使用hadoop mapreduce分析mongodb数据：（1）

最近考虑使用hadoop mapreduce来分析mongodb上的数据，从网上找了一些demo，东拼西凑，终于运行了一个demo，下面把过程展示给大家

环境

  • ubuntu 14.04 64bit
  • hadoop 2.6.4
  • mongodb 2.4.9
  • Java 1.8
  • mongo-hadoop-core-1.5.2.jar
  • mongo-java-driver-3.0.4.jar

mongo-hadoop-core-1.5.2.jar以及mongo-java-driver-3.0.4.jar的下载和配置

  • 编译mongo-hadoop-core-1.5.2.jar
  • $ git clone https://github.com/mongodb/mongo-hadoop
$ cd mongo-hadoop
$ ./gradlew jar
    • 编译时间比较长，成功编译之后mongo-hadoop-core-1.5.2.jar存在的路径是core/build/libs
  • 下载mongo-java-driver-3.0.4.jar
  • http://central.maven.org/maven2/org/mongodb/mongo-java-driver/3.0.4/
    选择     mongo-java-driver-3.0.4.jar

数据

  • 数据样例
  • > db.in.find({})
{ "_id" : ObjectId("5758db95ab12e17a067fbb6f"), "x" : "hello world" }
{ "_id" : ObjectId("5758db95ab12e17a067fbb70"), "x" : "nice to meet you" }
{ "_id" : ObjectId("5758db95ab12e17a067fbb71"), "x" : "good to see you" }
{ "_id" : ObjectId("5758db95ab12e17a067fbb72"), "x" : "world war 2" }
{ "_id" : ObjectId("5758db95ab12e17a067fbb73"), "x" : "see you again" }
{ "_id" : ObjectId("5758db95ab12e17a067fbb74"), "x" : "bye bye" }
  • 最后的结果
  • > db.out.find({})
{ "_id" : "2", "value" : 1 }
{ "_id" : "again", "value" : 1 }
{ "_id" : "bye", "value" : 2 }
{ "_id" : "good", "value" : 1 }
{ "_id" : "hello", "value" : 1 }
{ "_id" : "meet", "value" : 1 }
{ "_id" : "nice", "value" : 1 }
{ "_id" : "see", "value" : 2 }
{ "_id" : "to", "value" : 2 }
{ "_id" : "war", "value" : 1 }
{ "_id" : "world", "value" : 2 }
{ "_id" : "you", "value" : 3 }
  • 目标是统计每个文档中出现的词频，并且把单词作为key，词频作为value存在mongodb中

Hadoop mapreduce代码

  • Mapreduce 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 
 9 import org.apache.hadoop.conf.Configuration;
10 import org.apache.hadoop.io.*;
11 import org.apache.hadoop.mapreduce.*;
12 
13 
14 public class WordCount {
15     public static class TokenizerMapper extends Mapper<Object, BSONObject, Text, IntWritable> {
16         private final static IntWritable one = new IntWritable(1);
17         private Text word = new Text();
18         public void map(Object key, BSONObject value, Context context ) 
19                 throws IOException, InterruptedException {
20             System.out.println( "key: " + key );
21             System.out.println( "value: " + value );
22             StringTokenizer itr = new StringTokenizer(value.get( "x" ).toString());
23             while (itr.hasMoreTokens()) {
24                 word.set(itr.nextToken());
25                 context.write(word, one);
26             }
27         }
28     }
29     public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable> {
30         private IntWritable result = new IntWritable();
31         public void reduce(Text key, Iterable<IntWritable> values, Context context )
32             throws IOException, InterruptedException {
33             int sum = 0;
34             for (IntWritable val : values) {
35                 sum += val.get();
36             }
37             result.set(sum);
38             context.write(key, result);
39         }
40     }
41     public static void main(String[] args) throws Exception {
42         Configuration conf = new Configuration();
43         conf.set( "mongo.input.uri" , "mongodb://localhost/testmr.in" );
44         conf.set( "mongo.output.uri" , "mongodb://localhost/testmr.out" );
45         @SuppressWarnings("deprecation")
46         Job job = new Job(conf, "word count");
47         job.setJarByClass(WordCount.class);
48         job.setMapperClass(TokenizerMapper.class);
49         job.setCombinerClass(IntSumReducer.class);
50         job.setReducerClass(IntSumReducer.class);
51         job.setOutputKeyClass(Text.class);
52         job.setOutputValueClass(IntWritable.class);
53         job.setInputFormatClass( MongoInputFormat.class );
54         job.setOutputFormatClass( MongoOutputFormat.class );
55         System.exit(job.waitForCompletion(true) ? 0 : 1);
56     }
57 }
    • 注意：设置mongo.input.uri和mongo.output.uri 
      1 conf.set( "mongo.input.uri" , "mongodb://localhost/testmr.in" );
2 conf.set( "mongo.output.uri" , "mongodb://localhost/testmr.out" );
  • 编译
    • 编译 
      $ hadoop com.sun.tools.javac.Main WordCount.java -Xlint:deprecation
    • 编译jar包 
      $ jar cf wc.jar WordCount*.class
  • 运行
    • 启动hadoop，运行mapreduce代码必须启动hadoop 
      $ start-all.sh
    • 运行程序
    • $ hadoop jar  wc.jar WordCount
  • 查看结果
  • $ mongo
MongoDB shell version: 2.4.9
connecting to: test
> use testmr;
switched to db testmr
> db.out.find({})
{ "_id" : "2", "value" : 1 }
{ "_id" : "again", "value" : 1 }
{ "_id" : "bye", "value" : 2 }
{ "_id" : "good", "value" : 1 }
{ "_id" : "hello", "value" : 1 }
{ "_id" : "meet", "value" : 1 }
{ "_id" : "nice", "value" : 1 }
{ "_id" : "see", "value" : 2 }
{ "_id" : "to", "value" : 2 }
{ "_id" : "war", "value" : 1 }
{ "_id" : "world", "value" : 2 }
{ "_id" : "you", "value" : 3 }
>

     

以上是一个简单的例子，接下来我要用hadoop mapreduce处理mongodb中的更加复杂的数据。敬请期待，如果有疑问，请在留言区提出 ^_^

 

参考资料以及文档

  1. The elephant in the room mongo db + hadoop
  2. http://chenhua-1984.iteye.com/blog/2162576
  3. http://api.mongodb.com/java/2.12/com/mongodb/MongoURI.html
  4. http://stackoverflow.com/questions/27020075/mongo-hadoop-connector-issue

如果The elephant in the room mongo db + hadoop打不开，请到我的github下载ppt

posted @ 2016-06-09 12:03  GoingMyWay  阅读(2386)  评论(0)  编辑  收藏  举报