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Both sides previous revision Previous revision Next revision | Previous revision Next revision Both sides next revision | ||
spark:spark-introduction [2014/11/03 20:35] 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 |
IPYTHON=1 pyspark | IPYTHON=1 pyspark | ||
- | The IPYTHON=1 parameter instructs Spark to use '' | + | The IPYTHON=1 parameter instructs Spark to use '' |
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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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. Try the following in 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. Try the following in the opened Python shell: | ||
<file python> | <file python> | ||
- | wiki = sc.textFile("/ | + | wiki = sc.textFile("/ |
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) | ||
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# Alternatively, | # Alternatively, | ||
- | (sc.textFile("/ | + | (sc.textFile("/ |
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The Scala versions is quite similar: | The Scala versions is quite similar: | ||
<file scala> | <file scala> | ||
- | val wiki = sc.textFile("/ | + | val wiki = sc.textFile("/ |
val words = wiki.flatMap(line => line.split(" | val words = wiki.flatMap(line => line.split(" | ||
- | val counts = words.map(word => (word, | + | val counts = words.map(word => (word, |
val sorted = counts.sortBy({case (word, count) => count}, ascending=false) | val sorted = counts.sortBy({case (word, count) => count}, ascending=false) | ||
sorted.saveAsTextFile(' | sorted.saveAsTextFile(' | ||
// Alternatively without variables and using placeholders in lambda parameters: | // Alternatively without variables and using placeholders in lambda parameters: | ||
- | (sc.textFile("/ | + | (sc.textFile("/ |
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===== K-Means Example ===== | ===== K-Means Example ===== | ||
- | To show an example | + | An example |
<file python> | <file python> | ||
import numpy as np | import numpy as np | ||
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return min((np.sum((point - centers[i]) ** 2), i) for i in range(len(centers)))[1] | return min((np.sum((point - centers[i]) ** 2), i) for i in range(len(centers)))[1] | ||
- | lines = sc.textFile("/ | + | lines = sc.textFile("/ |
- | 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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centers.map(center => (center-point).norm(2)).zipWithIndex.min._2 | centers.map(center => (center-point).norm(2)).zipWithIndex.min._2 | ||
- | val lines = sc.textFile("/ | + | val lines = sc.textFile("/ |
val data = lines.map(line => Vector(line.split(" | val data = lines.map(line => Vector(line.split(" | ||