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Hadoop: Running MapReduce jobs in batch mode on different data sets?

+2 votes
110 views

Is it possible to run jobs on Hadoop in batch mode? I have 5 different datasets in HDFS and need to run the same MapReduce application on these datasets sets one after the other.

Right now I am doing it manually How can I automate this? How can I save the log of each execution in text files for later processing?

posted Feb 21, 2015 by anonymous

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1 Answer

+1 vote

Create 5 new jobs for 5 datasets. Change the input directory in each job. Actually there will 5 jobs but in a single mapreduce driver class. So you dont need to execute each manually.

answer Apr 10, 2015 by Sudhakar Singh
Similar Questions
+1 vote

To run a job we use the command
$ hadoop jar example.jar inputpath outputpath
If job is so time taken and we want to stop it in middle then which command is used? Or is there any other way to do that?

+1 vote

A mapreduce job can be run as jar file from terminal or directly from eclipse IDE. When a job run as jar file from terminal it uses multiple jvm and all resources of cluster. Does the same thing happen when we run from IDE. I have run a job on both and it takes less time on IDE than jar file on terminal.

+2 votes
public class MaxMinReducer extends Reducer {
int max_sum=0; 
int mean=0;
int count=0;
Text max_occured_key=new Text();
Text mean_key=new Text("Mean : ");
Text count_key=new Text("Count : ");
int min_sum=Integer.MAX_VALUE; 
Text min_occured_key=new Text();

 public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
       int sum = 0;           

       for (IntWritable value : values) {
             sum += value.get();
             count++;
       }

       if(sum < min_sum)
          {
              min_sum= sum;
              min_occured_key.set(key);        
          }     


       if(sum > max_sum) {
           max_sum = sum;
           max_occured_key.set(key);
       }          

       mean=max_sum+min_sum/count;
  }

 @Override
 protected void cleanup(Context context) throws IOException, InterruptedException {
       context.write(max_occured_key, new IntWritable(max_sum));   
       context.write(min_occured_key, new IntWritable(min_sum));   
       context.write(mean_key , new IntWritable(mean));   
       context.write(count_key , new IntWritable(count));   
 }
}

Here I am writing minimum,maximum and mean of wordcount.

My input file :

high low medium high low high low large small medium

Actual output is :

high - 3------maximum

low - 3--------maximum

large - 1------minimum

small - 1------minimum

but i am not getting above output ...can anyone please help me?

0 votes

I was trying to implement a Hadoop/Spark audit tool, but l met a problem that I can't get the input file location and file name. I can get username, IP address, time, user command, all of these info from hdfs-audit.log. But When I submit a MapReduce job, I can't see input file location neither in Hadoop logs or Hadoop ResourceManager.

Does hadoop have API or log that contains these info through some configuration ?If it have, what should I configure?

+2 votes

Let we change the default block size to 32 MB and replication factor to 1. Let Hadoop cluster consists of 4 DNs. Let input data size is 192 MB. Now I want to place data on DNs as following. DN1 and DN2 contain 2 blocks (32+32 = 64 MB) each and DN3 and DN4 contain 1 block (32 MB) each. Can it be possible? How to accomplish it?

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