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spark:spark-introduction [2022/12/14 12:29]
straka [Running Spark Shell in Python] 		spark:spark-introduction [2022/12/14 12:36]
straka [Word Count Example]
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 We start by simple word count example. We load the RDD from text file, every line of the input file becoming an element of RDD. We then split every line into words, count every word occurrence and sort the words by the occurrences. Copy the following to the opened Python shell: We start by simple word count example. We load the RDD from text file, every line of the input file becoming an element of RDD. We then split every line into words, count every word occurrence and sort the words by the occurrences. Copy the following to the opened Python shell:
 <file python> <file python>
-wiki = sc.textFile("/net/projects/spark-example-data/wiki-cs")+wiki = sc.textFile("/lnet/troja/data/npfl118/wiki/cs/wiki.txt")
 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:
-(sc.textFile("/net/projects/spark-example-data/wiki-cs")+(sc.textFile("/lnet/troja/data/npfl118/wiki/cs/wiki.txt")
    .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:
 <file scala> <file scala>
-val wiki = sc.textFile("/net/projects/spark-example-data/wiki-cs")+val wiki = sc.textFile("/lnet/troja/data/npfl118/wiki/cs/wiki.txt")
 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")
  
 // Alternatively without variables and using placeholders in lambda parameters: // Alternatively without variables and using placeholders in lambda parameters:
-(sc.textFile("/net/projects/spark-example-data/wiki-cs")+(sc.textFile("/lnet/troja/data/npfl118/wiki/cs/wiki.txt")
    .flatMap(_.split("\\s"))    .flatMap(_.split("\\s"))
    .map((_,1)).reduceByKey(_+_)    .map((_,1)).reduceByKey(_+_)

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