大數據教程（10.4）訂單中成交金額最大的訂單項分析
上一篇博客分享了日誌清洗的實現，本篇文章博主將爲小夥伴們分享"訂單中成交金額最大的訂單項分析"的案例。java
1、需求 apache
分析出下圖中每一個訂單中成交金額最大的訂單項centos
#訂單號,商品號,成交金額 Order_0000001,Pdt_01,222.8 Order_0000001,Pdt_05,25.8 Order_0000002,Pdt_05,325.8 Order_0000002,Pdt_03,522.8 Order_0000002,Pdt_04,122.4 Order_0000003,Pdt_01,222.8
2、代碼實現服務器
OrderBean（訂單bean類實現）app
package com.empire.hadoop.mr.secondarysort;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
/**
* 類 OrderBean.java的實現描述：訂單bean類
*
* @author arron 2018年12月23日 上午12:22:31
*/
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) {
int cmp = this.itemid.compareTo(o.getItemid());
if (cmp == 0) {
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 itemid.toString() + "\t" + amount.get();
}
}
ItemIdPartitioner（訂單bean分區類實現）框架
package com.empire.hadoop.mr.secondarysort;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.Partitioner;
/**
* 類 ItemIdPartitioner.java的實現描述：分區實現類
*
* @author arron 2018年12月23日 上午12:24:02
*/
public class ItemIdPartitioner extends Partitioner<OrderBean, NullWritable> {
@Override
public int getPartition(OrderBean bean, NullWritable value, int numReduceTasks) {
//相同id的訂單bean，會發往相同的partition
//並且，產生的分區數，是會跟用戶設置的reduce task數保持一致
return (bean.getItemid().hashCode() & Integer.MAX_VALUE) % numReduceTasks;
}
}
ItemidGroupingComparator（訂單bean分組實現）ide
package com.empire.hadoop.mr.secondarysort;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
/**
* 類
* ItemidGroupingComparator.java的實現描述：利用reduce端的GroupingComparator來實現將一組bean當作相同的key
*
* @author arron 2018年12月23日 上午12:23:35
*/
public class ItemidGroupingComparator extends WritableComparator {
//傳入做爲key的bean的class類型，以及制定須要讓框架作反射獲取實例對象
protected ItemidGroupingComparator() {
super(OrderBean.class, true);
}
@Override
public int compare(WritableComparable a, WritableComparable b) {
OrderBean abean = (OrderBean) a;
OrderBean bbean = (OrderBean) b;
//比較兩個bean時，指定只比較bean中的orderid
return abean.getItemid().compareTo(bbean.getItemid());
}
}
SecondarySort（訂單分析主程序實現）oop
package com.empire.hadoop.mr.secondarysort;
import java.io.IOException;
import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.conf.Configuration;
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;
/**
* 類 SecondarySort.java的實現描述：訂單處理主程序類
*
* @author arron 2018年12月23日 上午12:22:57
*/
public class SecondarySort {
static class SecondarySortMapper extends Mapper<LongWritable, Text, OrderBean, NullWritable> {
OrderBean bean = new OrderBean();
@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());
}
}
static class SecondarySortReducer extends Reducer<OrderBean, NullWritable, OrderBean, NullWritable> {
//到達reduce時，相同id的全部bean已經被當作一組，且金額最大的那個一排在第一位
@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 Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf);
job.setJarByClass(SecondarySort.class);
job.setMapperClass(SecondarySortMapper.class);
job.setReducerClass(SecondarySortReducer.class);
job.setOutputKeyClass(OrderBean.class);
job.setOutputValueClass(NullWritable.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
//在此設置自定義的Groupingcomparator類
job.setGroupingComparatorClass(ItemidGroupingComparator.class);
//在此設置自定義的partitioner類
job.setPartitionerClass(ItemIdPartitioner.class);
job.setNumReduceTasks(2);
job.waitForCompletion(true);
}
}
3、運行程序大數據
#上傳jar Alt+p lcd d:/ put ordergp.jar put ordergp.txt #準備hadoop處理的數據文件 cd /home/hadoop hadoop fs -mkdir -p /ordergp/gpinput hdfs dfs -put ordergp.txt /ordergp/gpinput #運行程序 hadoop jar ordergp.jar com.empire.hadoop.mr.secondarysort.SecondarySort /ordergp/gpinput /ordergp/gpoutput
