MapReduce Mapper Assignment

Motivation

In previous semesters the MapReduce lab has proven to be the most challenging. We have split things up to allow you to get familiar with the how Mappers and Reducers work first. We will build a card mapper that matches the spec outlined in the prep video, a simple word counting mapper, an analogous k-mer counting mapper, and an integer sum reducer to wrap things up.

The k-mer counting mapper will prepare us for (and hopefully lessen the burden of) upcoming lab 6.

Code To Use

BiConsumer

accept(t,u) (note: closest relative to "emit" from RiceX prep)

Collector

finisher() (note: closest relative to "reduce" from RiceX prep)

Deck (note: Iterable<Card>)

Card

getRank()

getSuit()

Rank

isNumeric()

getNumericValue()

Suit

TextSection

getWords()

Code To Implement

Card Mapper

The specification for this mapper is outlined in the prep video:

Non-numeric cards are considered to be bad data and ignored. Numeric cards should be emitted with their suit as the key and the numeric value as the value.

class:	CardMapper.java
methods:	map
package:	mapreduce.apps.intsum.cards.studio
source folder:	student/src/main/java

method: public void map(Deck deck, BiConsumer<Suit, Integer> keyValuePairConsumer) (sequential implementation only)

Word Count Mapper

Counting occurrences of words in text is a classic example of mapreduce. We will ignore any zero length words and convert the remaining words to lower-case so as to get a case insensitive count. Emitting each lower-cased word as the key with the value of 1 should do the trick here.

class:	WordCountMapper.java
methods:	map
package:	mapreduce.apps.intsum.wordcount.studio
source folder:	student/src/main/java

method: public void map(TextSection textSection, BiConsumer<String, Integer> keyValuePairConsumer) (sequential implementation only)

Warning:The javadocs have an error. They should read " the values of all of the key-value pairs will be one."

K-mer Count Mapper

K-mer counting is a useful technique in bioinformatics: http://www.csbio.unc.edu/mcmillan/Comp555S17/Lecture02.pdf

Background information on k-mer counting can be found here: https://en.wikipedia.org/wiki/K-mer

This mapper is similar to the word count mapper except that the k-mers overlap with each other while words are separate.

class:	KMerMapper.java
methods:	map
package:	mapreduce.apps.intsum.kmer.studio
source folder:	student/src/main/java

method: public void map(byte[] sequence, BiConsumer<String, Integer> keyValuePairConsumer) (sequential implementation only)

Be sure to use the provided toStringKMer(sequence, offset, kMerLength) method to generate your k-mers:

	private static String toStringKMer(byte[] sequence, int offset, int kMerLength) {
		return new String(sequence, offset, kMerLength, StandardCharsets.UTF_8);
	}

Int Sum Reducer

class:	IntegerSumClassicReducer.java
methods:	finisher
package:	mapreduce.apps.intsum.studio
source folder:	student/src/main/java

method: public Function<List<Integer>, Integer> finisher() (sequential implementation only)

The sea of code created by the anonymous inner class can admittedly be a bit intimidating at first:

	@Override
	public Function<List<Integer>, Integer> finisher() {
		return new Function<List<Integer>, Integer>() {
			@Override
			public Integer apply(List<Integer> list) {
				throw new NotYetImplementedException();
			}
		};
	}

but in the end, the apply method is passed a list of integers which it should simply sum up and return. Feel free to replace the bulky anonymous inner class with a lambda if you would like.

Testing Your Solution

Correctness

class:	IntSumStudioTestSuite.java
package:	mapreduce
source folder:	testing/src/test/java