job、Task和Task Attempt ID
Job的ID一般来源本地时间 例如：job_200904110811_0002(0002，Job的ID从1开始)
Task隶属于Job 所以Task的ID是以Job的ID为前缀，然后加上一个后缀，表示Job下的哪一个Task，例如：task_200904110811_0002_m_000003(000003，Task的ID从0开始)
Task Attempt是由Task的生成 自然Task AttemptID的前缀为Task的ID，之后加上后缀，后面表示表示失败后尝试的次数，例如：attempt_200904110811_0002_m_000003_0(0，Task Attempt的ID从0开始)

3).MapReduce的Web页面
因为Hadoop经过改版一些web的页面的URL也不断变化，所以这个需要参照Hadoop的网站为佳

4).获取结果
hadoop fs 命令中的-getmerge，可以得到源模式目录中的所有文件，并在本地系统上将它们合并成一个文件，实例如下：

% hadoop fs -getmerge max-temp max-temp-local
% sort max-temp-local | tail

1991           607
1992           605
1993           567
1994           568
1995           567
1996           561
1997           565
1998           568
1999           568
2000           558

5).作业调试
可以利用Hadoop输出的log文件和一些其他信息(例如计数器等工具)，进行调试，并用web页面查看调试后的结果
关于远程调试器：可以用JVM选项，Java profiling够工具，IsolationRunner工具还有，如果为了监视失败作业的情况，可以设置keep.failed.task.files为true

5.作业调优
作业调优表：





对Job程序的修改可以启用HPROF工具，另外也有其他分析工具帮助调优，例如：DistributedCache等等

6.MapReduce的工作流
1).将问题分解成MapReduce作业
需要注意的是：对于十分复杂的问题 可以使用Hadoop自带ChainMapper类库将它们连接成一个Mapper，结合使用ChainReducer，这样就可以在一个MapReduce作业中运行一系列的mapper，再运行一个reducer和另一个mapper链。

2).运行独立的Job
管理作业的执行顺序。其中主要考虑的是：是否有一个线性的作业链或一个更复杂的作业有向无环图(DAG)

附一个自己写的NewMaxTemperatureDriver

package com.hadoop.definitive.guid.NewMaXTemperature;

import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;

import org.apache.commons.lang.exception.ExceptionUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IOUtils;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.compress.CompressionCodec;
import org.apache.hadoop.io.compress.CompressionCodecFactory;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

import com.hadoop.definitive.guid.customobject.TextPair;
import com.hadoop.definitive.guid.customobject.TextPairFileInputStream;

public class NewMaxTemperatureDriver  extends Configured implements Tool{

static class NewMaxTemperatureMapper extends
Mapper<LongWritable, TextPair, Text, IntWritable>{

private static final int MISSING = 9999;

public void map(LongWritable key, TextPair value, Context context)
throws IOException, InterruptedException {

//System.out.println(value.getFirst().toString() + "  " + value.getSecond().toString());
String line = value.getSecond().toString();
String year = line.substring(15, 19);
int airTemperature;
if (line.charAt(87) == '+') {
airTemperature = Integer.parseInt(line.substring(88, 92));
} else {
airTemperature = Integer.parseInt(line.substring(87, 92));
}
String quality = line.substring(92, 93);
if (airTemperature != MISSING && quality.matches("[01459]")) {
context.write(new Text(year), new IntWritable(airTemperature));
}
}
}

static class NewMaxTemperatureReducer extends
Reducer<Text, IntWritable, Text, IntWritable> {

public void reduce(Text key, Iterable<IntWritable> values,
Context context) throws IOException, InterruptedException {

int maxValue = Integer.MIN_VALUE;
for (IntWritable value : values) {
maxValue = Math.max(maxValue, value.get());
}
context.write(key, new IntWritable(maxValue));
}
}

@Override
public int run(String[] args) throws Exception {
if (args.length != 2) {
System.err.printf("Usage: %s [generic options] <input> <output>\n",
getClass().getSimpleName());
ToolRunner.printGenericCommandUsage(System.err);
return -1;
}

