Rosetta Code

A collection of MapReduce tasks translated (from Pig, Hive, MapReduce streaming, etc.) into Scalding. For fully runnable code, see the repository here.

Word Count

Cascalog

(defmapcatop tokenize [text]
  (seq (.split text "\\s+")))

(defn word-count [input-filename]
	(let [input (hfs-textline input-filename)]
    (<- [?word ?count]
        (input ?textline)
        (tokenize ?textline :> ?word)
        (c/count ?count))))  

(?- (stdout) (word-count "tweets.tsv"))

Hive

# tokenizer.py
import sys

for line in sys.stdin:
  for word in line.split():
    print word

CREATE TABLE tweets (text STRING);
LOAD DATA LOCAL INPATH 'tweets.tsv' OVERWRITE INTO TABLE tweets;

SELECT
	word, COUNT(*) AS count
FROM
	(
		SELECT TRANSFORM(text) 
			USING 'python tokenizer.py' 
			AS word
		FROM tweets
	) t
GROUP BY word;

Pig

tweets = LOAD 'tweets.tsv' AS (text:chararray);
words = FOREACH tweets GENERATE FLATTEN(TOKENIZE(text)) AS word;
word_groups = GROUP words BY word;
word_counts = FOREACH word_groups GENERATE group AS word, COUNT(words) AS count;

STORE word_counts INTO 'word_counts.tsv';

Scalding

Tsv("tweets.tsv", 'text)
  .flatMap('text -> 'word) { text : String => text.split("\\s+") }
  .groupBy('word) { _.size }
  .write(Tsv("word_counts.tsv"))

Distributed Grep

The map function emits a line if it matches a given pattern. The reduce function is an identity function that just copies the supplied intermediate data to the output. (Example taken from Google Code University.)

Pig

%declare PATTERN '.*hello.*';

tweets = LOAD 'tweets.tsv' AS (text:chararray);
results = FILTER tweets BY (text MATCHES '$PATTERN');

Scalding

val Pattern = ".*hello.*";

Tsv("tweets.tsv", 'text)
  .filter('text) { text : String => text.matches(Pattern) }

Inverted Index

The map function parses each document, and emits a sequence of <word, document ID> pairs. The reduce function accepts all pairs for a given word, sorts the corresponding document IDs and emits a <word, list(document ID)> pair. The set of all output pairs forms a simple inverted index. It is easy to augment this computation to keep track of word positions. (Example taken from Google Code University.)

Pig

tweets = LOAD 'tweets.tsv' AS (tweet_id:int, text:chararray);

words = FOREACH tweets GENERATE tweet_id, FLATTEN(TOKENIZE(text)) AS word;
word_groups = GROUP words BY word;
inverted_index = FOREACH word_groups GENERATE group AS word, words.tweet_id;

Scalding

val tweets = Tsv("tweets.tsv", ('id, 'text))

val wordToTweets =
  tweets
    .flatMap(('id, 'text) -> ('word, 'tweetId)) { 
      fields : (Long, String) => 
      val (tweetId, text) = fields
      text.split("\\s+").map { word => (word, tweetId) }
    }

val invertedIndex =
  wordToTweets.groupBy('word) {  _.toList[Long]('tweetId -> 'tweetIds) }