文章目录
1. 倒排索引案例
1.1 需求
有大量的文本（文档、网页），需要建立搜索索引。
-
输入数据
① a.txt
atguigu pingping
atguigu ss
atguigu ss
② b.txt
atguigu pingping
atguigu pingping
pingping ss
③ c.txt
atguigu ss
atguigu pingping
- 期望输出数据
atguigu c.txt-->2 b.txt-->2 a.txt-->3
pingping c.txt-->1 b.txt-->3 a.txt-->1
ss c.txt-->1 b.txt-->1 a.txt-->2
1.2 需求分析
1.3 代码实现
1.3.1 第一次处理
- 第一次处理，编写 OneIndexMapper 类
package mr.index;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
public class OneIndexMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
String name;
Text k = new Text();
IntWritable v = new IntWritable();
@Override
protected void setup(Context context) throws IOException, InterruptedException {
// 获取文件名称
FileSplit split = (FileSplit) context.getInputSplit();
name = split.getPath().getName();
}
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
// 1 获取1行
String line = value.toString();
// 2 切割
String[] fields = line.split(" ");
for (String word : fields) {
// 3 拼接
k.set(word + "--" + name);
v.set(1);
// 4 写出
context.write(k, v);
}
}
}
- 第一次处理，编写 OneIndexReducer 类
package mr.index;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class OneIndexReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
IntWritable v = new IntWritable();
@Override
protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
int sum = 0;
// 1 累加求和
for (IntWritable value : values) {
sum += value.get();
}
v.set(sum);
// 2 写出
context.write(key, v);
}
}
- 第一次处理，编写 OneIndexDriver 类
package mr.index;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class OneIndexDriver {
public static void main(String[] args) throws Exception {
// 输入输出路径需要根据自己电脑上实际的输入输出路径设置
args = new String[]{"f:/0/input/index", "f:/0/output/index"};
Configuration conf = new Configuration();
Job job = Job.getInstance(conf);
job.setJarByClass(OneIndexDriver.class);
job.setMapperClass(OneIndexMapper.class);
job.setReducerClass(OneIndexReducer.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.waitForCompletion(true);
}
}
- 查看第一次输出结果
atguigu--a.txt 3
atguigu--b.txt 2
atguigu--c.txt 2
pingping--a.txt 1
pingping--b.txt 3
pingping--c.txt 1
ss--a.txt 2
ss--b.txt 1
ss--c.txt 1
1.3.2 第二次处理
- 第二次处理，编写 TwoIndexMapper 类
package mr.index;
import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class TwoIndexMapper extends Mapper<LongWritable, Text, Text, Text> {
Text k = new Text();
Text v = new Text();
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
// 1 获取1行数据
String line = value.toString();
// 2用“--”切割
String[] fields = line.split("--");
k.set(fields[0]);
v.set(fields[1]);
// 3 输出数据
context.write(k, v);
}
}
- 第二次处理，编写 TwoIndexReducer 类
package mr.index;
import java.io.IOException;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class TwoIndexReducer extends Reducer<Text, Text, Text, Text> {
Text v = new Text();
@Override
protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
// atguigu a.txt 3
// atguigu b.txt 2
// atguigu c.txt 2
// atguigu c.txt-->2 b.txt-->2 a.txt-->3
StringBuilder sb = new StringBuilder();
// 1 拼接
for (Text value : values) {
sb.append(value.toString().replace("\t", "-->") + "\t");
}
v.set(sb.toString());
// 2 写出
context.write(key, v);
}
}
- 第二次处理，编写 TwoIndexDriver 类
package mr.index;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class TwoIndexDriver {
public static void main(String[] args) throws Exception {
// 输入输出路径需要根据自己电脑上实际的输入输出路径设置
args = new String[]{"f:/0/output/index", "f:/output/index2"};
Configuration config = new Configuration();
Job job = Job.getInstance(config);
job.setJarByClass(TwoIndexDriver.class);
job.setMapperClass(TwoIndexMapper.class);
job.setReducerClass(TwoIndexReducer.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
