mapreduce计数和查找平均值

inn6fuwd 于 2021-05-29 发布在 Hadoop

关注(0)|答案(2)|浏览(386)

我想在mapreduce中开发一个程序，从.tbl文件中获取cust\u key和balance值。我将这两个值连接成字符串，然后将其发送到reducer，因此我将计算cust\u key并找到每个段的平均余额。这就是为什么我将该段添加为key。
我想拆分字符串并将这两个值分开，以便计算cust键数并对余额求和以找到平均值。但是拆分数组[0]给出的是整个字符串，而不是字符串的第一个值。同时拆分数组[1]引发ArrayYoutofBounds异常。我希望清楚。
代码如下

public class MapReduceTest {

        public static class TokenizerMapper extends Mapper<Object, Text, Text, Text>{

         private Text segment = new Text();

         private Text word = new Text();

         private float balance = 0;

         public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
           String[] line = value.toString().split("\\|");

           balance = Float.parseFloat(line[5]);

           String cust_key = line[1];

           int nation = Integer.parseInt(line[3]);

           if((balance > 8000) && ( nation < 15) && (nation > 1)){ 

             segment.set(line[6]);

             //word.set(cust_key+","+balance);

             word.set(cust_key+","+balance);

             context.write(segment,word);
           }
         }

       }

    public static class AvgReducer extends Reducer<Text,Text,Text,Text> {

         Text val = new Text();

    public void reduce(Text key, Iterable<Text> values,Context context) throws IOException, InterruptedException {

         String cust_key = "";
         float avg,sum = 0;
         int count = 0;
            for(Text v : values){
                 String[] a = v.toString().trim().split(",");

                 cust_key +=a[0];

            }

            val.set(cust_count);

            context.write(key, val);

     }

   }

输入数据

8794|Customer#000008794|6dnUgJZGX73Kx1idr6|18|28-434-484-9934|7779.30|HOUSEHOLD|deposits detect furiously even requests. furiously ironic packages are slyly into th
8795|Customer#000008795|oA1cLUtWOAIFz5Douypbq1jHv glSE|9|19-829-732-8102|9794.80|BUILDING|totes. blithely unusual theodolites integrate carefully ironic foxes. unusual excuses cajole carefully carefully fi
8796|Customer#000008796|CzCzpV7SDojXUzi4165j,xYJuRv wZzn grYsyZ|24|34-307-411-6825|4323.03|AUTOMOBILE|s. pending, bold accounts above the sometimes express accounts 
8797|Customer#000008797|TOWDryHNNqp8bvgMW6 FAhRoLyG1ldu2bHcJCM6|2|12-517-522-5820|219.78|FURNITURE|ly bold pinto beans can nod blithely quickly regular requests. fluffily even deposits ru
8798|Customer#000008798|bIegyozQ5kzprN|15|25-472-647-6270|6832.96|AUTOMOBILE|es-- silent instructions nag blithely

堆栈跟踪

java.lang.Exception: java.lang.ArrayIndexOutOfBoundsException: 1
        at org.apache.hadoop.mapred.LocalJobRunner$Job.runTasks(LocalJobRunner.java:462)
        at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:529)
Caused by: java.lang.ArrayIndexOutOfBoundsException: 1
        at MapReduceTest$AvgReducer.reduce(MapReduceTest.java:69)
        at MapReduceTest$AvgReducer.reduce(MapReduceTest.java:1)
        at org.apache.hadoop.mapreduce.Reducer.run(Reducer.java:171)
        at org.apache.hadoop.mapred.ReduceTask.runNewReducer(ReduceTask.java:627)
        at org.apache.hadoop.mapred.ReduceTask.run(ReduceTask.java:389)
        at org.apache.hadoop.mapred.LocalJobRunner$Job$ReduceTaskRunnable.run(LocalJobRunner.java:319)
        at java.util.concurrent.Executors$RunnableAdapter.call(Unknown Source)
        at java.util.concurrent.FutureTask.run(Unknown Source)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
        at java.lang.Thread.run(Unknown Source)
17/04/12 18:40:33 INFO mapreduce.Job: Job job_local806960399_0001 running in uber mode : false
17/04/12 18:40:33 INFO mapreduce.Job:  map 100% reduce 0%
17/04/12 18:40:33 INFO mapreduce.Job: Job job_local806960399_0001 failed with state FAILED due to: NA
17/04/12 18:40:33 INFO mapreduce.Job: Counters: 35

