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spark:using-python [2014/11/10 15:32]
straka 		spark:using-python [2014/11/11 09:28]
straka
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 ==== Usage Examples ==== ==== Usage Examples ====
 +Consider the following simple script computing 10 most frequent words of Czech Wikipedia:
 +<file python>
 +(sc.textFile("/net/projects/spark-example-data/wiki-cs", 3*sc.defaultParallelism)
 +   .flatMap(lambda line: line.split())
 +   .map(lambda word: (word, 1))
 +   .reduceByKey(lambda c1,c2: c1+c2)
 +   .sortBy(lambda (word,count): count, ascending=False)
 +   .take(10))
 +</file>
  
-  * run interactive shell inside ''spark-qrsh'', or start local Spark cluster using as many threads as there are cores:+  * run interactive shell using existing Spark cluster (i.e., inside ''spark-qrsh''), or start local Spark cluster using as many threads as there are cores if there is none:
   <file>IPYTHON=1 pyspark</file>   <file>IPYTHON=1 pyspark</file>
   * run interactive shell with local Spark cluster using one thread:   * run interactive shell with local Spark cluster using one thread:
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 ===== Running Python Spark Scripts ===== ===== Running Python Spark Scripts =====
  
-Python Spark scripts can be started using ''spark-submit''. +Python Spark scripts can be started using: 
 +<file>spark-submit</file>
  
 As described in [[running-spark-on-single-machine-or-on-cluster|Running Spark on Single Machine or on Cluster]], environmental variable ''MASTER'' specifies which Spark master to use (or whether to start a local one). As described in [[running-spark-on-single-machine-or-on-cluster|Running Spark on Single Machine or on Cluster]], environmental variable ''MASTER'' specifies which Spark master to use (or whether to start a local one).
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 from pyspark import SparkContext from pyspark import SparkContext
 +   
 sc = SparkContext() sc = SparkContext()
-(sc.textFile(input)+(sc.textFile(input, 3*sc.defaultParallelism)
    .flatMap(lambda line: line.split())    .flatMap(lambda line: line.split())
    .map(lambda token: (token, 1))    .map(lambda token: (token, 1))
    .reduceByKey(lambda x,y: x + y)    .reduceByKey(lambda x,y: x + y)
 +   .sortBy(lambda (word,count): count, ascending=False)
    .saveAsTextFile(output))    .saveAsTextFile(output))
 sc.stop() sc.stop()
 </file> </file>
  
-  * run ''word_count.py'' script inside ''spark-qsub'', ''spark-qrsh'', or start local Spark cluster using as many threads as there are cores:+  * run ''word_count.py'' script inside existing Spark cluster (i.e., inside ''spark-qsub'' or ''spark-qrsh''), or start local Spark cluster using as many threads as there are cores if there is none:
   <file>spark-submit word_count.py input output</file>   <file>spark-submit word_count.py input output</file>
   * run ''word_count.py'' script with local Spark cluster using one thread:   * run ''word_count.py'' script with local Spark cluster using one thread:
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   * start Spark cluster (10 machines, 1GB RAM each) on SGE and run ''word_count.py'' script:   * start Spark cluster (10 machines, 1GB RAM each) on SGE and run ''word_count.py'' script:
   <file>spark-qsub 10 1G spark-submit word_count.py input output</file>   <file>spark-qsub 10 1G spark-submit word_count.py input output</file>
 +

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