我想在 MapReduce 中开发一个程序，它从 .tbl 文件中获取 cust_key 和 balance 值。我已将 2 个值连接成字符串，然后将其发送到 Reducer，因此我将计算 cust_key 并找到平均余额每个段。这就是为什么我将段添加为键。

我想拆分字符串并将 2 个值分开，以便计算客户键并对余额求和以找到平均值。但是拆分数组 [0] 给我整个字符串，而不是字符串的第一个值.Also splitted array[1] 抛出 ArrayoutofBounds 异常。我希望它很清楚。

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

reducer

    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"

提前致谢。

最佳答案

Pig 运行 MapReduce(如果以这种方式配置)。它也比使用 MapReduce 更干净，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;

并输出

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

不确定你真的想要这里的平均值，只有一个元素，但你需要 GROUP BY在 segment 上和 customer_key , 那么你可以很容易地使用 AVG function .

如果您更熟悉 SQL，那么 Hive 也可能是一种更直接的方法。

(除非另外配置，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';

然后就是这样

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 Apache Spark 示例，但 Spark SQL 本质上就是 Hive 查询。

关于java - MapReduce 计数并求平均值，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/43305537/