使用 java Mapreduce 处理 JSON

我是 hadoop mapreduce 新手

我输入了文本文件，其中数据已存储如下。这里只有几个元组（data.txt）

{"author":"Sharīf Qāsim","book":"al- Rabīʻ al-manshūd"}
{"author":"Nāṣir Nimrī","book":"Adīb ʻAbbāsī"}
{"author":"Muẓaffar ʻAbd al-Majīd Kammūnah","book":"Asmāʼ Allāh al-ḥusná al-wāridah fī muḥkam kitābih"}
{"author":"Ḥasan Muṣṭafá Aḥmad","book":"al- Jabhah al-sharqīyah wa-maʻārikuhā fī ḥarb Ramaḍān"}
{"author":"Rafīqah Salīm Ḥammūd","book":"Taʻlīm fī al-Baḥrayn"}

这是我的 java 文件，我应该在其中编写代码（CombineBooks.java）

package org.hwone;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.util.GenericOptionsParser;

//TODO import necessary components

/*
*  Modify this file to combine books from the same other into
*  single JSON object. 
*  i.e. {"author": "Tobias Wells", "books": [{"book":"A die in the country"},{"book": "Dinky died"}]}
*  Beaware that, this may work on anynumber of nodes! 
*
*/

public class CombineBooks {

  //TODO define variables and implement necessary components

  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    String[] otherArgs = new GenericOptionsParser(conf, args)
                .getRemainingArgs();
    if (otherArgs.length != 2) {
      System.err.println("Usage: CombineBooks <in> <out>");
      System.exit(2);
    }

    //TODO implement CombineBooks

    Job job = new Job(conf, "CombineBooks");

    //TODO implement CombineBooks

    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

我的任务是在“CombineBooks.java”中创建一个 Hadoop 程序 返回到“question-2”目录中。该程序应该做 以下：给定输入作者书籍元组，map-reduce 程序应该生成一个 JSON 对象，其中包含所有 JSON 数组中同一作者的书籍，即

{"author": "Tobias Wells", "books":[{"book":"A die in the country"},{"book": "Dinky died"}]} 

知道如何做到吗？

首先，您尝试使用的 JSON 对象不可用。为了解决这个问题：

1. 转到此处并下载 zip 文件：https://github.com/douglascrockford/JSON-java https://github.com/douglascrockford/JSON-java
2. 解压到子目录 org/json/* 中的源文件夹中

接下来，代码的第一行创建一个包“org.json”，这是不正确的，您应该创建一个单独的包，例如“my.books”。

第三，这里使用combiner是没有用的。

这是我最终得到的代码，它可以工作并解决您的问题：

package my.books;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.json.*;

import javax.security.auth.callback.TextInputCallback;

public class CombineBooks {

    public static class Map extends Mapper<LongWritable, Text, Text, Text>{

        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException{

            String author;
            String book;
            String line = value.toString();
            String[] tuple = line.split("\\n");
            try{
                for(int i=0;i<tuple.length; i++){
                    JSONObject obj = new JSONObject(tuple[i]);
                    author = obj.getString("author");
                    book = obj.getString("book");
                    context.write(new Text(author), new Text(book));
                }
            }catch(JSONException e){
                e.printStackTrace();
            }
        }
    }

    public static class Reduce extends Reducer<Text,Text,NullWritable,Text>{

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

            try{
                JSONObject obj = new JSONObject();
                JSONArray ja = new JSONArray();
                for(Text val : values){
                    JSONObject jo = new JSONObject().put("book", val.toString());
                    ja.put(jo);
                }
                obj.put("books", ja);
                obj.put("author", key.toString());
                context.write(NullWritable.get(), new Text(obj.toString()));
            }catch(JSONException e){
                e.printStackTrace();
            }
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        if (args.length != 2) {
            System.err.println("Usage: CombineBooks <in> <out>");
            System.exit(2);
        }

        Job job = new Job(conf, "CombineBooks");
        job.setJarByClass(CombineBooks.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);
        job.setOutputKeyClass(NullWritable.class);
        job.setOutputValueClass(Text.class);
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);

        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

这是我的项目的文件夹结构：

src
src/my
src/my/books
src/my/books/CombineBooks.java
src/org
src/org/json
src/org/json/zip
src/org/json/zip/BitReader.java
...
src/org/json/zip/None.java
src/org/json/JSONStringer.java
src/org/json/JSONML.java
...
src/org/json/JSONException.java

这是输入

[localhost:CombineBooks]$ hdfs dfs -cat /example.txt
{"author":"author1", "book":"book1"}
{"author":"author1", "book":"book2"}
{"author":"author1", "book":"book3"}
{"author":"author2", "book":"book4"}
{"author":"author2", "book":"book5"}
{"author":"author3", "book":"book6"}

要运行的命令：

hadoop jar ./bookparse.jar my.books.CombineBooks /example.txt /test_output

这是输出：

[pivhdsne:CombineBooks]$ hdfs dfs -cat /test_output/part-r-00000
{"books":[{"book":"book3"},{"book":"book2"},{"book":"book1"}],"author":"author1"}
{"books":[{"book":"book5"},{"book":"book4"}],"author":"author2"}
{"books":[{"book":"book6"}],"author":"author3"}

您可以使用这三个选项中的一个来放置org.json.*类到您的集群中：

1. 打包org.json.*类到您的 jar 文件中（可以使用 GUI IDE 轻松完成）。这是我在答案中使用的选项
2. 将包含的 jar 文件放入org.json.*将每个集群节点上的类放入 CLASSPATH 目录之一（请参阅yarn.application.classpath）
3. 将包含的 jar 文件放入org.json.*进入HDFS（hdfs dfs -put <org.json jar> <hdfs path>）并使用job.addFileToClassPath要求此 jar 文件可用于在集群上执行作业的所有任务。在我的回答中你应该添加job.addFileToClassPath(new Path("<jar_file_on_hdfs_location>")); to the main