[TOC]
# 準備數據
~~~
Order_0000001,pd001,222.8
Order_0000001,pd005,25.8
Order_0000002,pd005,325.8
Order_0000002,pd003,522.8
Order_0000002,pd004,122.4
Order_0000003,pd001,222.8
Order_0000003,pd001,322.8
~~~

他是記錄訂單編號,商品和成交金額
然后取出每個訂單的top1和topN的數據
里面需要用到一個分組的
1. 利用“訂單id和成交金額”作為key,可以將map階段讀取到的所有訂單數據按照id分區,按照金額排序,發送到reduce
2. 在reduce端利用GroupingComparator將訂單id相同的kv聚合成組,然后取第一個即是最大值
# top1代碼
**OrderBean**
~~~
package com.top;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
public class OrderBean implements WritableComparable<OrderBean> {
private Text itemid;
private DoubleWritable amount;
public OrderBean() {
}
public OrderBean(Text itemid, DoubleWritable amount) {
set(itemid, amount);
}
public void set(Text itemid, DoubleWritable amount) {
this.itemid = itemid;
this.amount = amount;
}
public Text getItemid() {
return itemid;
}
public DoubleWritable getAmount() {
return amount;
}
@Override
public int compareTo(OrderBean o) {
//比較他的訂單id
int cmp = this.itemid.compareTo(o.getItemid());
//如果訂單id相同就比較金額
if (cmp == 0) {
//-號表示倒敘
cmp = -this.amount.compareTo(o.getAmount());
}
return cmp;
}
@Override
public void write(DataOutput out) throws IOException {
out.writeUTF(itemid.toString());
out.writeDouble(amount.get());
}
@Override
public void readFields(DataInput in) throws IOException {
String readUTF = in.readUTF();
double readDouble = in.readDouble();
this.itemid = new Text(readUTF);
this.amount = new DoubleWritable(readDouble);
}
@Override
public String toString() {
return "OrderBean{" +
"itemid=" + itemid +
", amount=" + amount +
'}';
}
}
~~~
**ItemIdPartitioner**
~~~
package com.top;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.Partitioner;
public class ItemIdPartitioner extends Partitioner<OrderBean, NullWritable> {
@Override
public int getPartition(OrderBean key, NullWritable nullWritable, int numPartitions) {
//模擬源碼中寫的,保證一個訂單中的相同bean的id一定能分到同一個地方
return (key.getItemid().hashCode() & Integer.MAX_VALUE) % numPartitions;
}
}
~~~
**ItemidGroupingComparator**
~~~
package com.top;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
public class ItemidGroupingComparator extends WritableComparator {
protected ItemidGroupingComparator() {
//一定要調用下super,里面放你要比較的對象
super(OrderBean.class, true);
}
//他會傳入2個你上面的寫的對象,比如這邊是2個bean
@Override
public int compare(WritableComparable a, WritableComparable b) {
//把這個bean強行轉換下
OrderBean abean = (OrderBean) a;
OrderBean bbean = (OrderBean) b;
//取出這2個bean,如果這2個bean的id相比較是一樣就放到一起
return abean.getItemid().compareTo(bbean.getItemid());
}
}
~~~
**TopOne**
~~~
package com.top;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
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.StringUtils;
import java.io.IOException;
public class TopOne {
public static class TopOneMapper extends Mapper<LongWritable, Text, OrderBean, NullWritable> {
OrderBean bean = new OrderBean();
// Text itemid = new Text();
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
String[] fields = StringUtils.split(line, ',');
bean.set(new Text(fields[0]), new DoubleWritable(Double.parseDouble(fields[2])));
context.write(bean, NullWritable.get());
}
}
public static class TopOneReducer extends Reducer<OrderBean, NullWritable, OrderBean, NullWritable> {
@Override
protected void reduce(OrderBean key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
context.write(key, NullWritable.get());
}
}
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf);
job.setJarByClass(TopOne.class);
job.setMapperClass(TopOneMapper.class);
job.setReducerClass(TopOneReducer.class);
job.setOutputKeyClass(OrderBean.class);
job.setOutputValueClass(NullWritable.class);
FileInputFormat.setInputPaths(job, new Path("/Users/jdxia/Desktop/website/hdfs/index/input"));
//如果有這個文件夾就刪除
Path out = new Path("/Users/jdxia/Desktop/website/hdfs/index/output/");
FileSystem fileSystem = FileSystem.get(conf);
if (fileSystem.exists(out)) {
fileSystem.delete(out, true);
}
//告訴框架,我們的處理結果要輸出到什么地方
