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>