Latest revision as of 18:49, 20 February 2024

Motivation

In previous semesters the MapReduce exercise has proven to be the most challenging. We will start by building some Mappers on our way to the final boss.

Each of the Mappers built today can be pairs with an Int Summing AccumulatorCombinerReducer:

a card mapper that matches the spec outlined in the prep video,
a simple word counting mapper, and
an analogous k-mer counting mapper.

Note: the k-mer counting mapper will prepare us for (and hopefully lessen the burden of) an exercise later in the semester.

Code To Use

Previous Exercise

DefaultEntry<K,V>

Provided

CardMapper Utilities

Deck implements Iterable<Card>

rank.numericValue() note: returns Optional<Integer>

Suit

WordCount Mapper Utilities

TextSection

textSection.words()

K-mer Mapper Utilities

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

Code To Invesitigate

Note: each of the clients print entries. The entries produced by the map methods of the Mappers are instances of DefaultEntry. The entries produced by the StreamFramework are instances of a different implementation of Entry. Their toString() methods might be slightly different, but rest assured they are all Entries.

Card Mapping Clients

CardMapperClient

class:	CardMapperClient.java	CLIENT
package:	mapreduce.apps.cards.client
source folder:	student/src/main/java

CardMapperClient
Deck deck = Deck.createFull(); CardMapper mapper = new CardMapper(); List<Map.Entry<Suit, Integer>> keyValuePairs = mapper.map(deck); keyValuePairs.forEach(kv -> { System.out.println(kv); });

CardMapperClient Output

SPADES=10
SPADES=9
SPADES=8
SPADES=7
SPADES=6
SPADES=5
SPADES=4
SPADES=3
SPADES=2
HEARTS=10
HEARTS=9
HEARTS=8
HEARTS=7
HEARTS=6
HEARTS=5
HEARTS=4
HEARTS=3
HEARTS=2
DIAMONDS=10
DIAMONDS=9
DIAMONDS=8
DIAMONDS=7
DIAMONDS=6
DIAMONDS=5
DIAMONDS=4
DIAMONDS=3
DIAMONDS=2
CLUBS=10
CLUBS=9
CLUBS=8
CLUBS=7
CLUBS=6
CLUBS=5
CLUBS=4
CLUBS=3
CLUBS=2

CardMapReduceClient

class:	CardMapReduceClient.java	CLIENT
package:	mapreduce.apps.cards.client
source folder:	student/src/main/java

CardMapReduceClient

Deck[] decks = {
	Deck.createFull(),
	Deck.createFull(),
	Deck.createFull(),
	Deck.createFull(),
};
CardMapper mapper = new CardMapper();
AccumulatorCombinerReducer<Integer, ?, Integer> accumulatorCombinerReducer = StreamUtils.summingIntAccumulatorCombinerReducer();
MapReduceFramework<Deck, Suit, Integer, ?, Integer> framework = new StreamMapReduceFramework<>(mapper, accumulatorCombinerReducer);
Map<Suit, Integer> map = framework.mapReduceAll(decks);
map.entrySet().forEach(entry -> {
	System.out.println(entry);
});

CardMapReduceClient Output
HEARTS=216 SPADES=216 DIAMONDS=216 CLUBS=216

Word Count Mapping Clients

The word count mapping example clients use the beginning of If--- by Rudyard Kipling.

WordCountMapperClient

class:	WordCountMapperClient.java	CLIENT
package:	mapreduce.apps.wordcount.client
source folder:	student/src/main/java

Passing:

new TextSection("If you can keep your head when all about you")

to the WordCountMapper's map method will return a list of Entries containing:

WordCountMapperClient

TextSection textSection = new TextSection("If you can keep your head when all about you");
WordCountMapper mapper = new WordCountMapper();
List<Map.Entry<String, Integer>> keyValuePairs = mapper.map(textSection);
keyValuePairs.forEach(kv -> {
    System.out.println(kv);
});

WordCountMapperClient Output
if=1 you=1 can=1 keep=1 your=1 head=1 when=1 all=1 about=1 you=1

WordCountMapReduceClient

class:	WordCountMapReduceClient.java	CLIENT
package:	mapreduce.apps.wordcount.client
source folder:	student/src/main/java

