成为mapreduce＆amp;的新手hadoop world，在尝试了基本的mapreduce程序之后，我想尝试使用compositekey示例代码。
输入数据集如下：
国家，州，县，populationinmillions
USA，CA，Alameda的100
USA，CA，losangels，200
USA，CA，萨克拉门托，100
USA，FL，xxx，10
USA，FL，YYY，12
所需的输出数据应如下所示：
USA，CA，500
美国，佛罗里达州，22
此处，Country + State字段构成复合键。 我得到以下输出。由于某种原因，人口没有增加。有人能指出我正在做的错误。另请参阅实现WriteableComparable接口的Country.java类。这种实现可能有问题。
USA，CA，100
USA，CA，200
USA，CA，100
USA，FL，10
USA，FL，12
每个国家/地区都没有增加人口。
这是实现WritableComparable接口的Country类。
import java.io.DataInput;
import java.io.DataOutput;
import java.io.File;
import java.io.IOException;
import java.util.Iterator;
import org.apache.commons.io.FileUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
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;
* The Country class implements WritabelComparator to implements custom sorting to perform group by operation. It
* sorts country and then state.
*
*/
public class Country implements WritableComparable<Country> {
Text country;
Text state;
public Country(Text country, Text state) {
this.country = country;
this.state = state;
}
public Country() {
this.country = new Text();
this.state = new Text();
}
/*
* (non-Javadoc)
*
* @see org.apache.hadoop.io.Writable#write(java.io.DataOutput)
*/
public void write(DataOutput out) throws IOException {
this.country.write(out);
this.state.write(out);
}
/*
* (non-Javadoc)
*
* @see org.apache.hadoop.io.Writable#readFields(java.io.DataInput)
*/
public void readFields(DataInput in) throws IOException {
this.country.readFields(in);
this.state.readFields(in);
;
}
/*
* (non-Javadoc)
*
* @see java.lang.Comparable#compareTo(java.lang.Object)
*/
public int compareTo(Country pop) {
if (pop == null)
return 0;
int intcnt = country.compareTo(pop.country);
if (intcnt != 0) {
return intcnt;
} else {
return state.compareTo(pop.state);
}
}
/*
* (non-Javadoc)
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
return country.toString() + ":" + state.toString();
}
}
驱动程序：
import java.io.DataInput;
import java.io.DataOutput;
import java.io.File;
import java.io.IOException;
import java.util.Iterator;
import org.apache.commons.io.FileUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
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;
public class CompositeKeyDriver {
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, "CompositeKeyDriver");
//first argument is job itself
//second argument is location of the input dataset
FileInputFormat.addInputPath(job, new Path(args[0]));
//first argument is the job itself
//second argument is the location of the output path
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setJarByClass(CompositeKeyDriver.class);
job.setMapperClass(CompositeKeyMapper.class);
job.setReducerClass(CompositeKeyReducer.class);
job.setOutputKeyClass(Country.class);
job.setOutputValueClass(IntWritable.class);
//setting the second argument as a path in a path variable
Path outputPath = new Path(args[1]);
//deleting the output path automatically from hdfs so that we don't have delete it explicitly
outputPath.getFileSystem(conf).delete(outputPath);
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
Mapper程序：
import java.io.DataInput;
import java.io.DataOutput;
import java.io.File;
import java.io.IOException;
import java.util.Iterator;
import org.apache.commons.io.FileUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
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;
// First two parameters are Input Key and Input Value. Input Key = offset of each line (remember each line is a record). Input value = Line itself
// Second two parameters are Output Key and Output value of the Mapper. BTW, the outputs of the mapper are stored in the local file system and not on HDFS.
// Output Key = Country object is sent. Output Value = population in millions in that country + state combination
public class CompositeKeyMapper extends Mapper<LongWritable, Text, Country, IntWritable> {
/** The cntry. */
Country cntry = new Country();
/** The cnt text. */
Text cntText = new Text();
/** The state text. */
Text stateText = new Text();
//population in a Country + State
IntWritable populat = new IntWritable();
/**
*
* Reducer are optional in Map-Reduce if there is no Reducer defined in program then the output of the Mapper
* directly write to disk without sorting.
*
*/
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
//Reader will give each record in a line to the Mapper.
//That line is split with the de-limiter ","
String line = value.toString();
String[] keyvalue = line.split(",");
//Country is the first item in the line in each record
cntText.set(new Text(keyvalue[0]));
//State is the second item in the line in each record
stateText.set(keyvalue[1]);
//This is the population. BTW, we can't send Java primitive datatypes into Context object. Java primitive data types are not effective in Serialization and De-serialization.
//So we have to use the equivalent Writable datatypes provided by mapreduce framework
populat.set(Integer.parseInt(keyvalue[3]));
//Here you are creating an object of Country class and in the constructor assigning the country name and state
Country cntry = new Country(cntText, stateText);
//Here you are passing the country object and their population to the context object.
//Remember that country object already implements "WritableComparable" interface which is equivalient to "Comparable" interface in Java. That implementation is in Country.java class
//Because it implements the WritableComparable interface, the Country objects can be sorted in the shuffle phase. If WritableComparable interface is not implemented, we
//can't sort the objects.
context.write(cntry, populat);
}
}
减速计划
import java.io.DataInput;
import java.io.DataOutput;
import java.io.File;
import java.io.IOException;
import java.util.Iterator;
import org.apache.commons.io.FileUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.FloatWritable;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
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;
//Remember the two output parameters of the Mapper class will become the first two input parameters to the reducer class.
public class CompositeKeyReducer extends Reducer<Country, IntWritable, Country, IntWritable> {
// The first parameter to reduce method is "Country". The country object has country name and state name (look at the Country.java class for more details.
// The second parameter "values" is the collection of population for Country+State (this is a composite Key)
public void reduce(Country key, Iterator<IntWritable> values, Context context) throws IOException, InterruptedException {
int numberofelements = 0;
int cnt = 0;
while (values.hasNext()) {
cnt = cnt + values.next().get();
}
context.write(key, new IntWritable(cnt));
}
}
答案 0 :(得分：1)
您正在使用HashPartitioner，因此您的Country课程需要实施hashCode()方法。
目前它将使用hashCode()上的默认Object实施，这将导致您的密钥无法正确分组。
以下是hashCode()方法示例：
@Override
public int hashCode() {
final int prime = 31;
int result = 1;
result = prime * result + ((country == null) ? 0 : country.hashCode());
result = prime * result + ((state == null) ? 0 : state.hashCode());
return result;
}
其他信息：
为了安全起见，你应该set文本对象。目前，您在Country构造函数中执行此操作。
public Country(Text country, Text state) {
this.country = country;
this.state = state;
}
您应该将其更改为：
public Country(Text country, Text state) {
this.country.set(country);
this.state.set(state);
}
答案 1 :(得分：0)
减速机问题现已解决。我没有对代码进行任何更改。我所做的只是重新启动我的Cloudera Hadoop图像。
我在调试过程中注意到以下内容。有人可以评论这些观察结果吗？
答案 2 :(得分：0)
我遇到了与Basam相同的问题，尽管重新启动Cloudera不足以解决它。
在CompositeKeyReducer类中，我将 Iterator 替换为 Iterable 和其他几行代码：
public void reduce(TextPair key, Iterable<IntWritable> values,
Context context) throws IOException, InterruptedException {
int numberofelements = 0;
int cnt = 0;
for (IntWritable value : values) {
cnt += value.get();
}
context.write(key, new IntWritable(cnt));
结果：
美国：CA 500
美国：FL 22