Configuration conf = new Configuration();

//add special client conf, default is using local
conf.addResource("conf/core-site.xml");

//setup compress format for mapper output
//conf.setBoolean("mapred.compress.map.output", true);
//conf.setClass("mapred.map.output.compression.codec", BZip2Codec.class, CompressionCodec.class);

//upload air resource file
FileSystem fs = FileSystem.get(conf);
//1st way: upload file to hdfs
//upload(fs, "/mnt/hgfs/Shared/1901", args[0]);
//upload(fs, "/mnt/hgfs/Shared/1902", args[0]);

//2st way: upload two zip file into hdfs system
upload(fs, "/mnt/hgfs/Shared/1901.gz", args[0]);
upload(fs, "/mnt/hgfs/Shared/1902.gz", args[0]);

if (fs.exists(new Path(args[1])))
fs.delete(new Path(args[1]), true);

Job job = new Job(conf);
job.setJarByClass(NewMaxTemperatureDriver.class);

FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));

//setup special input format, default is TextInputFormat
job.setInputFormatClass(TextPairFileInputStream.class);

job.setMapperClass(NewMaxTemperatureMapper.class);
job.setReducerClass(NewMaxTemperatureReducer.class);

job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);

//setup output compress format for whole job
//FileOutputFormat.setCompressOutput(job, true);
//FileOutputFormat.setOutputCompressorClass(job, GzipCodec.class);

return job.waitForCompletion(true) ? 0 : 1;
}

public static void main(String[] args) throws Exception {
int exitCode = ToolRunner.run(new NewMaxTemperatureDriver(), args);
System.exit(exitCode);
}

public synchronized static void upload(FileSystem fs, String local,
String remote) {

Path dst = new Path(remote);
Path src = new Path(local);
try {
fs.copyFromLocalFile(false, true, src, dst);
System.out.println("upload " + local + " to  " + remote
+ " successed. ");
} catch (Exception e) {
System.err.println("upload " + local + " to  " + remote
+ " failed :" + ExceptionUtils.getFullStackTrace(e));
}
}

public synchronized static void uploadCompressFile(FileSystem fs, String local,
String remote) {

Path dst = new Path(remote);
Path src = new Path(local);

CompressionCodecFactory factory = new CompressionCodecFactory(fs.getConf());
CompressionCodec codec = factory.getCodec(src);

InputStream in = null;
OutputStream out = null;
try {
in = codec.createInputStream(FileSystem.getLocal(fs.getConf()).open(src));
out = fs.create(dst, true);
IOUtils.copyBytes(in, out, fs.getConf(), true);

System.out.println("upload " + local + " to  " + remote
+ " successed. ");
} catch (Exception e) {
System.err.println("upload " + local + " to  " + remote
+ " failed :" + ExceptionUtils.getFullStackTrace(e));
}
}
}

import static org.junit.Assert.assertThat;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.junit.Test;

public class NewMaxTemperatureDriverTest {

@Test
public void test() throws Exception {
Configuration conf = new Configuration();
//conf.set("fs.default.name", "file:///");
//conf.set("mapred.job.tracker", "local");
Path input = new Path("/definitive_guid/ncdc/input");
Path output = new Path("/definitive_guid/ncdc/output/");

FileSystem fs = FileSystem.getLocal(conf);
fs.delete(output, true); // delete old output
NewMaxTemperatureDriver driver = new NewMaxTemperatureDriver();
driver.setConf(conf);
int exitCode = driver.run(new String[] {
input.toString(), output.toString() });

System.out.println("exitCode= " + exitCode);
//assertThat(exitCode, is(0));
//checkOutput(conf, output);
}

}

<?xml version="1.0" encoding="UTF-8"?>
<configuration>
<property>
<name>fs.default.name</name>
<value>hdfs://localhost:9000</value>
</property>
<property>
<name>hadoop.tmp.dir</name>
<value>/usr/local/hadoop/tmp</value>
<!--备注：请先在 /usr/hadoop 目录下建立 tmp 文件夹-->
<description>A base for other temporary directories.</description>
</property>
</configuration>