How to perform a word count on text data in HDFS¶
Overview¶
This example counts the number of words in text files that are stored in HDFS.
Who is this for?¶
This how-to 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.
Spark Wordcount Summary¶
Before you start¶
To execute this example, download the
spark-wordcount.py example script
and the download-data.py script
.
For this example, you’ll 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 will also need valid Amazon Web Services (AWS) credentials.
Load HDFS data¶
First, we will load the sample text data into HDFS. The following script will transfer 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 will be loaded into HDFS and ready for analysis.
Running the Job¶
Download the
spark-wordcount.py example script
to your cluster. This script will read the text files downloaded in the previous
step and count all of the words.
# 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)
In the above script, replace HEAD_NODE_IP
with the IP address of the head
node.
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, consult the Help and Support page.