boolean result = job.waitForCompletion(true);
System.exit(result ? 0 : 1);
}
}
- 查看第二次输出结果
atguigu c.txt-->2 b.txt-->2 a.txt-->3
pingping c.txt-->1 b.txt-->3 a.txt-->1
ss c.txt-->1 b.txt-->1 a.txt-->2
2. TopN 案例
2.1 需求
输出流量使用量在前 10 的用户信息
-
输入数据
top10.txt
扫描二维码关注公众号，回复： 11342051 查看本文章
13470253144 180 180 360
13509468723 7335 110349 117684
13560439638 918 4938 5856
13568436656 3597 25635 29232
13590439668 1116 954 2070
13630577991 6960 690 7650
13682846555 1938 2910 4848
13729199489 240 0 240
13736230513 2481 24681 27162
13768778790 120 120 240
13846544121 264 0 264
13956435636 132 1512 1644
13966251146 240 0 240
13975057813 11058 48243 59301
13992314666 3008 3720 6728
15043685818 3659 3538 7197
15910133277 3156 2936 6092
15959002129 1938 180 2118
18271575951 1527 2106 3633
18390173782 9531 2412 11943
18418841387 4116 1432 5548
2.2 代码实现
- 编写 FlowBean 类
package mr.top;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.WritableComparable;
public class FlowBean implements WritableComparable<FlowBean> {
private long upFlow;
private long downFlow;
private long sumFlow;
public FlowBean() {
super();
}
public FlowBean(long upFlow, long downFlow) {
super();
this.upFlow = upFlow;
this.downFlow = downFlow;
}
@Override
public void write(DataOutput out) throws IOException {
out.writeLong(upFlow);
out.writeLong(downFlow);
out.writeLong(sumFlow);
}
@Override
public void readFields(DataInput in) throws IOException {
upFlow = in.readLong();
downFlow = in.readLong();
sumFlow = in.readLong();
}
public long getUpFlow() {
return upFlow;
}
public void setUpFlow(long upFlow) {
this.upFlow = upFlow;
}
public long getDownFlow() {
return downFlow;
}
public void setDownFlow(long downFlow) {
this.downFlow = downFlow;
}
public long getSumFlow() {
return sumFlow;
}
public void setSumFlow(long sumFlow) {
this.sumFlow = sumFlow;
}
@Override
public String toString() {
return upFlow + "\t" + downFlow + "\t" + sumFlow;
}
public void set(long downFlow2, long upFlow2) {
downFlow = downFlow2;
upFlow = upFlow2;
sumFlow = downFlow2 + upFlow2;
}
@Override
public int compareTo(FlowBean bean) {
int result;
if (this.sumFlow > bean.getSumFlow()) {
result = -1;
} else if (this.sumFlow < bean.getSumFlow()) {
result = 1;
} else {
result = 0;
}
return result;
}
}
- 编写 TopNMapper 类
package mr.top;
import java.io.IOException;
import java.util.Iterator;
import java.util.TreeMap;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class TopNMapper extends Mapper<LongWritable, Text, FlowBean, Text> {
// 定义一个TreeMap作为存储数据的容器（天然按key排序）
private TreeMap<FlowBean, Text> flowMap = new TreeMap<FlowBean, Text>();
private FlowBean kBean;
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
kBean = new FlowBean();
Text v = new Text();
// 1 获取一行
String line = value.toString();
// 2 切割
String[] fields = line.split("\t");
// 3 封装数据
String phoneNum = fields[0];
long upFlow = Long.parseLong(fields[1]);
long downFlow = Long.parseLong(fields[2]);
long sumFlow = Long.parseLong(fields[3]);
kBean.setDownFlow(downFlow);
kBean.setUpFlow(upFlow);
kBean.setSumFlow(sumFlow);
v.set(phoneNum);
// 4 向TreeMap中添加数据
flowMap.put(kBean, v);
// 5 限制TreeMap的数据量，超过10条就删除掉流量最小的一条数据
if (flowMap.size() > 10) {
flowMap.remove(flowMap.lastKey());
}
}
@Override
protected void cleanup(Context context) throws IOException, InterruptedException {
// 6 遍历treeMap集合，输出数据
Iterator<FlowBean> bean = flowMap.keySet().iterator();
while (bean.hasNext()) {
FlowBean k = bean.next();
context.write(k, flowMap.get(k));
}
}
}
- 编写 TopNReducer 类
package mr.top;
import java.io.IOException;
import java.util.Iterator;
import java.util.TreeMap;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class TopNReducer extends Reducer<FlowBean, Text, Text, FlowBean> {
// 定义一个TreeMap作为存储数据的容器（天然按key排序）
TreeMap<FlowBean, Text> flowMap = new TreeMap<FlowBean, Text>();
@Override
protected void reduce(FlowBean key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
for (Text value : values) {
FlowBean bean = new FlowBean();
bean.set(key.getDownFlow(), key.getUpFlow());
// 1 向treeMap集合中添加数据
flowMap.put(bean, new Text(value));
// 2 限制TreeMap数据量，超过10条就删除掉流量最小的一条数据
if (flowMap.size() > 10) {
flowMap.remove(flowMap.lastKey());
}
}
}
@Override
protected void cleanup(Reducer<FlowBean, Text, Text, FlowBean>.Context context) throws IOException, InterruptedException {
// 3 遍历集合，输出数据
Iterator<FlowBean> it = flowMap.keySet().iterator();
while (it.hasNext()) {
FlowBean v = it.next();
context.write(new Text(flowMap.get(v)), v);
}
}
}
- 编写 TopNDriver 类
package mr.top;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class TopNDriver {
public static void main(String[] args) throws Exception {
args = new String[]{"f:/0/input/top10.txt", "f:/0/output/top10"};
// 1 获取配置信息，或者job对象实例
Configuration configuration = new Configuration();
Job job = Job.getInstance(configuration);
// 6 指定本程序的jar包所在的本地路径
job.setJarByClass(TopNDriver.class);
// 2 指定本业务job要使用的mapper/Reducer业务类
job.setMapperClass(TopNMapper.class);
job.setReducerClass(TopNReducer.class);
// 3 指定mapper输出数据的kv类型
job.setMapOutputKeyClass(FlowBean.class);
job.setMapOutputValueClass(Text.class);
// 4 指定最终输出的数据的kv类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(FlowBean.class);
// 5 指定job的输入原始文件所在目录
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
// 7 将job中配置的相关参数，以及job所用的java类所在的jar包， 提交给yarn去运行
boolean result = job.waitForCompletion(true);
System.exit(result ? 0 : 1);
}
}
- 查看输出数据
13509468723 7335 110349 117684
13975057813 11058 48243 59301
13568436656 3597 25635 29232
13736230513 2481 24681 27162
18390173782 9531 2412 11943
13630577991 6960 690 7650
15043685818 3659 3538 7197
13992314666 3008 3720 6728
15910133277 3156 2936 6092
13560439638 918 4938 5856
3. 找共同好友案例
3.1 需求
以下是好友列表数据，冒号前是一个用户，冒号后是该用户的所有好友（数据中的好友关系是单向的）求出哪些人两两之间有共同好友，及他俩的共同好友都有谁？
-
输入数据
friends.txt
A:B,C,D,F,E,O
B:A,C,E,K
C:F,A,D,I
D:A,E,F,L
E:B,C,D,M,L
F:A,B,C,D,E,O,M
G:A,C,D,E,F
H:A,C,D,E,O
I:A,O
J:B,O
K:A,C,D
L:D,E,F
M:E,F,G
O:A,H,I,J
3.2 需求分析
-
第一次 MR
目的： 先求出 A、B、C、…. 等是谁的好友。
方法： map 阶段以冒号前的为 value，冒号后的每一个值为 key。reduce 阶段将多个 value 拼接在一起。
输出：
A I,K,C,B,G,F,H,O,D,
B A,F,J,E,
C A,E,B,H,F,G,K,
D G,C,K,A,L,F,E,H,
E G,M,L,H,A,F,B,D,
F L,M,D,C,G,A,
G M,
H O,
I O,C,
J O,
K B,
L D,E,
M E,F,
O A,H,I,J,F,
-
第二次 MR
目的： 找出 两个人间的共同好友。
方法： map 阶段以 “\t” 前的为 value， “\t” 后的每两个值为 key。reduce 阶段将多个 value 拼接在一起。
输出：
A-B E C
A-C D F
A-D E F
A-E D B C
A-F O B C D E
A-G F E C D
A-H E C D O
A-I O
A-J O B
A-K D C
A-L F E D
A-M E F
B-C A
B-D A E
B-E C
B-F E A C
B-G C E A
B-H A E C
B-I A
B-K C A
B-L E
B-M E
B-O A
C-D A F
C-E D
C-F D A
C-G D F A
C-H D A
C-I A
C-K A D
C-L D F
C-M F
C-O I A
D-E L
D-F A E
D-G E A F
D-H A E
D-I A
D-K A
D-L E F
D-M F E
D-O A
E-F D M C B
E-G C D
E-H C D
E-J B
E-K C D
E-L D
F-G D C A E
F-H A D O E C
F-I O A
F-J B O
F-K D C A
F-L E D
F-M E
F-O A
G-H D C E A
G-I A
G-K D A C
G-L D F E
G-M E F
G-O A
H-I O A
H-J O
H-K A C D
H-L D E
H-M E
H-O A
I-J O
I-K A
I-O A
K-L D
K-O A
L-M E F
3.3 代码实现
3.3.1 第一次处理
- 第一次 Mapper 类
package mr.friends;
import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class OneShareFriendsMapper extends Mapper<LongWritable, Text, Text, Text> {
Text k = new Text();
Text v = new Text();
@Override
protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, Text>.Context context)
throws IOException, InterruptedException {
