Continuum Analytics: Documentation

# How to do Natural Language Processing

## Overview

This example provides a simple PySpark job that utilizes the NLTK library. NLTK is a popular Python package for natural language processing. This example will demonstrate the installation of Python libraries on the cluster, the usage of Spark with the YARN resource manager and execution of the Spark job.

## Who is this for?

This how-to is for users of a Spark cluster who wish to run Python code using the YARN resource manager. This how-to will show you how to integrate thrid-party Python libraries with Spark.

## Spark NLTK summary

1. Before you start
2. Install NLTK
3. Running the Job
4. Troubleshooting
5. Further information

## Before you start

To execute this example, download the: `spark-nltk.py example script` or `spark-nltk.ipynb example notebook`.

For this example you’ll need Spark running with the YARN resource manager. You can install Spark and YARN using an enterprise Hadoop distribution such as Cloudera CDH or Hortonworks HDP.

## Install NLTK

If you have permission to install packages with `acluster` you can install NLTK as a conda package.

    acluster conda install nltk

You should see output similar to this from each node, which indicates that the package was successfully installed across the cluster:

    Node "ip-10-140-235-89.ec2.internal":
        Successful actions: 1/1
    Node "ip-10-154-10-144.ec2.internal":
        Successful actions: 1/1
    Node "ip-10-31-96-152.ec2.internal":
        Successful actions: 1/1

In order to use the full NLTK library, you will need to download the data for the NLTK project. You can download the data on all cluster nodes by using the `acluster cmd` command.

    acluster cmd 'sudo /opt/anaconda/bin/python -m nltk.downloader -d /usr/share/nltk_data all'

After a few minutes you should see output similar to this.

    Execute command "sudo /opt/anaconda/bin/python -m nltk.downloader -d /usr/share/nltk_data all" target: "*" cluster: "d"
        All nodes (x3) response: [nltk_data] Downloading collection 'all'
    [nltk_data]    |
    [nltk_data]    | Downloading package abc to /usr/share/nltk_data...
    [nltk_data]    |   Unzipping corpora/abc.zip.
    [nltk_data]    | Downloading package alpino to /usr/share/nltk_data...
    [nltk_data]    |   Unzipping corpora/alpino.zip.
    [nltk_data]    | Downloading package biocreative_ppi to
    [nltk_data]    |     /usr/share/nltk_data...

    ....

    [nltk_data]    |   Unzipping models/bllip_wsj_no_aux.zip.
    [nltk_data]    | Downloading package word2vec_sample to
    [nltk_data]    |     /usr/share/nltk_data...
    [nltk_data]    |   Unzipping models/word2vec_sample.zip.
    [nltk_data]    |
    [nltk_data]  Done downloading collection all

## Running the Job

Here is the complete script to run the Spark + NLTK example in PySpark.

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

    conf = SparkConf()
    conf.setMaster('yarn-client')
    conf.setAppName('spark-nltk')
    sc = SparkContext(conf=conf)

    data = sc.textFile('file:///usr/share/nltk_data/corpora/state_union/1972-Nixon.txt')

    def word_tokenize(x):
        import nltk
        return nltk.word_tokenize(x)

    def pos_tag(x):
        import nltk
        return nltk.pos_tag([x])

    words = data.flatMap(word_tokenize)
    print words.take(10)

    pos_word = words.map(pos_tag)
    print pos_word.take(5)

Let’s walk through the above code example. First, we will create a SparkContext. Note that Anaconda for cluster management will not create a SparkContext by default. In this example, we use the YARN resource manager.

    from pyspark import SparkConf
    from pyspark import SparkContext

    conf = SparkConf()
    conf.setMaster('yarn-client')
    conf.setAppName('spark-nltk')
    sc = SparkContext(conf=conf)

After a SparkContext is created, we can load some data into Spark. In this case, the data file is from one of the example documents provided by NLTK.

    data = sc.textFile('file:///usr/share/nltk_data/corpora/state_union/1972-Nixon.txt')

Next, we write a function that imports `nltk` and calls `nltk.word_tokenize`. The function is mapped to the text file that was read in the previous step.

    def word_tokenize(x):
        import nltk
        return nltk.word_tokenize(x)

    words = data.flatMap(word_tokenize)

We can confirm that the `flatMap` operation worked by returning some of the words in the dataset.

    print words.take(10)

Finally, NTLK’s POS-tagger can be used to find the part of speech for each word.

    def pos_tag(x):
        import nltk
        return nltk.pos_tag([x])

    pos_word = words.map(pos_tag)
    print pos_word.take(5)

Run the script on the Spark cluster using the spark-submit script. The output shows the words that were returned from the Spark script, including the results from the `flatMap` operation and the `POS-tagger`.

    Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
    15/06/13 05:14:29 INFO SparkContext: Running Spark version 1.4.0

    [...]

    ['Address',
     'on',
     'the',
     'State',
     'of',
     'the',
     'Union',
     'Delivered',
     'Before',
     'a']

    [...]

    [[('Address', 'NN')],
     [('on', 'IN')],
     [('the', 'DT')],
     [('State', 'NNP')],
     [('of', 'IN')]]

## Troubleshooting

If something goes wrong consult the FAQ / Known issues page.

## Further information

See the Spark and PySpark documentation pages for more information.

For more information on NLTK see the NLTK book <http://www.nltk.org/book/>.