Github user Stibbons commented on a diff in the pull request:
https://github.com/apache/spark/pull/14830#discussion_r101263182
--- Diff: examples/src/main/python/streaming/network_wordjoinsentiments.py
---
@@ -54,22 +54,25 @@ def print_happiest_words(rdd):
# Read in the word-sentiment list and create a static RDD from it
word_sentiments_file_path = "data/streaming/AFINN-111.txt"
- word_sentiments = ssc.sparkContext.textFile(word_sentiments_file_path)
\
- .map(lambda line: tuple(line.split("\t")))
+ word_sentiments = (ssc.sparkContext
+ .textFile(word_sentiments_file_path)
+ .map(lambda line: tuple(line.split("\t"))))
lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
- word_counts = lines.flatMap(lambda line: line.split(" ")) \
- .map(lambda word: (word, 1)) \
- .reduceByKey(lambda a, b: a + b)
+ word_counts = (lines
+ .flatMap(lambda line: line.split(" "))
+ .map(lambda word: (word, 1))
+ .reduceByKey(lambda a, b: a + b))
# Determine the words with the highest sentiment values by joining the
streaming RDD
# with the static RDD inside the transform() method and then
multiplying
# the frequency of the words by its sentiment value
- happiest_words = word_counts.transform(lambda rdd:
word_sentiments.join(rdd)) \
- .map(lambda word_tuples: (word_tuples[0], float(word_tuples[1][0])
* word_tuples[1][1])) \
- .map(lambda word_happiness: (word_happiness[1],
word_happiness[0])) \
- .transform(lambda rdd: rdd.sortByKey(False))
+ happiest_words = (word_counts
+ .map(lambda word_tuples: (word_tuples[0],
+ float(word_tuples[1][0]) *
word_tuples[1][1]))
--- End diff --
I agree, if you prefer I can change all at once. But like I said, I don't
know any autoformat that does it automatically
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