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spark:spark-introduction [2014/11/11 08:55]
straka 		spark:spark-introduction [2014/11/11 09:06]
straka
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 ===== Running Spark Shell in Python ===== ===== Running Spark Shell in Python =====
  
-To run interactive Python shell in local Spark mode, run (on your local workstation or on cluster)+To run interactive Python shell in local Spark mode, run (on your local workstation or on cluster using ''qrsh'' from ''lrc1'')
   IPYTHON=1 pyspark   IPYTHON=1 pyspark
-The IPYTHON=1 parameter instructs Spark to use ''ipython'' instead of ''python'' (the ''ipython'' is an enhanced interactive shell than Python). If you do not want ''ipython'' or you do not have it installed (it is installed everywhere on the cluster, but maybe not on your local workstations -- ask our IT if you want it), use only ''pyspark''.+The IPYTHON=1 parameter instructs Spark to use ''ipython'' instead of ''python'' (the ''ipython'' is an enhanced interactive shell than Python). If you do not want ''ipython'' or you do not have it installed (it is installed everywhere on the cluster, but maybe not on your local workstations -- ask our IT if you want it), use only ''pyspark'', but note that it has some issues when copy-pasting examples from this wiki.
  
 After a local Spark executor is started, the Python shell starts. Severel lines above After a local Spark executor is started, the Python shell starts. Severel lines above
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 ===== K-Means Example ===== ===== K-Means Example =====
-To show an example of iterative algorithm, consider [[http://en.wikipedia.org/wiki/K-means_clustering|Standard iterative K-Means algorithm]].+An example implementing [[http://en.wikipedia.org/wiki/K-means_clustering|Standard iterative K-Means algorithm]] follows:
 <file python> <file python>
 import numpy as np import numpy as np
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 lines = sc.textFile("/net/projects/spark-example-data/points", sc.defaultParallelism) lines = sc.textFile("/net/projects/spark-example-data/points", sc.defaultParallelism)
-data = lines.map(lambda line: np.array([float(x) for x in line.split()])).cache()+data = lines.map(lambda line: map(float, line.split())).cache()
  
 K = 50 K = 50

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