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You're reading from  Apache Spark for Data Science Cookbook

Product typeBook
Published inDec 2016
Publisher
ISBN-139781785880100
Edition1st Edition
Tools
Apache SparkPandas
Concepts
Data Science
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Author (1)
Padma Priya Chitturi
Padma Priya Chitturi
author image
Padma Priya Chitturi

Padma Priya Chitturi is Analytics Lead at Fractal Analytics Pvt Ltd and has over five years of experience in Big Data processing. Currently, she is part of capability development at Fractal and responsible for solution development for analytical problems across multiple business domains at large scale. Prior to this, she worked for an Airlines product on a real-time processing platform serving one million user requests/sec at Amadeus Software Labs. She has worked on realizing large-scale deep networks (Jeffrey deans work in Google brain) for image classification on the big data platform Spark. She works closely with Big Data technologies such as Spark, Storm, Cassandra and Hadoop. She was an open source contributor to Apache Storm.
Read more about Padma Priya Chitturi

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Working with Spark's Python and Scala shells

This recipe explains the spark-shell and PySpark command-line interface tools from the Apache Spark project. Spark-shell is the Scala-based command line interface tool and PySpark is the Python-based command-line tool used to develop Spark interactive applications. They are already initialized with SparkContext, SQLContext, and HiveContext.

How to do it…

Both spark-shell and PySpark are available in the bin directory of SPARK_HOME, that is, SPARK_HOME/bin:

  1. Invoke spark-shell as follows:

        $SPARK_HOME/bin/spark-shell [Options] 
 
    $SPARK_HOME/bin/spark-shell --master <master type> i.e., local, 
    spark, yarn, mesos. 
    $SPARK_HOME/bin/spark-shell --master
    spark://<sparkmasterHostName>:7077 
 
    Welcome to 
         ____              __ 
        / __/__  ___ _____/ /__ 
       _\ \/ _ \/ _ `/ __/  '_/ 
      /__ / .__/\_,_/_/ /_/\_\   version 1.6.0 
         /_/ 
 
    Using Scala version 2.10.5 (Java HotSpot(TM) 64-Bit Server VM, Java  
    1.7.0_79) 
    Type in expressions to have them evaluated. 
    Type :help for more information. 
    16/01/17 20:05:38 WARN Utils: Your hostname, localhost resolves to
    a loopback address: 127.0.0.1; using 192.168.1.6 instead (on 
    interface en0)
  SQL context available as sqlContext. 
 
    scala> val data = sc.textFile("hdfs://namenode:9000/stocks.txt"); 
    data: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[1]
    textFile at <console>:27 
 
    scala> data.count() 
    res0: Long = 57391  
 
    scala> data.first() 
    res1: String = NYSE  CLI   2009-12-31  35.39 35.70 34.50 34.5
                   890100         34.12 
 
    scala> data.top(2) 
    res5: Array[String] = Array(NYSE CZZ   2009-12-31  8.77  8.77  8.67
         8.70  694200    8.70, NYSE  CZZ   2009- 12-30  8.71  8.80 
         8.46    8.68  1588200     8.68) 
 
    scala> val mydata = data.map(line => line.toLowerCase()) 
    mydata: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[3] at
    map at <console>:29 
 
    scala> mydata.collect() 
    res6: Array[String] = Array(nyse cli 2009-12-31 35.39 35.70
    34.50 34.57 890100 34.12, nyse cli 2009-12-30 35.22 35.46
    34.96 35.40 516900 34.94, nyse cli 2009-12-29 35.69 35.95 
    35.21 35.34 556500 34.88, nyse cli 2009-12-28 35.67 36.23 
    35.49 35.69 565000 35.23, nyse cli 2009-12-24 35.38 35.60 
    35.19 35.47 230200 35.01, nyse cli 2009-12-23 35.13 35.51 
    35.07 35.21 520200 34.75, nyse cli 2009-12-22 34.76 35.04 
    34.71 35.04 564600 34.58, nyse cli 2009-12-21 34.65 34.74
 34.41 34.73 428400 34.28, nyse cli 2009-12-18 34.11 34.38 
    33.73 34.22 1152600 33.77, nyse cli 2009-12-17 34.18 34.53 
    33.84 34.21 1082600 33.76, nyse cli 2009-12-16 34.79 35.10 
    34.48 34.66 1007900 34.21, nyse cli 2009-12-15 34.60 34.91 
    34.39 34.84 813200 34.39, nyse cli 2009-12-14 34.21 34.90 
    33.86 34.82 987700 34.37, nyse cli 200...)

