Performing a word count on text data in HDFS

This example counts the number of words in text files that are stored in HDFS.

Who is this for?

This example is for users of a Spark cluster who wish to run a PySpark job–with the YARN resource manager–that reads and processes text files stored in HDFS.

Before you start

Download the spark-wordcount.py example script and the download-data.py script.

You need Spark running with the YARN resource manager and the Hadoop Distributed File System (HDFS). You can install Spark, YARN and HDFS using an enterprise Hadoop distribution such as Cloudera CDH or Hortonworks HDP.

You also need valid Amazon Web Services (AWS) credentials.

Load HDFS data

Load the sample text data into HDFS. The following script transfers sample text data (approximately 6.4 GB) from a public Amazon S3 bucket to the HDFS data store on the cluster.

Download the download-data.py script to your cluster and insert your Amazon AWS credentials in the AWS_KEY and AWS_SECRET variables.

import subprocess

AWS_KEY = ''
AWS_SECRET = ''

s3_path = 's3n://{0}:{1}@blaze-data/enron-email'.format(AWS_KEY, AWS_SECRET)
cmd = ['hadoop', 'distcp', s3_path, 'hdfs:///tmp/enron']
subprocess.call(cmd)

NOTE: The hadoop distcp command might fail to run on smaller Amazon EC2 instance sizes due to memory limits.

Run the download-data.py script on the Spark cluster:

$ python download-data.py

After a few minutes, the text data is loaded into HDFS and ready for analysis.

Running the job

The following script reads the text files downloaded in the previous step and counts all of the words. Download the spark-wordcount.py example script to your cluster, and then replace HEAD_NODE_IP with the IP address of the head node.

# spark-wordcount.py
from pyspark import SparkConf
from pyspark import SparkContext

HDFS_MASTER = 'HEAD_NODE_IP'

conf = SparkConf()
conf.setMaster('yarn-client')
conf.setAppName('spark-wordcount')
conf.set('spark.executor.instances', 10)
sc = SparkContext(conf=conf)

distFile = sc.textFile('hdfs://{0}:9000/tmp/enron/*/*.txt'.format(HDFS_MASTER))

nonempty_lines = distFile.filter(lambda x: len(x) > 0)
print 'Nonempty lines', nonempty_lines.count()

words = nonempty_lines.flatMap(lambda x: x.split(' '))

wordcounts = words.map(lambda x: (x, 1)) \
                  .reduceByKey(lambda x, y: x+y) \
                  .map(lambda x: (x[1], x[0])).sortByKey(False)

print 'Top 100 words:'
print wordcounts.take(100)

Run the script on your Spark cluster using spark-submit. The output shows the 100 most frequently occurring words from the sample text data:

54.237.100.240: Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
15/06/13 04:58:42 INFO SparkContext: Running Spark version 1.4.0

[...]

15/06/26 04:32:03 INFO YarnScheduler: Removed TaskSet 7.0, whose tasks have all completed, from pool
15/06/26 04:32:03 INFO DAGScheduler: ResultStage 7 (runJob at PythonRDD.scala:366) finished in 0.210 s
15/06/26 04:32:03 INFO DAGScheduler: Job 3 finished: runJob at PythonRDD.scala:366, took 18.124243 s
[(288283320, ''), (22761900, '\t'), (19583689, 'the'), (13084511, '\t0'), (12330608, '-'),
(11882910, 'to'), (11715692, 'of'), (10822018, '0'), (10251855, 'and'), (6682827, 'in'),
(5463285, 'a'), (5226811, 'or'), (4353317, '/'), (3946632, 'for'), (3695870, 'is'),
(3497341, 'by'), (3481685, 'be'), (2714199, 'that'), (2650159, 'any'), (2444644, 'shall'),
(2414488, 'on'), (2325204, 'with'), (2308456, 'Gas'), (2268827, 'as'), (2265197, 'this'),
(2180110, '$'), (1996779, '\t$0'), (1903157, '12:00:00'), (1823570, 'The'), (1727698, 'not'),
(1626044, 'such'), (1578335, 'at'), (1570484, 'will'), (1509361, 'has'), (1506064, 'Enron'),
(1460737, 'Inc.'), (1453005, 'under'), (1411595, 'are'), (1408357, 'from'), (1334359, 'Data'),
(1315444, 'have'), (1310093, 'Energy'), (1289975, 'Set'), (1281998, 'Technologies,'),
(1280088, '***********'), (1238125, '\t-'), (1176380, 'all'), (1169961, 'other'), (1166151, 'its'),
(1132810, 'an'), (1127730, '&'), (1112331, '>'), (1111663, 'been'), (1098435, 'This'),
(1054291, '0\t0\t0\t0\t'), (1021797, 'States'), (971255, 'you'), (971180, 'which'), (961102, '.'),
(945348, 'I'), (941903, 'it'), (939439, 'provide'), (902312, 'North'), (867218, 'Subject:'),
(851401, 'Party'), (845111, 'America'), (840747, 'Agreement'), (810554, '#N/A\t'), (807259, 'may'),
(800753, 'please'), (798382, 'To'), (771784, '\t$-'), (753774, 'United'), (740472, 'if'),
(739731, '\t0.00'), (723399, 'Power'), (699294, 'To:'), (697798, 'From:'), (672727, 'Date:'),
(661399, 'produced'), (652527, '2001'), (651164, 'format'), (650637, 'Email'), (646922, '3.0'),
(645078, 'licensed'), (644200, 'License'), (642700, 'PST'), (641426, 'cite'), (640441, 'Creative'),
(640089, 'Commons'), (640066, 'NSF'), (639960, 'EML,'), (639949, 'Attribution'),
(639938, 'attribution,'), (639936, 'ZL'), (639936, '(http://www.zlti.com)."'), (639936, '"ZL'),
(639936, 'X-ZLID:'), (639936, '<http://creativecommons.org/licenses/by/3.0/us/>'), (639936, 'X-SDOC:')]

Troubleshooting

If something goes wrong, see Help and support.

Further information

See the Spark and PySpark documentation: