Writing a MapReduce program from scratch

Writing a MapReduce program from scratch (using Hadoop) involves understanding a few key concepts, classes, and structure. It’s not like regular Java development—you’re building distributed data processing logic that runs on multiple machines.

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✅ What You Need to Know

To write a simple Hadoop MapReduce program from scratch, you should understand these essentials:

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1. MapReduce Programming Model (Concept)

• Mapper: Processes input data line by line and outputs key-value pairs.
• Reducer: Aggregates all values by key and outputs final key-value pairs.
• Combiner (optional): A mini-reducer run on mapper output before it's sent across the network.
• Driver: Java class that sets up and runs the job.

Think of it like:

Input Text File → [Mapper → Shuffle & Sort → Reducer] → Output File

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2. Required Java Knowledge

You should know:

• Classes and objects
• Inheritance and method overriding
• File I/O
• Java generics (for Hadoop types like Text, IntWritable)
• Basic understanding of how to compile and run Java with dependencies

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3. Key Hadoop Classes

Role	Class Name
Mapper	org.apache.hadoop.mapreduce.Mapper
Reducer	org.apache.hadoop.mapreduce.Reducer
Writable	IntWritable, Text, etc.
Job Control	org.apache.hadoop.mapreduce.Job

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🔧 4. Minimal Setup Example: Word Count

Here’s the simplest version of a MapReduce Word Count job:

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<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().replaceAll("[^a-zA-Z]", "").toLowerCase());
                context.write(word, one);
            }
        }
    }

    public static 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);
        }
    }

    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); // optional, improves performance
        job.setReducerClass(IntSumReducer.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        FileInputFormat.addInputPath(job, new Path(args[0]));   // input file path
        FileOutputFormat.setOutputPath(job, new Path(args[1])); // output directory

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

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🧱 5. Build & Run the Program

📦 Compile into a JAR

You’ll need to compile with Hadoop dependencies. If you're not using Maven, something like:

javac -classpath $(hadoop classpath) -d . WordCount.java
jar cf wordcount.jar WordCount*.class

▶️ Run with Hadoop

hadoop jar wordcount.jar WordCount /input/path /output/path

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🔍 Summary: Steps to Build a Simple Program

Step	What to Do
1	Write Mapper class
2	Write Reducer class
3	Write main() method to set up the job
4	Compile it with Hadoop dependencies
5	Package into a JAR file
6	Run it using hadoop jar on your cluster

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🧠 Next Level: If You Want to Learn More

• Learn about combiners, partitioners, and custom comparators
• Use multiple reducers
• Handle large datasets and failures
• Look into Apache Spark, which is easier and more efficient than raw MapReduce

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Let me know if you want a ready-to-run example you can test locally (with input/output files), or if you're using a cluster like AWS EMR or a local Hadoop setup.

Great question! The generic types in extends Mapper<...> and extends Reducer<...> are NOT fixed — they are fully customizable based on your data.

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📌 Quick Rule:

Mapper<INPUT_KEY, INPUT_VALUE, OUTPUT_KEY, OUTPUT_VALUE>
Reducer<INPUT_KEY, INPUT_VALUE, OUTPUT_KEY, OUTPUT_VALUE>

You define these types based on:

• What kind of data is being read
• What kind of key/value you want to emit

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🔍 Example: Word Count

Mapper<Object, Text, Text, IntWritable>

Why?

Type	Meaning
Object	Input key (offset of the line, not used)
Text	Input value (entire line of text)
Text	Output key (each word)
IntWritable	Output value (count = 1)

Reducer:

Reducer<Text, IntWritable, Text, IntWritable>

Type	Meaning
Text	Input key (a word)
IntWritable	Input values (list of 1s)
Text	Output key (word)
IntWritable	Output value (total count)

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🧠 So, How to Decide?

Ask yourself these questions:

1. What is your input format?

Default: Line-by-line from text file

• Then: Mapper<Object, Text, ..., ...>

2. What do you want your mapper to emit?

Example:

• Counting visits per IP → Mapper<Object, Text, Text (IP), IntWritable (1)>
• Sales sum → Mapper<Object, Text, Text (ProductID), FloatWritable (Price)>

3. What keys and values does your reducer expect to process?

• It receives all values grouped by key

• Output can be anything:

  • Text, IntWritable, FloatWritable, NullWritable, etc.

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🧪 Example 2: Summing Sales by Product

Assume CSV: productId,price

Mapper<Object, Text, Text, FloatWritable>
Reducer<Text, FloatWritable, Text, FloatWritable>

• Mapper emits: <productId, price>
• Reducer receives: <productId, list of prices> → sums → emits total sales

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✅ Writable Types You Can Use

Java Type	Hadoop Type
int	IntWritable
long	LongWritable
float	FloatWritable
double	DoubleWritable
String	Text
null	NullWritable

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💡 Final Tip

When unsure, print debug output inside map() and reduce() to test what keys and values are flowing.

