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使用hadoop mapreduce分析mongodb数据:(1)

[日期:2016-06-12] 来源:博客园精华区  作者: [字体: ]

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

Hadoop

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 +





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