YARN vs. MapReduce Hadoop

What You’ll Learn 📚

• Understanding the core concepts of YARN and MapReduce
• Key terminology and definitions
• Simple and progressively complex examples
• Common questions and troubleshooting tips

Introduction to YARN and MapReduce

Before we jump into the details, let’s get a quick overview of what YARN and MapReduce are. Hadoop is a framework that allows for the distributed processing of large data sets across clusters of computers. Within Hadoop, MapReduce is a programming model for processing large data sets with a parallel, distributed algorithm. On the other hand, YARN (Yet Another Resource Negotiator) is a resource management layer for Hadoop that allows multiple data processing engines to handle data stored in a single platform.

Key Terminology

• MapReduce: A programming model for processing and generating large data sets with a parallel, distributed algorithm.
• YARN: A resource management platform responsible for managing compute resources in clusters and using them to schedule users’ applications.
• Node Manager: A per-machine framework agent responsible for containers, monitoring their resource usage (CPU, memory, disk, network) and reporting the same to the ResourceManager.
• Resource Manager: The master daemon of YARN that manages resources and schedules applications running on top of YARN.

Simple Example: MapReduce

import java.io.IOException;import java.util.StringTokenizer;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;public class WordCount {public static class TokenizerMapper extends Mapper {private final static IntWritable one = new IntWritable(1);private Text word = new Text();public void map(Object key, Text value, Context context) throws IOException, InterruptedException {StringTokenizer itr = new StringTokenizer(value.toString());while (itr.hasMoreTokens()) {word.set(itr.nextToken());context.write(word, one);}}}public static class IntSumReducer extends Reducer {private IntWritable result = new IntWritable();public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {int sum = 0;for (IntWritable val : values) {sum += val.get();}result.set(sum);context.write(key, result);}}public static void main(String[] args) throws Exception {Configuration conf = new Configuration();Job job = Job.getInstance(conf, "word count");job.setJarByClass(WordCount.class);job.setMapperClass(TokenizerMapper.class);job.setCombinerClass(IntSumReducer.class);job.setReducerClass(IntSumReducer.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(IntWritable.class);FileInputFormat.addInputPath(job, new Path(args[0]));FileOutputFormat.setOutputPath(job, new Path(args[1]));System.exit(job.waitForCompletion(true) ? 0 : 1);}}

Progressively Complex Examples

Example 1: Running a Simple YARN Application

YARN allows you to run applications in a distributed environment. Let’s see a basic example of running a simple YARN application.

# Start the YARN Resource Manager and Node Manager$ start-yarn.sh# Submit a simple YARN application$ yarn jar /path/to/your/application.jar

Example 2: Advanced MapReduce with Custom Partitioner

Let’s enhance our MapReduce example by adding a custom partitioner to control how keys are distributed across reducers.

import org.apache.hadoop.mapreduce.Partitioner;public class CustomPartitioner extends Partitioner {@Overridepublic int getPartition(Text key, IntWritable value, int numReduceTasks) {String word = key.toString();if (word.startsWith("a")) {return 0;} else {return 1;}}}

Common Questions and Answers

1. What is the main role of YARN in Hadoop?

   YARN is responsible for managing resources in a Hadoop cluster and scheduling applications. It allows for better resource utilization and scalability.

2. How does MapReduce work?

   MapReduce works by dividing a task into smaller sub-tasks (Map) and then combining the results (Reduce). It processes data in parallel across a distributed cluster.

3. Can YARN run without MapReduce?

   Yes, YARN can run other processing models besides MapReduce, such as Apache Spark and Apache Tez.

4. What are common errors when running MapReduce jobs?

   Common errors include incorrect input/output paths, class not found exceptions, and memory allocation issues.

Troubleshooting Common Issues

If you encounter a ClassNotFoundException, ensure that your JAR file contains all necessary classes and dependencies.

If your MapReduce job is running out of memory, consider increasing the memory allocation for your Mapper and Reducer tasks in the configuration file.

Practice Exercises

• Modify the Word Count example to ignore case sensitivity.
• Create a MapReduce job that counts the number of lines in a text file.
• Experiment with different partitioning strategies in the Custom Partitioner example.

Remember, practice makes perfect! Keep experimenting and don’t hesitate to revisit this guide whenever you need a refresher. Happy coding! 😊