使用hadoop mapreduce分析mongodb数据：

Posted 2020-07-23

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 +