Cloudera Enterprise 6.0.x | Other versions

Running Your First Spark Application

The simplest way to run a Spark application is by using the Scala or Python shells.
  1. To start one of the shell applications, run one of the following commands:
    • Scala: 
      $ SPARK_HOME/bin/spark-shell
Welcome to
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Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_67)
Type in expressions to have them evaluated.
Type :help for more information
...
SQL context available as sqlContext.

scala>
    • Python: 
      $ SPARK_HOME/bin/pyspark
Python 2.6.6 (r266:84292, Jul 23 2015, 15:22:56)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-11)] on linux2
Type "help", "copyright", "credits" or "license" for more information
...
Welcome to
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Using Python version 2.6.6 (r266:84292, Jul 23 2015 15:22:56)
SparkContext available as sc, HiveContext available as sqlContext.
>>>

    In a CDH deployment, SPARK_HOME defaults to /usr/lib/spark in package installations and /opt/cloudera/parcels/CDH/lib/spark in parcel installations. In a Cloudera Manager deployment, the shells are also available from /usr/bin.

    For a complete list of shell options, run spark-shell or pyspark with the -h flag.

  2. To run the classic Hadoop word count application, copy an input file to HDFS: 
    $ hdfs dfs -put input
  3. Within a shell, run the word count application using the following code examples, substituting for namenode_host, path/to/input, and path/to/output:
    • Scala 
      scala> val myfile = sc.textFile("hdfs://namenode_host:8020/path/to/input")
scala> val counts = myfile.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey(_ + _)
scala> counts.saveAsTextFile("hdfs://namenode:8020/path/to/output")
    • Python 
      >>> myfile = sc.textFile("hdfs://namenode_host:8020/path/to/input")
>>> counts = myfile.flatMap(lambda line: line.split(" ")).map(lambda word: (word, 1)).reduceByKey(lambda v1,v2: v1 + v2)
>>> counts.saveAsTextFile("hdfs://namenode:8020/path/to/output")
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