Advanced RDD Actions

Advanced RDD Actions

 

reduce() action

  • reduce(func) action is used for aggregating the elements of a regular RDD.
  • The fucntion should be commutative (changing the order of the operands does not change the result) and associative.

 

saveAsTextFile() action

  • saveAsTextFile() action saves RDD into a text file inside a directory with each partition as a separate file.

 

countByKey() action

  • countByKey() only available for type(K,V)
  • countByKey() action counts the number of elements for each key

 

collectAsMap() action

  • collectAsMap() return the key-value pairs in the RDD a dictionary

 

Example

CountingBykeys

Rdd = sc.parallelize([(1, 2), (3, 4), (3, 6), (4, 5)])

total = Rdd.countByKey()

# What is the type of total?
print("The type of total is", type(total))

# Iterate over the total and print the output
for k, v in total.items():
  print("key", k, "has", v, "counts")

 

Create a base RDD and transform

Here are the brief steps for writing the word counting program:

 

  • Create a base RDD Complete_Shakespeare file.
  • Use RDD transformation to create a long list of words from each element of the base RDD.
  • Remove stop words from your data.
  • Create pair RDD where each element is a pair tuple of (“w”,1)
  • Group the elements of the pair RDD by key (word) and add up their values.
  • Swap the keys (word) and values (counts) so that keys is count and value is the word.
  • Finally, sort the RDD by descending order and print the 10 most frequent words and their frequencies.

You can dowload here, http://www.gutenberg.org/ebooks/100

baseRDD = sc.textFile("100-0.txt")

# Split the lines of baseRDD into words
splitRDD = baseRDD.flatMap(lambda x: x.split())

# Count the total number of words
print("Total number of words in splitRDD:", splitRDD.count())

 

for word in resultRDD.take(10):
   print(word)

# Swap the keys and values
resultRDD_swap = resultRDD.map(lambda x: (x[1], x[0]))

# Sort the keys in descending order
resultRDD_swap_sort = resultRDD_swap.sortByKey(ascending=False)

# Show the top 10 most frequent words and their frequencies
for word in resultRDD_swap_sort.take(10):
   print("{} has {} counts". format(word[1], word[0]))

See you in the next article..

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About Deniz Parlak

Hi, i’m Security Data Scientist & Data Engineer at My Security Analytics. I have experienced Advance Python, Machine Learning and Big Data tools. Also i worked Oracle Database Administration, Migration and upgrade projects. For your questions [email protected]

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