FileOutputFormat.setOutputPath(job, out);
//注冊一個GroupingComparator
job.setGroupingComparatorClass(ItemidGroupingComparator.class);
job.setPartitionerClass(ItemIdPartitioner.class);
job.setNumReduceTasks(1);
job.waitForCompletion(true);
}
}
~~~
# topN代碼
bean中要添加
~~~
@Override
public boolean equals(Object o) {
OrderBean bean = (OrderBean) o;
return bean.getItemid().equals(this.itemid);
}
~~~
主類中修改
~~~
package com.top;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
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.StringUtils;
import java.io.IOException;
public class TopN {
static class TopNMapper extends Mapper<LongWritable, Text, OrderBean, OrderBean> {
OrderBean v = new OrderBean();
Text k = new Text();
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
String[] fields = StringUtils.split(line, ',');
k.set(fields[0]);
v.set(new Text(fields[0]), new DoubleWritable(Double.parseDouble(fields[2])));
context.write(v, v);
}
}
static class TopNReducer extends Reducer<OrderBean, OrderBean, NullWritable, OrderBean> {
int topn = 1;
int count = 0;
@Override
protected void setup(Context context) throws IOException, InterruptedException {
Configuration conf = context.getConfiguration();
topn = Integer.parseInt(conf.get("topn"));
}
@Override
protected void reduce(OrderBean key, Iterable<OrderBean> values, Context context) throws IOException, InterruptedException {
count = 0;
for (OrderBean bean : values) {
if ((count++) == topn) {
return;
}
context.write(NullWritable.get(), bean);
}
}
}
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
Configuration conf = new Configuration();
// ?如果要寫配置文件就這樣寫
// conf.addResource("userconfig.xml");
// System.out.println(conf.get("top.n"));
// 我這邊就直接設置要求top2了
conf.set("topn", "2");
Job job = Job.getInstance(conf);
job.setJarByClass(TopN.class);
job.setMapperClass(TopNMapper.class);
job.setReducerClass(TopNReducer.class);
job.setOutputKeyClass(OrderBean.class);
job.setOutputValueClass(OrderBean.class);
FileInputFormat.setInputPaths(job, new Path("/Users/jdxia/Desktop/website/hdfs/index/input"));
//如果有這個文件夾就刪除
Path out = new Path("/Users/jdxia/Desktop/website/hdfs/index/output/");
FileSystem fileSystem = FileSystem.get(conf);
if (fileSystem.exists(out)) {
fileSystem.delete(out, true);
}
//告訴框架,我們的處理結果要輸出到什么地方
FileOutputFormat.setOutputPath(job, out);
//注冊一個GroupingComparator
job.setGroupingComparatorClass(ItemidGroupingComparator.class);
job.setPartitionerClass(ItemIdPartitioner.class);
job.setNumReduceTasks(1);
job.waitForCompletion(true);
}
}
~~~
- linux
- 常用命令
- 高級文本命令
- 面試題
- redis
- String
- list
- hash
- set
- sortedSet
- 案例-推薦
- java高級特性
- 多線程
- 實現線程的三種方式
- 同步關鍵詞
- 讀寫鎖
- 鎖的相關概念
- 多線程的join
- 有三個線程T1 T2 T3,保證順序執行
- java五種線程池
- 守護線程與普通線程
- ThreadLocal
- BlockingQueue消息隊列
- JMS
- 反射
- volatile
- jvm
- IO
- nio
- netty
- netty簡介
- 案例一發送字符串
- 案例二發送對象
- 輕量級RPC開發
- 簡介
- spring(IOC/AOP)
- spring初始化順序
- 通過ApplicationContextAware加載Spring上下文
- InitializingBean的作用
- 結論
- 自定義注解
- zk在框架中的應用
- hadoop
- 簡介
- hadoop集群搭建
- hadoop單機安裝
- HDFS簡介
- hdfs基本操作
- hdfs環境搭建
- 常見問題匯總
- hdfs客戶端操作
- mapreduce工作機制
- 案列-單詞統計
- 局部聚合Combiner
- 案列-流量統計(分區,排序,比較)
- 案列-倒排索引
- 案例-共同好友
- 案列-join算法實現
- 案例-求topN(分組)
- 自定義inputFormat
- 自定義outputFormat
- 框架運算全流程
- mapreduce的優化方案
- HA機制
- Hive
- 安裝
- DDL操作
- 創建表
- 修改表
- DML操作
- Load
- insert
- select
- join操作
- 嚴格模式
- 數據類型
- shell參數
- 函數
- 內置運算符
- 內置函數
- 自定義函數
- Transform實現
- 特殊分割符處理
- 案例
- 級聯求和accumulate
- flume
- 簡介
- 安裝
- 常用的組件
- 攔截器
- 案例
- 采集目錄到HDFS
- 采集文件到HDFS
- 多個agent串聯
- 日志采集和匯總
- 自定義攔截器
- 高可用配置
- 使用注意
- sqoop
- 安裝
- 數據導入
- 導入數據到HDFS
- 導入關系表到HIVE
- 導入表數據子集
- 增量導入
- 數據導出
- 作業
- 原理
- azkaban
- 簡介
- 安裝
- 案例
- 簡介
- command類型單一job
- command類型多job工作流flow
- HDFS操作任務
- mapreduce任務
- hive腳本任務
- hbase
- 簡介
- 安裝
- 命令行
- 基本CURD
- 過濾器查詢
- 系統架構
- 物理存儲
- 尋址機制
- 讀寫過程
- Region管理
- master工作機制
- 建表高級屬性
- 與mapreduce結合
- 協處理器
- 點擊流平臺開發
- 簡介
- storm
- 簡介
- 安裝
- 集群啟動及任務過程分析
- 單詞統計
- 并行度
- ACK容錯機制
- ACK簡介