top button
Flag Notify
    Connect to us
      Site Registration

Site Registration

Hadoop: How reduce tasks know which partition they should read?

+3 votes
607 views

I am looking to the Yarn mapreduce internals to try to understand how reduce tasks know which partition of the map output they should read. Even, when they re-execute after a crash?

I am also looking to the mapreduce source code. Is there any class that I should look to try to understand this question?

posted Mar 9, 2015 by anonymous

Share this question
Facebook Share Button Twitter Share Button LinkedIn Share Button

2 Answers

+1 vote

The reducers(Fetcher.java) simply ask the Shuffle Service (ShuffleHandler.java) to give them output corresponding to a specific map. The partitioning detail is hidden from the reducers.

answer Mar 9, 2015 by Jagan Mishra
0 votes

Hadoop uses default partitioner. You can customize it for according to your need too.

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

How a job works in YARN/Map Reduce? like navigation path.

Please check my understanding is right?

When the application or job or client starts, client communicate with Name node the application manager started on node (data node), Application manager communicates with Resource manager (on name node) to get resource.The resource are assigned to container. The job runs on Container which is JVM.

+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?

+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?

+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?

+3 votes

As I studied that data distribution, load balancing, fault tolerance are implicit in Hadoop. But I need to customize it, can we do that?

...