WordCountMapReduceClient

TextSection[] textSections = {
        new TextSection("If you can keep your head when all about you"),
        new TextSection("   Are losing theirs and blaming it on you,"),
};
WordCountMapper mapper = new WordCountMapper();
AccumulatorCombinerReducer<Integer, ?, Integer> accumulatorCombinerReducer = StreamUtils.summingIntAccumulatorCombinerReducer();
MapReduceFramework<TextSection, String, Integer, ?, Integer> framework = new StreamMapReduceFramework<>(mapper, accumulatorCombinerReducer);
Map<String, Integer> map = framework.mapReduceAll(textSections);
map.entrySet().forEach(entry -> {
    System.out.println(entry);
});

WordCountMapReduceClient Output
all=1 theirs=1 about=1 it=1 your=1 when=1 losing=1 head=1 can=1 blaming=1 are=1 and=1 keep=1 if=1 you=3 on=1

K-mer Mapping Clients

The word count mapping example clients use the beginning of If--- by Rudyard Kipling.

KMerMapperClient

class:	KMerMapperClient.java	CLIENT
package:	mapreduce.apps.wordcount.client
source folder:	student/src/main/java

KMerMapperClient

byte[] sequence = "ACTCATGAG".getBytes(StandardCharsets.UTF_8);
KMerMapper mapper = new KMerMapper(3);
List<Map.Entry<String, Integer>> keyValuePairs = mapper.map(sequence);
keyValuePairs.forEach(kv -> {
    System.out.println(kv);
});

KMerMapperClient Output
ACT=1 CTC=1 TCA=1 CAT=1 ATG=1 TGA=1 GAG=1

KMerMapReduceClient

class:	KMerMapReduceClient.java	CLIENT
package:	mapreduce.apps.wordcount.client
source folder:	student/src/main/java

KMerMapReduceClient

byte[][] sequences = {
        "ACTCATGAG".getBytes(StandardCharsets.UTF_8),
        "CATGAAAAAA".getBytes(StandardCharsets.UTF_8),
};
KMerMapper mapper = new KMerMapper(3);
AccumulatorCombinerReducer<Integer, ?, Integer> accumulatorCombinerReducer = StreamUtils.summingIntAccumulatorCombinerReducer();
MapReduceFramework<byte[], String, Integer, ?, Integer> framework = new StreamMapReduceFramework<>(mapper, accumulatorCombinerReducer);
Map<String, Integer> map = framework.mapReduceAll(sequences);
map.entrySet().forEach(entry -> {
    System.out.println(entry);
});

KMerMapReduceClient Output
AAA=4 ACT=1 TCA=1 CTC=1 ATG=2 GAA=1 CAT=2 GAG=1 TGA=2

Code To Implement

CardMapper

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

Video: MapReduce Tutorial

Video: Learning MapReduce with Playing Cards

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. Emitted key-value pairs are returned in a list of Entries.

class:	CardMapper.java
methods:	map
package:	mapreduce.app.cards.exercise
source folder:	student/src/main/java

method: List<Map.Entry<Suit, Integer>> map(Deck deck) (sequential implementation only)

Make sure you have implemented DefaultEntry<K,V> before starting this assignment.

Word Count Mapper

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

method: List<Map.Entry<String, Integer>> map(TextSection textSection) (sequential implementation only)

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.

Hint: Look at the methods in TextSection and the toLowerCase() method for strings for assistance.

K-mer Count Mapper

K-mer counting is a useful technique in bioinformatics.

Further background information on k-mer counting can be found here.

The 3-mers in the chromosome data:

ACTCATGAG

are:

ACT
 CTC
  TCA
   CAT
    ATG
     TGA
      GAG

The 4-mers in that same chromosome data:

ACTCATGAG

are:

ACTC
 CTCA
  TCAT
   CATG
    ATGA
     TGAG

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

method: List<Map.Entry<String, Integer>> map(byte[] sequence) (sequential implementation only)

Take note of the private instance variable declared and initialized for you. 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);
}

This mapper is similar to the #Word Count Mapper except that the k-mers overlap with each other while words are separate.

As the emitted values for each key will be later summed up in the reduction phase, what value makes sense to emit with each key?

