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spark:recipes:using-perl-via-pipes [2014/11/07 11:12]
straka 		spark:recipes:using-perl-via-pipes [2014/11/07 14:16]
straka
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 </file> </file>
  
-==== Complete Example using Simple Perl Tokenizer ====+==== Complete Example using Simple Perl Tokenizer and Python ====
  
 Suppose we want to write program which uses Perl Tokenizer and then produces token counts. Suppose we want to write program which uses Perl Tokenizer and then produces token counts.
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 sc = SparkContext() sc = SparkContext()
 (sc.textFile(input) (sc.textFile(input)
-   .map(json.dumps).pipe("perl tokenize.pl", os.environ).map(json.loads)+   .map(json.dumps).pipe("env perl tokenize.pl", os.environ).map(json.loads)
    .flatMap(lambda tokens: map(lambda x: (x, 1), tokens))    .flatMap(lambda tokens: map(lambda x: (x, 1), tokens))
    .reduceByKey(lambda x,y: x + y)    .reduceByKey(lambda x,y: x + y)
    .saveAsTextFile(output))    .saveAsTextFile(output))
 +sc.stop()
 </file> </file>
  
-It can be executed using ''spark-submit perl_integration.py input output''.+It can be executed using ''spark-submit --files tokenize.pl perl_integration.py input output''. Note that the Perl script has to be added to the list of files used by the job. 
  
 ===== Using Scala and JSON ===== ===== Using Scala and JSON =====
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 // let rdd be an RDD we want to process, creating ''RDD[ProcessedType]'' // let rdd be an RDD we want to process, creating ''RDD[ProcessedType]''
-rdd.map(encodeJson).pipe("perl script").map(decodeJson[ProcessedType])+rdd.map(encodeJson).pipe("perl script.pl").map(decodeJson[ProcessedType])
 </file> </file>
 +

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