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spark:spark-introduction [2022/12/14 12:29]
straka [Running Spark Shell in Python] 		spark:spark-introduction [2022/12/14 13:28] (current)
straka [Running Spark Shell in Python]
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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 using ''qrsh'' from ''lrc1'') +To run interactive Python shell in local Spark mode, run (on your local workstation or on cluster using ''srun'' from ''lrc1'') 
-  PYSPARK_DRIVER_PYTHON=ipython3 pyspark+  MASTER=local PYSPARK_DRIVER_PYTHON=ipython3 pyspark
 The PYSPARK_DRIVER_PYTHON=ipython3 parameter instructs Spark to use ''ipython3'' instead of ''python3''. The PYSPARK_DRIVER_PYTHON=ipython3 parameter instructs Spark to use ''ipython3'' instead of ''python3''.
  
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 wiki = sc.textFile("/net/projects/spark-example-data/wiki-cs") wiki = sc.textFile("/net/projects/spark-example-data/wiki-cs")
 words = wiki.flatMap(lambda line: line.split()) words = wiki.flatMap(lambda line: line.split())
-counts = words.map(lambda word: (word, 1)).reduceByKey(lambda c1,c2: c1+c2) +counts = words.map(lambda word: (word, 1)).reduceByKey(lambda c1, c2: c1+c2) 
-sorted = counts.sortBy(lambda (word,count): count, ascending=False) +sorted = counts.sortBy(lambda word_count: word_count[1], ascending=False) 
-sorted.saveAsTextFile('output')+sorted.saveAsTextFile("output")
  
 # Alternatively, we can avoid variables: # Alternatively, we can avoid variables:
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    .flatMap(lambda line: line.split())    .flatMap(lambda line: line.split())
    .map(lambda word: (word, 1))    .map(lambda word: (word, 1))
-   .reduceByKey(lambda c1,c2: c1+c2) +   .reduceByKey(lambda c1, c2: c1+c2) 
-   .sortBy(lambda (word,count): count, ascending=False)+   .sortBy(lambda word_count: word_count[1], ascending=False)
    .take(10)) # Instead of saveAsTextFile, we only print 10 most frequent words    .take(10)) # Instead of saveAsTextFile, we only print 10 most frequent words
 </file> </file>
 The output of 'saveAsTextFile' is the directory ''output'' -- because the RDD can be distributed on several computers, the output is a directory containing possibly multiple files. The output of 'saveAsTextFile' is the directory ''output'' -- because the RDD can be distributed on several computers, the output is a directory containing possibly multiple files.
  
-Note that 'map' and 'reduceByKey' operations exist, allowing any Hadoop MapReduce operation to be implemented. On the other hand, several operations like 'join', 'sortBy', 'cogroup' are available, which are not available in Hadoop (or at least not directly), making Spark computational model a strict superset of Hadoop computational model.+Note that ''flatMap'' and ''reduceByKey'' operations exist, allowing any Hadoop MapReduce operation to be implemented. On the other hand, several operations like ''join'', ''sortBy'', ''cogroup'' are available, which are not available in Hadoop (or at least not directly), making Spark computational model a strict superset of Hadoop computational model.
  
 The Scala versions is quite similar: The Scala versions is quite similar:
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 val wiki = sc.textFile("/net/projects/spark-example-data/wiki-cs") val wiki = sc.textFile("/net/projects/spark-example-data/wiki-cs")
 val words = wiki.flatMap(line => line.split("\\s")) val words = wiki.flatMap(line => line.split("\\s"))
-val counts = words.map(word => (word,1)).reduceByKey((c1,c2) => c1+c2)+val counts = words.map(word => (word, 1)).reduceByKey((c1, c2) => c1+c2)
 val sorted = counts.sortBy({case (word, count) => count}, ascending=false) val sorted = counts.sortBy({case (word, count) => count}, ascending=false)
 sorted.saveAsTextFile("output") sorted.saveAsTextFile("output")
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 ===== K-Means Example ===== ===== K-Means Example =====
-An example implementing [[http://en.wikipedia.org/wiki/K-means_clustering|Standard iterative K-Means algorithm]] follows. Try copying it to open Python shell. Note that this wiki is formating empty lines as lines with one space, which is confusing for ''pyspark'' used without ''IPYTHON=1'', so either use ''IPYTHON=1'' or copy the text paragraph-by-paragraph.+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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 data = lines.map(lambda line: np.array(map(float, line.split()))).cache() data = lines.map(lambda line: np.array(map(float, line.split()))).cache()
  
-K = 50+K = 100
 epsilon = 1e-3 epsilon = 1e-3
  
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 val data = lines.map(line => Vector(line.split("\\s+").map(_.toDouble))).cache() val data = lines.map(line => Vector(line.split("\\s+").map(_.toDouble))).cache()
  
-val K = 50+val K = 100
 val epsilon = 1e-3 val epsilon = 1e-3
  

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