Testing Your Solution

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

Pledge, Acknowledgments, Citations

file:

map-reduce-mapper-pledge-acknowledgments-citations.txt

More info about the Honor Pledge

@@ Line 27: / Line 27: @@
 ===WordCount Mapper Utilities===
-[https://www.cse.wustl.edu/~cosgroved/courses/cse231/current/apidocs/mapreduce/apps/wordcount/core/TextSection.html TextSection]
+[https://www.cse.wustl.edu/~dennis.cosgrove/courses/cse231/current/apidocs/edu/wustl/cse231s/text/core/TextSection.html TextSection]
-: [https://www.cse.wustl.edu/~cosgroved/courses/cse231/current/apidocs/mapreduce/apps/wordcount/core/TextSection.html#words-- textSection.words()]
+: [https://www.cse.wustl.edu/~dennis.cosgrove/courses/cse231/current/apidocs/edu/wustl/cse231s/text/core/TextSection.html#words-- textSection.words()]
 ===K-mer Mapper Utilities===
@@ Line 54: / Line 54: @@
 </syntaxhighlight>}}
-{{CollapsibleConsole|CardMapperClient Output|<pre style="border: 0px; background: #000; color:#fff;">DefaultEntry[SPADES=>10]
+{{CollapsibleConsole|CardMapperClient Output|<pre style="border: 0px; background: #000; color:#fff;">SPADES=10
-DefaultEntry[SPADES=>9]
+SPADES=9
-DefaultEntry[SPADES=>8]
+SPADES=8
-DefaultEntry[SPADES=>7]
+SPADES=7
-DefaultEntry[SPADES=>6]
+SPADES=6
-DefaultEntry[SPADES=>5]
+SPADES=5
-DefaultEntry[SPADES=>4]
+SPADES=4
-DefaultEntry[SPADES=>3]
+SPADES=3
-DefaultEntry[SPADES=>2]
+SPADES=2
-DefaultEntry[HEARTS=>10]
+HEARTS=10
-DefaultEntry[HEARTS=>9]
+HEARTS=9
-DefaultEntry[HEARTS=>8]
+HEARTS=8
-DefaultEntry[HEARTS=>7]
+HEARTS=7
-DefaultEntry[HEARTS=>6]
+HEARTS=6
-DefaultEntry[HEARTS=>5]
+HEARTS=5
-DefaultEntry[HEARTS=>4]
+HEARTS=4
-DefaultEntry[HEARTS=>3]
+HEARTS=3
-DefaultEntry[HEARTS=>2]
+HEARTS=2
-DefaultEntry[DIAMONDS=>10]
+DIAMONDS=10
-DefaultEntry[DIAMONDS=>9]
+DIAMONDS=9
-DefaultEntry[DIAMONDS=>8]
+DIAMONDS=8
-DefaultEntry[DIAMONDS=>7]
+DIAMONDS=7
-DefaultEntry[DIAMONDS=>6]
+DIAMONDS=6
-DefaultEntry[DIAMONDS=>5]
+DIAMONDS=5
-DefaultEntry[DIAMONDS=>4]
+DIAMONDS=4
-DefaultEntry[DIAMONDS=>3]
+DIAMONDS=3
-DefaultEntry[DIAMONDS=>2]
+DIAMONDS=2
-DefaultEntry[CLUBS=>10]
+CLUBS=10
-DefaultEntry[CLUBS=>9]
+CLUBS=9
-DefaultEntry[CLUBS=>8]
+CLUBS=8
-DefaultEntry[CLUBS=>7]
+CLUBS=7
-DefaultEntry[CLUBS=>6]
+CLUBS=6
-DefaultEntry[CLUBS=>5]
+CLUBS=5
-DefaultEntry[CLUBS=>4]
+CLUBS=4
-DefaultEntry[CLUBS=>3]
+CLUBS=3
-DefaultEntry[CLUBS=>2]</pre>}}
+CLUBS=2</pre>}}
 ===CardMapReduceClient===
 {{Client|CardMapReduceClient|mapreduce.apps.cards.client|main}}
@@ Line 119: / Line 120: @@
 ===WordCountMapperClient===
 {{Client|WordCountMapperClient|mapreduce.apps.wordcount.client|main}}
+Passing:
+ new TextSection("If you can keep your head when all about you")
+to the WordCountMapper's map method will return a list of Entries containing:
+[[File:WordCount MapResult.svg|1200px]]
 {{CollapsibleCode|WordCountMapperClient|
@@ Line 130: / Line 139: @@
 </syntaxhighlight>}}
-{{CollapsibleConsole|WordCountMapperClient Output|<pre style="border: 0px; background: #000; color:#fff;">DefaultEntry[DefaultEntry[if=>1]
+{{CollapsibleConsole|WordCountMapperClient Output|<pre style="border: 0px; background: #000; color:#fff;">if=1
-DefaultEntry[you=>1]
+you=1
-DefaultEntry[can=>1]
+can=1
-DefaultEntry[keep=>1]
+keep=1
-DefaultEntry[your=>1]
+your=1
-DefaultEntry[head=>1]
+head=1
-DefaultEntry[when=>1]
+when=1
-DefaultEntry[all=>1]
+all=1
-DefaultEntry[about=>1]
+about=1
-DefaultEntry[you=>1]</pre>}}
+you=1</pre>}}
 ===WordCountMapReduceClient===
 {{Client|WordCountMapReduceClient|mapreduce.apps.wordcount.client|main}}
@@ Line 190: / Line 200: @@
 </syntaxhighlight>}}
-{{CollapsibleConsole|KMerMapperClient Output|<pre style="border: 0px; background: #000; color:#fff;">DefaultEntry[ACT=>1]
