Distributed natural language processing — Anaconda Enterprise 4.4 documentation

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# Distributed natural language processing

This example provides a simple PySpark job that utilizes the NLTK library. NLTK is a popular Python package for natural language processing. This example shows you how to integrate third-party Python libraries with Spark. This example demonstrates the installation of Python libraries on the cluster, the usage of Spark with the YARN resource manager and execution of the PySpark job.

## 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.

## Before you start

Download the `spark-nltk.py example script` or `spark-nltk.ipynb example notebook`.

You 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

Install NLTK on all of the cluster nodes using the `adam scale` command:

    $ adam scale -n cluster conda install nltk

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

    All nodes (x4) response:
    {
      "actions": {
        "EXTRACT": [
          "conda-env-2.5.2-py27_0",
          "conda-4.1.11-py27_0"
        ],
        "FETCH": [
          "conda-env-2.5.2-py27_0",
          "conda-4.1.11-py27_0"
        ],
        "LINK": [
          "conda-env-2.5.2-py27_0 1 None",
          "conda-4.1.11-py27_0 1 None"
        ],
        "PREFIX": "/opt/continuum/anaconda",
        "SYMLINK_CONDA": [
          "/opt/continuum/anaconda"
        ],
        "UNLINK": [
          "conda-4.1.6-py27_0",
          "conda-env-2.5.1-py27_0"
        ],
        "op_order": [
          "RM_FETCHED",
          "FETCH",
          "RM_EXTRACTED",
          "EXTRACT",
          "UNLINK",
          "LINK",
          "SYMLINK_CONDA"
        ]
      },
      "success": true
    }

For this example, you need to download the NLTK sample data. Download the data on all cluster nodes by using the `adam cmd` command:

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

The sample data is downloaded over a few minutes. After the download completes, you should see output similar to:

    All nodes (x4) 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)

Examine the above code example. First, it imports PySpark and creates a SparkContext:

    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.

NOTE: You could also copy the data to HDFS and load it from Spark.

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

Next is a function called `word_tokenize` that imports `nltk` on the Spark worker nodes 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)

You can confirm that the `flatMap` operation worked by returning some of the words in the dataset:

    print words.take(10)

Finally, you can use NTLK’s part-of-speech tagger to attach the part of speech to each word in the data set:

    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 Help and support.

## Further information

See the Spark and PySpark documentation:

For more information on NLTK, see the NLTK book.