Example: Distributing Dependencies on a PySpark Cluster

Although Python is a popular choice for data scientists, it is not straightforward to make a Python library available on a distributed PySpark cluster. To determine which dependencies are required on the cluster, you must understand that Spark code applications run in Spark executor processes distributed throughout the cluster. If the Python code you are running uses any third-party libraries, Spark executors require access to those libraries when they run on remote executors.

This example demonstrates a way to run the following Python code (nltk_sample.py), that includes pure Python libraries (nltk), on a distributed PySpark cluster.

  1. (Prerequisites)
    • Make sure the Anaconda parcel has been distributed and activated on your cluster.
    • Create a new project in Cloudera Machine Learning. In that project, create a new file called nltk_sample.py with the following sample script.
      nltk_sample.py
      # This code uses NLTK, a Python natural language processing library.
      # NLTK is not installed with conda by default. 
      # You can use 'import' within your Python UDFs, to use Python libraries.
      # The goal here is to distribute NLTK with the conda environment.
      
      import os
      import sys
      from pyspark.sql import SparkSession
       
      spark = SparkSession.builder \
                .appName("spark-nltk") \
                .getOrCreate()
       
      data = spark.sparkContext.textFile('1970-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)
      words.saveAsTextFile('nixon_tokens')
       
      pos_word = words.map(pos_tag)
      pos_word.saveAsTextFile('nixon_token_pos')
  2. Go to the project you created and launch a new PySpark session.
  3. Click Terminal Access and run the following command to pack the Python environment into conda.
    conda create -n nltk_env --copy -y -q python=2.7.11 nltk numpy
    The --copy option allows you to copy whole dependent packages into certain directory of a conda environment. If you want to add extra pip packages without conda, you should copy packages manually after using pip install. In Cloudera Machine Learning, pip will install packages into the ~/.local directory in a project.
    pip install some-awesome-package
    cp -r ~/.local/lib ~/.conda/envs/nltk_env/
    Zip the conda environment for shipping on PySpark cluster.
    cd ~/.conda/envs
    zip -r ../../nltk_env.zip nltk_env
  4. (Specific to NLTK) For this example, you can use NLTK data as input.
    cd ~/
    source activate nltk_env
    
    # download nltk data
    (nltk_env)$ python -m nltk.downloader -d nltk_data all
    (nltk_env)$ hdfs dfs -put nltk_data/corpora/state_union/1970-Nixon.txt ./
      
    # archive nltk data for distribution
    cd ~/nltk_data/tokenizers/
    zip -r ../../tokenizers.zip *
    cd ~/nltk_data/taggers/
    zip -r ../../taggers.zip *
  5. Set spark-submit options in spark-defaults.conf.
    spark.yarn.appMasterEnv.PYSPARK_PYTHON=./NLTK/nltk_env/bin/python
    spark.yarn.appMasterEnv.NLTK_DATA=./
    spark.executorEnv.NLTK_DATA=./
    spark.yarn.dist.archives=nltk_env.zip#NLTK,tokenizers.zip#tokenizers,taggers.zip#taggers

    With these settings, PySpark unzips nltk_env.zip into the NLTK directory. NLTK_DATA is the environmental variable where NLTK data is stored.

  6. In Cloudera Machine Learning, set the PYSPARK_PYTHON environment variable to the newly-created environment. To do this, navigate back to the Project Overview page and click Settings > Engine > Environment Variables. Set PYSPARK_PYTHON to ./NLTK/nltk_env/bin/python and click Add. Then click Save Environment.
  7. Launch a new PySpark session and run the nltk_sample.py script in the workbench. You can test whether the script ran successfully using the following command:
    !hdfs dfs -cat ./nixon_tokens/* | head -n 20
    Annual
    Message
    to
    the
    Congress
    on
    the
    State
    of
    the
    Union
    .
    January
    22
    ,
    1970
    Mr.
    Speaker
    ,
    Mr.
      
    ! hdfs dfs -cat nixon_token_pos/* | head -n 20
    [(u'Annual', 'JJ')]
    [(u'Message', 'NN')]
    [(u'to', 'TO')]
    [(u'the', 'DT')]
    [(u'Congress', 'NNP')]
    [(u'on', 'IN')]
    [(u'the', 'DT')]
    [(u'State', 'NNP')]
    [(u'of', 'IN')]
    [(u'the', 'DT')]
    [(u'Union', 'NN')]
    [(u'.', '.')]
    [(u'January', 'NNP')]
    [(u'22', 'CD')]
    [(u',', ',')]
    [(u'1970', 'CD')]
    [(u'Mr.', 'NNP')]
    [(u'Speaker', 'NN')]
    [(u',', ',')]
    [(u'Mr.', 'NNP')]