使用hadoop mapreduce分析mongodb数据：

Posted 2020-07-26

在上一篇使用hadoop mapreduce分析mongodb数据：（１）中，介绍了如何使用Hadoop MapReduce连接MongoDB数据库以及如何处理数据库，本文结合一个案例来进一步说明Hadoop MapReduce处理MongoDB的细节

• 原始数据

• > db.stackin.find({})
  { "_id" : ObjectId("575ce909aa02c3b21f1be0bb"), "summary" : "good good day",  "url" : "url_1" }
  { "_id" : ObjectId("575ce909aa02c3b21f1be0bc"), "summary" : "hello world good world", "url" : "url_2" }
  { "_id" : ObjectId("575ce909aa02c3b21f1be0bd"), "summary" : "hello world good hello good", "url" : "url_3" }
  { "_id" : ObjectId("575ce909aa02c3b21f1be0be"), "summary" : "hello world hello",  "url" : "url_4" }

  每一个记录表示一个网页，summary对应的值是网页的文章，url对应的值是该文章的链接

• 目标结果

• > db.stackout.find({})
  { "_id" : "world", "data" : [  {  "url_2" : 2 },  {  "url_3" : 1 },  {  "url_4" : 1 } ], "index" : 0, "maxindex" : 3 }
  { "_id" : "good", "data" : [  {  "url_1" : 2 },  {  "url_2" : 1 },  {  "url_3" : 2 } ], "index" : 0, "maxindex" : 3 }
  { "_id" : "day", "data" : [  {  "url_1" : 1 } ], "index" : 0, "maxindex" : 1 }
  { "_id" : "hello", "data" : [  {  "url_2" : 1 },  {  "url_3" : 2 },  {  "url_4" : 2 } ], "index" : 0, "maxindex" : 3 }

  我们需要统计每个单词在每个网页中分别出现的次数，从结果可知，单词world在每个url出现的次数

• 设计代码
  

   1 import java.util.*; 
   2 import java.io.*;
   3 
   4 import org.bson.*;
   5 
   6 import com.mongodb.hadoop.MongoInputFormat;
   7 import com.mongodb.hadoop.MongoOutputFormat;
   8 import com.mongodb.hadoop.io.BSONWritable;
   9 
  10 import org.apache.hadoop.conf.Configuration;
  11 import org.apache.hadoop.io.*;
  12 import org.apache.hadoop.mapreduce.*;
  13 
  14 
  15 public class WordCount {
  16 
  17     public static class TokenizerMapper extends Mapper<Object, BSONObject, Text, BSONWritable> {
  18         //private final static 
  19         private Text word = new Text();
  20 
  21         public void map(Object key, BSONObject value, Context context ) 
  22                 throws IOException, InterruptedException {
  23             String url = value.get("url").toString();
  24             StringTokenizer itr = new StringTokenizer(value.get("summary").toString().
  25                     replaceAll("\\\\p{Punct}|\\\\d","").replaceAll("\\r\\n", " ").replace("\\r", " ").
  26                     replace("\\n", " ").toLowerCase());
  27             while (itr.hasMoreTokens()) {
  28                 word.set(itr.nextToken());
  29                 BasicBSONObject urlCounts = new BasicBSONObject();
  30                 urlCounts.put(url, 1);
  31                 context.write(word, new BSONWritable(urlCounts));
  32             }
  33         }
  34     }
  35 
  36     public static class IntSumReducer extends Reducer<Text, BSONWritable, Text, BSONWritable> {
  37         //private BasicBSONObject result = new BasicBSONObject();
  38         
  39         public void reduce(Text key, Iterable<BSONWritable> values, Context context)
  40             throws IOException, InterruptedException {
  41             HashMap<String, Integer> mymap = new HashMap<String, Integer>();
  42             BasicBSONObject result = new BasicBSONObject();
  43             BasicBSONObject urlcount = new BasicBSONObject();
  44             for (BSONWritable val : values) {
  45                 @SuppressWarnings("unchecked")
  46                 BSONObject temp2 = val.getDoc();
  47                 @SuppressWarnings("unchecked")
  48                 HashMap<String, Integer> temp = (HashMap<String, Integer>) val.getDoc().toMap();
  49                 for (Map.Entry<String, Integer> entry :  temp.entrySet()) {
  50                     if (mymap.containsKey(entry.getKey())) {
  51                         mymap.put(entry.getKey(), entry.getValue()+1);
  52                     }
  53                     else {
  54                         mymap.put(entry.getKey(), 1);
  55                     }
  56                 }
  57             }
  58             result.putAll(mymap);
  59             context.write(key, new BSONWritable(result));
  60         }
  61     }
  62 
  63     public static void main(String[] args) throws Exception {
  64         Configuration conf = new Configuration();
  65         conf.set( "mongo.input.uri" , "mongodb://localhost/stackoverflow.stackin" );
  66         conf.set( "mongo.output.uri" , "mongodb://localhost/stackoverflow.stackout" );
  67         @SuppressWarnings("deprecation")
  68         Job job = new Job(conf, "word count");
  69         job.setJarByClass(WordCount.class);
  70         job.setMapperClass(TokenizerMapper.class);
  71         //job.setCombinerClass(IntSumReducer.class);
  72         job.setReducerClass(IntSumReducer.class);
  73         job.setMapOutputKeyClass(Text.class);
  74         job.setMapOutputValueClass(BSONWritable.class);
  75         job.setOutputKeyClass(Text.class);
  76         job.setOutputValueClass(BSONWritable.class);
  77         job.setInputFormatClass( MongoInputFormat.class );
  78         job.setOutputFormatClass( MongoOutputFormat.class );
  79         System.exit(job.waitForCompletion(true) ? 0 : 1);
  80     }
  81 }

  设计的思路是，在map部分得到一个word以及键为url值为1的Bson对象，然后写入content中。对应的，在reduce部分对传入的值进行统计。

 

总结：本案例很简单，但是需要明白Hadoop MapReduce的原理以及mongo-hadoop API中对象的使用。如果有疑问，可以在评论区提出～