4、運行效果this
[hadoop@centos-aaron-h1 ~]$ hadoop jar ordergp.jar com.empire.hadoop.mr.secondarysort.SecondarySort /ordergp/gpinput /ordergp/gpoutput 18/12/23 08:12:37 INFO client.RMProxy: Connecting to ResourceManager at centos-aaron-h1/192.168.29.144:8032
18/12/23 08:12:38 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
18/12/23 08:12:38 INFO mapreduce.JobSubmitter: number of splits:1
18/12/23 08:12:38 INFO Configuration.deprecation: yarn.resourcemanager.system-metrics-publisher.enabled is deprecated. Instead, use yarn.system-metrics-publisher.enabled
18/12/23 08:12:38 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1545512861141_0004
18/12/23 08:12:38 INFO impl.YarnClientImpl: Submitted application application_1545512861141_0004
18/12/23 08:12:38 INFO mapreduce.Job: The url to track the job: http://centos-aaron-h1:8088/proxy/application_1545512861141_0004/
18/12/23 08:12:38 INFO mapreduce.Job: Running job: job_1545512861141_0004
18/12/23 08:12:46 INFO mapreduce.Job: Job job_1545512861141_0004 running in uber mode : false
18/12/23 08:12:46 INFO mapreduce.Job: map 0% reduce 0%
18/12/23 08:12:52 INFO mapreduce.Job: map 100% reduce 0%
18/12/23 08:12:58 INFO mapreduce.Job: map 100% reduce 100%
18/12/23 08:12:58 INFO mapreduce.Job: Job job_1545512861141_0004 completed successfully
18/12/23 08:12:58 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=162
FILE: Number of bytes written=593176
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=285
HDFS: Number of bytes written=60
HDFS: Number of read operations=9
HDFS: Number of large read operations=0
HDFS: Number of write operations=4
Job Counters
Launched map tasks=1
Launched reduce tasks=2
Data-local map tasks=1
Total time spent by all maps in occupied slots (ms)=4568
Total time spent by all reduces in occupied slots (ms)=6712
Total time spent by all map tasks (ms)=4568
Total time spent by all reduce tasks (ms)=6712
Total vcore-milliseconds taken by all map tasks=4568
Total vcore-milliseconds taken by all reduce tasks=6712
Total megabyte-milliseconds taken by all map tasks=4677632
Total megabyte-milliseconds taken by all reduce tasks=6873088
Map-Reduce Framework
Map input records=6
Map output records=6
Map output bytes=138
Map output materialized bytes=162
Input split bytes=120
Combine input records=0
Combine output records=0
Reduce input groups=3
Reduce shuffle bytes=162
Reduce input records=6
Reduce output records=3
Spilled Records=12
Shuffled Maps =2
Failed Shuffles=0
Merged Map outputs=2
GC time elapsed (ms)=269
CPU time spent (ms)=2620
Physical memory (bytes) snapshot=454995968
Virtual memory (bytes) snapshot=2544693248
Total committed heap usage (bytes)=154140672
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters
Bytes Read=165
File Output Format Counters
Bytes Written=60
[hadoop@centos-aaron-h1 ~]$
5、運行結果
[hadoop@centos-aaron-h1 ~]$ hdfs dfs -ls /ordergp/gpoutput Found 3 items -rw-r--r-- 2 hadoop supergroup 0 2018-12-23 08:12 /ordergp/gpoutput/_SUCCESS -rw-r--r-- 2 hadoop supergroup 20 2018-12-23 08:12 /ordergp/gpoutput/part-r-00000 -rw-r--r-- 2 hadoop supergroup 40 2018-12-23 08:12 /ordergp/gpoutput/part-r-00001 [hadoop@centos-aaron-h1 ~]$ hdfs dfs -cat /ordergp/gpoutput/part-r-00000 Order_0000002 522.8 [hadoop@centos-aaron-h1 ~]$ hdfs dfs -cat /ordergp/gpoutput/part-r-00001 Order_0000001 222.8 Order_0000003 222.8 [hadoop@centos-aaron-h1 ~]$
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