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mapreduce tutorial

introduction to mapreduce

mapreduce is a programming model and an associated implementation for processing and generating large datasets that can be parallelized across a distributed cluster of computers. it provides a high-level abstraction for processing data in a distributed manner and is widely used in big data processing frameworks like apache hadoop.

key concepts

1. **map function**: the map function takes an input pair and produces a set of intermediate key/value pairs. the framework collects all intermediate values associated with the same intermediate key and passes them to the reduce function.

2. **reduce function**: the reduce function takes intermediate keys and a list of values for that key and merges these values together. the output of the reduce function is the final output of the mapreduce job.

3. **input and output**: the input to a mapreduce job is typically a large dataset stored in a distributed file system (like hdfs), while the output is also stored in the distributed file system.

example system design

let's consider an example use case: **word count**. this is a classic example where we count the occurrences of each word in a large text file.

system design

1. **input**: a large text file containing multiple lines of text.
2. **map phase**:
read each line of the text file.
split the line into words.
emit each word as a key with a value of `1`.

3. **shuffle phase**: the framework groups all emitted key/value pairs by key (word).

4. **reduce phase**:
for each unique word, sum the values to get the total count of occurrences.

5. **output**: a text file with each word and its corresponding count.

code example

using hadoop's java api

let's write a simple mapreduce application for the word count example using hadoop's java api.

*1. maven dependencies*
```xml
dependency
groupidorg.apache.hadoop/groupid
artifactidhadoop-common/artifactid
version3.2.0/versio ...

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