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HDFS and YARN security and interface impacts

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We have an application that interfaces directly to HDFS and YARN (no MapReduce). It does not currently support any Hadoop security other than the insecure "trust the client" defaults. I've been doing some reading about Hadoop security, but it mostly assumes that applications will be MapReduce. For a "native" YARN/HDFS application, what changes if any must be made to the API calls to support Kerberos or other authentication?

Does it just happen automatically at the OS level using the authenticated user ID of the process? If there's a good reference I'd appreciate it.

posted Apr 20, 2014 by Sidharth

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+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));   
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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?

+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?

+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?

+1 vote

In xmls configuration file of Hadoop-2.x, "mapreduce.input.fileinputformat.split.minsize" is given which can be set but how to set "mapreduce.input.fileinputformat.split.maxsize" in xml file. I need to set it in my mapreduce code.

+3 votes

Date date; long start, end; // for recording start and end time of job
date = new Date(); start = date.getTime(); // starting timer

job.waitForCompletion(true)

date = new Date(); end = date.getTime(); //end timer
log.info("Total Time (in milliseconds) = "+ (end-start));
log.info("Total Time (in seconds) = "+ (end-start)*0.001F);

I am not sure this is the correct way to find. Is there any other method or API to find the execution time of a MapReduce job?

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