We can use Hadoop to count the number of words per sentence-tokenized document in a sizable data set such as the MASC corpus.
Download the “MASC Sentence Corpus” to
/common/clusterdata/<username> and prepare the data for “Hadooping” with:
cut -f 7 masc_sentences.tsv > masc_sentences.lst
hdfs dfs -put masc_sentences.lst /user/<username>
The “Mapper” Script
This first script will serve as the “map” portion of our Hadoop MapReduce job. It simply tokenizes each sentence provided as input–one sentence per line–and returns the number of tokens per sentence.
import sys import re from nltk.tokenize import TreebankWordTokenizer tokenizer = TreebankWordTokenizer() sentences = sys.stdin for (sentence_no, sentence) in enumerate(sentences): tokenized = [t for t in tokenizer.tokenize(sentence) if re.search(r'[a-zA-Z0-9]+', t)] print(sentence_no, len(tokenized))
The “Reducer” Script
This second script is the “reduce” portion of our Hadoop MapReduce job. It takes the output from the “mapper” script as input and returns the number of words across all sentences provided as input to the “mapper”.
import sys total = 0 for line in sys.stdin: (sentence_no, word_count) = line.strip().split(" ") total += int(word_count) print(total)
How to Run
If you have a copy of
masc_sentences.lst saved locally, you can test your mapper and reducer scripts with:
python mapper.py < masc_sentences.lst | python reducer.py
When you’re ready to utilize the cluster, you can do:
hadoop jar /usr/hdp/126.96.36.199-78/hadoop-mapreduce/hadoop-streaming.jar \ -files mapper.py,reducer.py \ -cmdenv PATH=$PATH \ -mapper "python3 mapper.py" \ -reducer "python3 reducer.py" \ -input /user/<username>/masc_sentences.lst \ -output /user/<username>/output
Understanding the Hadoop Command
hadoop is a program that submits our MapReduce jobs to our cluster via the YARN scheduler. The program
yarn can also be used, with all other arguments remaining the same.
Every Hadoop job is a pair of programs: a “mapper” program and a “reducer” program. Hadoop requires that they both read from
stdin, so while the mapper must write its output to
stdout so the reducer can read it, the reducer can write its final output anywhere (
stdout, local files, HDFS, databases, etc.).
- The order of the first three arguments after
hadoopare fixed; that is, this command must always take the form of
jar <path to jar> -files.
-outputlocation must not exist prior to running this command, otherwise the job will fail.