Would you like a small practice challenge to try customizing this?

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— the method signatures for map() and reduce() must have exactly three parameters:

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🔹 map() Signature (always):

map(KEYIN key, VALUEIN value, Context context)

• KEYIN: Input key (e.g., line offset)
• VALUEIN: Input value (e.g., line of text)
• Context: Used to emit output (context.write(...))

🟡 You must not change the number of parameters. The Context is required for Hadoop to interact with the mapper's output.

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🔹 reduce() Signature (always):

reduce(KEYIN key, Iterable<VALUEIN> values, Context context)

• KEYIN: The grouped key (e.g., a word)
• Iterable<VALUEIN>: All values for that key (e.g., [1, 1, 1])
• Context: Used to emit reduced output

Again, fixed three parameters — this is what Hadoop internally expects.

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⛔️ You Cannot Do:

• map(Text a, Text b) ← ❌ won't compile or run
• reduce(String key, List<IntWritable> vals) ← ❌ invalid types + missing context

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✅ Correct Customization Is:

You choose the generic types in:

Mapper<KEYIN, VALUEIN, KEYOUT, VALUEOUT>
Reducer<KEYIN, VALUEIN, KEYOUT, VALUEOUT>

…but the map() and reduce() method must follow the three-parameter structure.

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Let me know if you want a template to follow for any custom logic (e.g., sales, weather data, etc.).

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Excellent — now we're digging into the heart of the Reducer logic, where Hadoop actually processes those "arrays" (really, Iterable<IntWritable>) that it grouped from the Mapper's output.

Let’s walk through it clearly.

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✅ Here's the Key Reducer Code Again:

public static class AvgWordLengthReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    private int totalLength = 0;
    private int totalCount = 0;

    public void reduce(Text key, Iterable<IntWritable> values, Context context)
            throws IOException, InterruptedException {
        int sum = 0;
        for (IntWritable val : values) {
            sum += val.get();  // <- This is where the addition happens
        }

        if (key.toString().equals("length")) {
            totalLength = sum;
        } else if (key.toString().equals("count")) {
            totalCount = sum;
        }
    }

    @Override
    protected void cleanup(Context context) throws IOException, InterruptedException {
        if (totalCount > 0) {
            int avg = totalLength / totalCount;
            context.write(new Text("Average Word Length"), new IntWritable(avg));
        }
    }
}

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✅ Now Let's Break It Down

🔹 1. This method is called once for each key (e.g., "length", "count"):

public void reduce(Text key, Iterable<IntWritable> values, Context context)

• Hadoop guarantees that all values with the same key are grouped and passed together.

• You can think of values as an array:

  • If key == "length" → values = [4, 5, 6, 7, ...]
  • If key == "count" → values = [1, 1, 1, 1, ...]

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🔹 2. This loop handles the actual addition:

int sum = 0;
for (IntWritable val : values) {
    sum += val.get();
}

• This sums up the elements of the "array"
• Once the sum is computed, we store it in a class variable

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🔹 3. This part checks which key we are processing:

if (key.toString().equals("length")) {
    totalLength = sum;
} else if (key.toString().equals("count")) {
    totalCount = sum;
}

• So if the Reducer is handling "length" key group, it sets totalLength
• If it's the "count" key group, it sets totalCount

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🔹 4. Then in cleanup() we calculate and output the result:

int avg = totalLength / totalCount;
context.write(new Text("Average Word Length"), new IntWritable(avg));

• This happens after all reduce() calls are complete
• It's used when you want to output something after aggregating multiple keys

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✅ Summary Table

Role	Code	Description
Handle grouped values	for (IntWritable val : values)	Iterate like an array
Sum values	sum += val.get();	Adds up word lengths or counts
Track totals by key type	if (key.equals(...))	Distinguish between "length" and "count"
Output after all reductions	cleanup() method	Outputs final average

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🧠 Visualization

Mapper output:

("length", 4)
("count", 1)
("length", 5)
("count", 1)
("length", 6)
("count", 1)

Reducer receives:

"length" → [4, 5, 6]   → sum = 15
"count"  → [1, 1, 1]   → sum = 3

Then:

avg = 15 / 3 = 5

Output:

("Average Word Length", 5)

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the Reducer's reduce() method runs once per key, not once per all keys combined.

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How it actually works:

• Hadoop groups the mapper outputs by key.
• For each unique key, Hadoop calls reduce(key, Iterable<values>, context) once.
• So, the for-loop inside reduce() processes only the values for that one key.

