Introduction to Hadoop

Hadoop is an open-source framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

Key Components of Hadoop

Hadoop Distributed File System (HDFS):
- Purpose: HDFS is designed to store very large data sets reliably and to stream those data sets at high bandwidth to user applications.
- Architecture: It follows a master/slave architecture with a single NameNode (master) and multiple DataNodes (slaves).
MapReduce:
- Purpose: MapReduce is a programming model for processing large data sets with a parallel, distributed algorithm on a cluster.
- Components: It consists of two main functions: Map and Reduce.
YARN (Yet Another Resource Negotiator):
- Purpose: YARN is the resource management layer of Hadoop. It allows multiple data processing engines such as interactive SQL, real-time streaming, and batch processing to handle data stored in a single platform.
- Components: ResourceManager and NodeManager.
Hadoop Common:
- Purpose: These are the common utilities that support the other Hadoop modules.

HDFS Architecture

Component	Description
NameNode	Manages the file system namespace and regulates access to files by clients.
DataNode	Stores actual data. DataNodes perform read-write operations as requested.
Secondary NameNode	Performs housekeeping functions for the NameNode.

Example: Basic HDFS Commands

# List files in the HDFS directory
hdfs dfs -ls /

# Create a directory in HDFS
hdfs dfs -mkdir /user/hadoop

# Copy a file from local filesystem to HDFS
hdfs dfs -put localfile.txt /user/hadoop

# Read a file from HDFS
hdfs dfs -cat /user/hadoop/localfile.txt

# Delete a file in HDFS
hdfs dfs -rm /user/hadoop/localfile.txt

MapReduce Programming Model

Map Function

The Map function takes a set of data and converts it into another set of data, where individual elements are broken down into key-value pairs.

public class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{
    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);
        }
    }
}

Reduce Function

The Reduce function takes the output from a Map function and combines those data tuples into a smaller set of tuples.

public class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    private IntWritable result = new IntWritable();

    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int sum = 0;
        for (IntWritable val : values) {
            sum += val.get();
        }
        result.set(sum);
        context.write(key, result);
    }
}

Practical Exercise

Objective: Implement a simple word count program using Hadoop MapReduce.

Setup Hadoop Environment:
- Install Hadoop on your local machine or use a cloud-based Hadoop service.
Write the Mapper and Reducer Classes:
- Use the provided code snippets for the TokenizerMapper and IntSumReducer classes.
Create a Driver Class:
- This class will set up the job configuration and start the MapReduce job.

public class WordCount {
    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);
    }
}

Compile and Run the Program:
- Compile the Java code and create a JAR file.
- Run the Hadoop job using the JAR file.

# Compile the Java code
javac -classpath `hadoop classpath` -d wordcount_classes WordCount.java

# Create a JAR file
jar -cvf wordcount.jar -C wordcount_classes/ .

# Run the Hadoop job
hadoop jar wordcount.jar WordCount input_dir output_dir

Common Mistakes and Tips

Incorrect Path: Ensure that the input and output paths are correctly specified.
ClassNotFoundException: Make sure all classes are included in the JAR file.
Memory Issues: Allocate sufficient memory to the Hadoop job if processing large data sets.

Conclusion

In this section, we covered the basics of Hadoop, including its key components and architecture. We also walked through a practical example of a word count program using Hadoop MapReduce. Understanding these fundamentals will prepare you for more advanced topics in massive data processing.

Next, we will explore another powerful tool for big data processing: Apache Kafka.

Hadoop

Introduction to Hadoop

Key Components of Hadoop

HDFS Architecture

Example: Basic HDFS Commands

MapReduce Programming Model

Map Function

Reduce Function

Practical Exercise

Common Mistakes and Tips

Conclusion

Massive Data Processing

Module 1: Introduction to Massive Data Processing

Module 2: Storage Technologies

Module 3: Processing Techniques

Module 4: Tools and Platforms

Module 5: Storage and Processing Optimization

Module 6: Massive Data Analysis

Module 7: Case Studies and Practical Applications

Module 8: Best Practices and Future of Massive Data Processing