+{{CollapsibleConsole|KMerMapperClient Output|<pre style="border: 0px; background: #000; color:#fff;">ACT=1
-DefaultEntry[CTC=>1]
+CTC=1
-DefaultEntry[TCA=>1]
+TCA=1
-DefaultEntry[CAT=>1]
+CAT=1
-DefaultEntry[ATG=>1]
+ATG=1
-DefaultEntry[TGA=>1]
+TGA=1
-DefaultEntry[GAG=>1]</pre>}}
+GAG=1</pre>}}
 ===KMerMapReduceClient===
 {{Client|KMerMapReduceClient|mapreduce.apps.wordcount.client|main}}
@@ Line 228: / Line 239: @@
 ==CardMapper==
 The specification for this mapper is outlined in the prep video:
+{{CollapsibleYouTube|MapReduce Tutorial|<youtube>K8VPHHPS3BQ</youtube>}}
 {{CollapsibleYouTube|Learning MapReduce with Playing Cards|<youtube>bcjSe0xCHbE</youtube>}}
@@ Line 237: / Line 248: @@
 {{Sequential|List<Map.Entry<Suit, Integer>> map(Deck deck)}}
+Make sure you have implemented [[Not_Thread_Safe_Hash_Table_Assignment#DefaultEntry.3CK.2CV.3E|DefaultEntry<K,V>]] before starting this assignment.
 ==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.
 {{CodeToImplement|WordCountMapper|map|mapreduce.apps.wordcount.exercise}}
@@ Line 245: / Line 257: @@
 {{Sequential|List<Map.Entry<String, Integer>> map(TextSection textSection)}}
-The goal of this implementation is to count the number of times a word appears in a given text, using MapReduce. To accomplish this, you will need to create both the mapper and the reducer. Navigate to the <code>WordCountMapper.java</code> and <code>IntSumListAccumulatingReducer.java</code> classes. You will specifically define how the framework accomplishes the map and reduce methods.
+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.
+<!-- COMMENTED OUT count the number of times a word appears in a given text, using MapReduce. To accomplish this, you will need to create both the mapper and the reducer. Navigate to the <code>WordCountMapper.java</code> and <code>IntSumListAccumulatingReducer.java</code> classes. You will specifically define how the framework accomplishes the map and reduce methods.
-The only method you will need to alter is the map method. In this method, you need to record every instance of a given word and feed it into the keyValuePairConsumer. To do this, access all of the words in the TextSection and if the length of the word is greater than zero (meaning it is not just blank space), convert it into lower-case and accept it into the consumer.
+The only method you will need to alter is the map method.
+In this method, you need to record every instance of a given word and feed it into the keyValuePairConsumer. To do this, access all of the words in the TextSection and if the length of the word is greater than zero (meaning it is not just blank space), convert it into lower-case and accept it into the consumer. COMMENTED OUT-->
 Hint: Look at the methods in TextSection and the toLowerCase() method for strings for assistance.
@@ Line 288: / Line 303: @@
 {{Sequential|List<Map.Entry<String, Integer>> map(byte[] sequence)}}
+Take note of the private instance variable declared and initialized for you.
 Be sure to use the provided toStringKMer(sequence, offset, kMerLength) method to generate your k-mers:

Difference between revisions of "MapReduce Mapper Assignment"

Latest revision as of 18:49, 20 February 2024

Contents

Motivation

Code To Use

Previous Exercise

Provided

CardMapper Utilities

WordCount Mapper Utilities

K-mer Mapper Utilities

Code To Invesitigate

Card Mapping Clients

CardMapperClient

CardMapReduceClient

Word Count Mapping Clients

WordCountMapperClient

WordCountMapReduceClient

K-mer Mapping Clients

KMerMapperClient

KMerMapReduceClient

Code To Implement

CardMapper

Word Count Mapper

K-mer Count Mapper

Testing Your Solution

Pledge, Acknowledgments, Citations

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