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So:

• First, Hadoop calls:

reduce("count", Iterable of all count values, context)

The for-loop sums all the counts (like 1 + 1 + 1 + ...), then you assign:

totalCount = sum;

• Then, Hadoop calls:

reduce("length", Iterable of all length values, context)

The for-loop sums all the lengths (like 4 + 5 + 6 + ...), then you assign:

totalLength = sum;

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To summarize:

Call #	Key	Values	What happens inside reduce()
1	"count"	[1, 1, 1, 1, ...]	Sum all counts and set totalCount
2	"length"	[4, 5, 6, 7, ...]	Sum all lengths and set totalLength

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So the for-loop only sums the values for one key at a time, not both keys in the same call.

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That's why:

if (key.toString().equals("length")) {
    totalLength = sum;
} else if (key.toString().equals("count")) {
    totalCount = sum;
}

happens after the summation for that key only.

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Then after both calls have finished (i.e., after all keys processed),

the cleanup() method runs — which uses the stored totalLength and totalCount to calculate the average and write the final output.

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So the lifecycle is like this:

1. reduce("count", [...]) → sums counts → sets totalCount
2. reduce("length", [...]) → sums lengths → sets totalLength
3. cleanup() → calculates and outputs average

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Absolutely! Here’s a generalized format/template you can use for any Hadoop MapReduce job driver class. Just plug in your specific Mapper, Reducer, and data types.

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General Hadoop MapReduce Job Driver Template

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class MyJobDriver {

    public static void main(String[] args) throws Exception {
        if (args.length != 2) {
            System.err.println("Usage: MyJobDriver <input path> <output path>");
            System.exit(-1);
        }

        // Create configuration and job
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "Job Name Here");

        // Set the main class
        job.setJarByClass(MyJobDriver.class);

        // Set Mapper and Reducer classes
        job.setMapperClass(MyMapper.class);
        job.setReducerClass(MyReducer.class);

        // (Optional) Set Combiner class if any
        // job.setCombinerClass(MyCombiner.class);

        // Set Mapper output key and value types
        job.setMapOutputKeyClass(MyMapperOutputKeyClass.class);
        job.setMapOutputValueClass(MyMapperOutputValueClass.class);

        // Set Reducer (final output) key and value types
        job.setOutputKeyClass(MyReducerOutputKeyClass.class);
        job.setOutputValueClass(MyReducerOutputValueClass.class);

        // Set input and output paths from args
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        // Submit job and wait for completion
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

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How to use this template?

• Replace MyMapper.class with your Mapper class name.
• Replace MyReducer.class with your Reducer class name.
• Replace MyMapperOutputKeyClass.class, MyMapperOutputValueClass.class with the key/value output types from the Mapper.
• Replace MyReducerOutputKeyClass.class, MyReducerOutputValueClass.class with the final output key/value types from the Reducer.
• Change "Job Name Here" to a descriptive job name.
• Pass the input and output directories as command-line arguments when running the job.

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Example with your AvgWordLength job:

job.setMapperClass(AvgWordLengthMapper.class);
job.setReducerClass(AvgWordLengthReducer.class);

job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);

job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);

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Here’s a list of the most commonly used Hadoop and HDFS commands, grouped by category, along with short descriptions.

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🗂️ Basic HDFS File System Commands

Command	Description
hdfs dfs -ls /path	List files/directories in HDFS path
hdfs dfs -mkdir /path	Create directory in HDFS
hdfs dfs -put localfile /hdfs/path	Upload local file to HDFS
hdfs dfs -copyFromLocal localfile /hdfs/path	Same as -put
hdfs dfs -get /hdfs/file localpath	Download HDFS file to local
hdfs dfs -copyToLocal /hdfs/file localpath	Same as -get
hdfs dfs -cat /hdfs/file	Display contents of HDFS file
hdfs dfs -tail /hdfs/file	Show last KB of a file
hdfs dfs -rm /hdfs/file	Delete HDFS file
hdfs dfs -rm -r /hdfs/dir	Recursively delete directory
hdfs dfs -du -h /path	Show space used by files (human readable)
hdfs dfs -df -h	Show HDFS disk usage info
hdfs dfs -stat /hdfs/file	Show file metadata (size, mod time, etc.)

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🔄 File Operations & Permissions

Command	Description
hdfs dfs -chmod 755 /file	Change file permissions
hdfs dfs -chown user:group /file	Change file owner and group
hdfs dfs -mv /src /dest	Move/rename file in HDFS
hdfs dfs -cp /src /dest	Copy file in HDFS

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🔍 Debugging and Admin

Command	Description
hdfs fsck /path	Check file system health
hdfs dfsadmin -report	Summary of data nodes and storage
hdfs dfsadmin -safemode get	Check if NameNode is in safe mode
hdfs dfsadmin -safemode leave	Exit safe mode

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🛠️ Other Useful Commands

Command	Description
hadoop version	Show Hadoop version
hdfs dfs -help	Get help for HDFS commands
yarn top	Monitor YARN container usage (if available)

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All the text above are responses generated by LLM for certain prompts in order to grasp Hadoop MapReduce