// 1 获取一行 A:B,C,D,F,E,O
String line = value.toString();
// 2 切割
String[] fields = line.split(":");
// 3 获取person和好友
String person = fields[0];
String[] friends = fields[1].split(",");
v.set(person);
// 4写出去
for (String friend : friends) {
k.set(friend);
// 输出 <好友，人>
context.write(k, v);
}
}
}
- 第一次 Reducer 类
import java.io.IOException;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class OneShareFriendsReducer extends Reducer<Text, Text, Text, Text> {
Text v = new Text();
@Override
protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
StringBuffer sb = new StringBuffer();
//1 拼接
for (Text person : values) {
sb.append(person).append(",");
}
//2 写出
v.set(sb.toString());
context.write(key, v);
}
}
- 第一次 Driver 类
package mr.friends;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class OneShareFriendsDriver {
public static void main(String[] args) throws Exception {
// 输入输出路径需要根据自己电脑上实际的输入输出路径设置
args = new String[]{"f:/0/input/friends.txt", "f:/0/output/friends1"};
// 1 获取job对象
Configuration configuration = new Configuration();
Job job = Job.getInstance(configuration);
// 2 指定jar包运行的路径
job.setJarByClass(OneShareFriendsDriver.class);
// 3 指定map/reduce使用的类
job.setMapperClass(OneShareFriendsMapper.class);
job.setReducerClass(OneShareFriendsReducer.class);
// 4 指定map输出的数据类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class);
// 5 指定最终输出的数据类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
// 6 指定job的输入原始所在目录
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
// 7 提交
boolean result = job.waitForCompletion(true);
System.exit(result ? 0 : 1);
}
}
3.3.2 第二次处理
- 第二次 Mapper 类
package mr.friends;
import java.io.IOException;
import java.util.Arrays;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class TwoShareFriendsMapper extends Mapper<LongWritable, Text, Text, Text> {
Text k = new Text();
Text v = new Text();
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
// A I,K,C,B,G,F,H,O,D,
// 友 人，人，人
String line = value.toString();
String[] friend_persons = line.split("\t");
String friend = friend_persons[0];
String[] persons = friend_persons[1].split(",");
Arrays.sort(persons);
v.set(friend);
for (int i = 0; i < persons.length - 1; i++) {
for (int j = i + 1; j < persons.length; j++) {
k.set(persons[i] + "-" + persons[j]);
// 发出 <人-人，好友> ，这样，相同的“人-人”对的所有好友就会到同1个reduce中去
context.write(k, v);
}
}
}
}
- 第二次 Reducer 类
package mr.friends;
import java.io.IOException;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class TwoShareFriendsReducer extends Reducer<Text, Text, Text, Text> {
Text v = new Text();
@Override
protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
StringBuffer sb = new StringBuffer();
for (Text friend : values) {
sb.append(friend).append(" ");
}
v.set(sb.toString());
context.write(key, v);
}
}
- 第二次 Driver 类
package mr.friends;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class TwoShareFriendsDriver {
public static void main(String[] args) throws Exception {
// 输入输出路径需要根据自己电脑上实际的输入输出路径设置
args = new String[]{"f:/0/output/friends1", "f:/0/output/friends2"};
// 1 获取job对象
Configuration configuration = new Configuration();
Job job = Job.getInstance(configuration);
// 2 指定jar包运行的路径
job.setJarByClass(TwoShareFriendsDriver.class);
// 3 指定map/reduce使用的类
job.setMapperClass(TwoShareFriendsMapper.class);
job.setReducerClass(TwoShareFriendsReducer.class);
// 4 指定map输出的数据类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class);
// 5 指定最终输出的数据类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
// 6 指定job的输入原始所在目录
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
// 7 提交
boolean result = job.waitForCompletion(true);
System.exit(result ? 0 : 1);
}
}