更新
减速机

public static class AvgReducer extends Reducer<Text,Text,Text,Text> {

    Logger log = Logger.getLogger(AvgReducer.class.getName());

    public void reduce(Text key, Iterable<Text> values,Context context) throws IOException, InterruptedException {

            float sumBalance=0,avgBalance = 0;

            int cust_count = 1;

            for(Text v : values){
               String[] a = v.toString().trim().split(",");

               //c2 += " i "+i+" "+a[0]+"\n";

               sumBalance +=Float.parseFloat(a[a.length-1]);

               cust_count++;
            }

            avgBalance = sumBalance / cust_count;

            context.write(key,new Text(avgBalance+" "+cust_count));

     }

   }

堆栈跟踪

java.lang.Exception: java.lang.NumberFormatException: For input string: "8991.715 289"

提前谢谢。

Java hadoop mapreduce Mapper

来源：https://stackoverflow.com/questions/43305537/mapreduce-count-and-find-average

2条答案

按热度按时间

7y4bm7vi1#

pig运行mapreduce（如果这样配置的话）。它也比在mapreduce上乱搞干净得多，并且安装在主要的hadoop发行版上。

A = LOAD 'test.txt' USING PigStorage('|') AS (f1:int,customer_key:chararray,f3:chararray,nation:int,f5:chararray,balance:float,segment:chararray,f7:chararray);
filtered = FILTER A BY balance > 8000 AND (nation > 1 AND nation < 15);
X = FOREACH filtered generate segment,customer_key,balance;

和输出

\d X
(BUILDING,Customer#000008795,9794.8)

不确定你真的想要平均值，这里只有一个元素，但是你需要 GROUP BY 上 segment 以及 customer_key ，这样您就可以方便地使用 AVG 功能。
如果您更熟悉sql，那么hive也可能是一种更直接的方法。
（除非另有配置，否则也通过mapreduce运行）

CREATE EXTERNAL TABLE IF NOT EXISTS records (
    f1 INT,
    customer_key STRING, 
    f3 STRING, 
    nation INT,
    f5 STRING,
    balance FLOAT,
    f8 STRING
) ROW FORMAT DELIMETED 
FIELDS TERMINATED BY '|'
LOCATION 'hdfs://path/test.txt';

然后，会是这样的

SELECT segment, customer_key, AVG(balance)
FROM records
WHERE balance > 8000 AND nation > 1 AND nation < 15
GROUP BY segment, customer_key;

我将进入apachespark示例，但sparksql本质上就是这个配置单元查询。

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nkhmeac62#

如果您真的想在javamapreduce中尝试这一点，请尝试标准化您的输入并快速捕获错误。
返回以丢弃有问题的记录

public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
       float balance = 0.0;
       String custKey = "";
       String segment = "";
       int nation = 0;

       String[] line = value.toString().split("\\|");
       if (line.length < 7) { 
           System.err.println("map: Not enough records");
           return;
       }
       cust_key = line[1];
       try {
           nation = Integer.parseInt(line[3]);
           balance = Float.parseFloat(line[5]);
       } catch (NumberFormatException e) {
           e.printStackTrace();
           return;
       }

       if(balance > 8000 && (nation < 15 && nation > 1)){ 
         segment.set(line[6]);
         word.set(cust_key + "\t" + balance);
         context.write(segment,word);
       }
  }

然后，如果您试图编写类似的输出记录，那么reducer应该理想地生成相同的格式

public void reduce(Text key, Iterable<Text> values,Context context) throws IOException, InterruptedException {

        float sumBalance=0
        int count = 0;

        for(Text v : values){
           String[] a = v.toString().trim().split("\t");
           if (a.length < 2) {
               System.err.println("reduce: Not enough records");
               continue;
           }

           sumBalance += Float.parseFloat(a[1]);
           count++;
        }

        float avgBalance = count <= 1 ? sumBalance : sumBalance / count;

        context.write(key,new Text(avgBalance + "\t" + count));

 }

（代码未测试）

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mapreduce计数和查找平均值

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