  2. Invoke PySpark as follows:

        $SPARK_HOME/bin/pyspark [options] 
    $SPARK_HOME/bin/pyspark --master <master type> i.e., local, 
    spark, yarn, mesos 
    $SPARK_HOME/bin/pyspark --master spark://
    sparkmasterHostName:7077 
 
    Python 2.7.6 (default, Sep  9 2014, 15:04:36)  
    [GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.39)] on darwin 
    Type "help", "copyright", "credits" or "license" for more
    information. 
    Using Spark's default log4j profile: org/apache/spark/log4j-
    defaults.properties 
    16/01/17 20:25:48 INFO SparkContext: Running Spark version 1.6.0 
    ... 
 
    Welcome to 
         ____              __ 
        / __/__  ___ _____/ /__ 
       _\ \/ _ \/ _ `/ __/  '_/ 
      /__ / .__/\_,_/_/ /_/\_\   version 1.6.0 
         /_/
Using Python version 2.7.6 (default, Sep  9 2014 15:04:36) 
    SparkContext available as sc, HiveContext available as sqlContext. 
 
    >>> data = sc.textFile"hdfs://namenode:9000/stocks.txt"); 
 
    >>> data.count() 
    57391  
    >>> data.first() 
    NYSE     CLI   2009-12-31  35.39 35.70 34.50 34.57 890100
    34.12 
    >>> data.top(2) 
    ['NYSE   CZZ   2009-12-31  8.77  8.77  8.67  8.70  694200   8.70',
     'NYSE   CZZ   2009-12-30  8.71  8.80  8.46  8.68  1588200  8.68' ] 
 
    >>> data.collect()
 ['NYSE CLI 2009-12-31 35.39 35.70 34.50 34.57 890100 34.12, 
     'NYSE CLI 2009-12-30 35.22 35.46 34.96 35.40 516900 34.94, 
     'NYSE CLI 2009-12-29 35.69 35.95 35.21 35.34 556500 34.88', 
     'NYSE CLI 2009-12-28 35.67 36.23 35.49 35.69 565000 35.23', 
     'NYSE CLI 2009-12-24 35.38 35.60 35.19 35.47 230200 35.01', 
     'NYSE CLI 2009-12-23 35.13 35.51 35.07 35.21 520200 34.75', 
     'NYSE CLI 2009-12-22 34.76 35.04 34.71 35.04 564600 34.58', 
     'NYSE CLI 2009-12-21 34.65 34.74 34.41 34.73 428400 34.28', 
     'NYSE CLI 2009-12-18 34.11 34.38 33.73 34.22 1152600 33.77', 
     'NYSE CLI 2009-12-17 34.18 34.53 33.84 34.21 1082600 33.76', 
     'NYSE CLI 2009-12-16 34.79 35.10 34.48 34.66 1007900 34.21', 
     'NYSE CLI 2009-12-15 34.60 34.91 34.39 34.84 813200 34.39', 
     'NYSE CLI 2009-12-14 34.21 34.90 33.86 34.82 987700 34.37', 
     'NYSE CLI 200...

How it works…

In the preceding code snippets, Spark RDD transformations and actions are executed interactively in both Spark-shell and PySpark. They work in Read Eval Print Loop (REPL) style and represent a computer environment such as a Window console or Unix/Linux shell where a command is entered and the system responds with an output in interactive mode.

There's more…

Both Spark-shell and PySpark are better command-line interfaces for developing Spark applications interactively. They have advanced features for application prototyping and quicker development. Also, they have numerous options for customizing them.

See also

The Apache Spark documentation offers plenty of examples using these two command-line interfaces; please refer to this documentation page: http://spark.apache.org/docs/latest/quick-start.html#interactive-analysis-with-the-spark-shell.

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Author (1)

author image
Padma Priya Chitturi

Padma Priya Chitturi is Analytics Lead at Fractal Analytics Pvt Ltd and has over five years of experience in Big Data processing. Currently, she is part of capability development at Fractal and responsible for solution development for analytical problems across multiple business domains at large scale. Prior to this, she worked for an Airlines product on a real-time processing platform serving one million user requests/sec at Amadeus Software Labs. She has worked on realizing large-scale deep networks (Jeffrey deans work in Google brain) for image classification on the big data platform Spark. She works closely with Big Data technologies such as Spark, Storm, Cassandra and Hadoop. She was an open source contributor to Apache Storm.
Read more about Padma Priya Chitturi

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