Lecture 7: Spark Programming

In what follows, you can find pyspark code for the examples we saw in class. Many of the examples follow examples found in Learning Spark: Lightning-Fast Big Data Analysis, by Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia, which you can also find at Aalto's library.

Further information also available here: https://spark.apache.org/docs/1.6.0/programming-guide.html

Setup

These instructions should work for Mac and Linux. We'll assume you'll be using python3.

To run the following on your computer, make sure that pyspark is in your PYTHONPATH variable. You can do that by downloading a zipped file with Spark, extracting it into its own folder (e.g., spark-1.6.0-bin-hadoop2.6/) and then executing the following commands in bash.

export PYSPARK_PYTHON=python3
export SPARK_HOME=/path/to/spark-1.6.0-bin-hadoop2.6/
export PYTHONPATH=$SPARK_HOME/python:$PYTHONPATH

On Windows, the easiest way is to use pyspark is to use Anaconda and then install Jupyter and findspark



In [1]:

    
#On windows
#import findspark
#findspark.init(spark_home="C:/Users/me/software/spark-1.6.3-bin-hadoop2.6/")



In [5]:

    
import pyspark
import numpy as np # we'll be using numpy for some numeric operations
sc = pyspark.SparkContext(master="local[*]", appName="tour")



In [4]:

    
sc.stop()

local: Run Spark locally with one worker thread (i.e. no parallelism at all).
local[K]: Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine).
local[*]: Run Spark locally with as many worker threads as logical cores on your machine.
spark://HOST:PORT: Connect to the given Spark standalone cluster master. The port must be whichever one your master is configured to use, which is 7077 by default.
mesos://HOST:PORT: Connect to the given Mesos cluster. The port must be whichever one your is configured to use, which is 5050 by default. Or, for a Mesos cluster using ZooKeeper, use mesos://zk://.... To submit with --deploy-mode cluster, the HOST:PORT should be configured to connect to the MesosClusterDispatcher.
yarn: Connect to a YARN cluster in client or cluster mode depending on the value of --deploy-mode. The cluster location will be found based on the HADOOP_CONF_DIR or YARN_CONF_DIR variable.
yarn-client: Equivalent to yarn with --deploy-mode client, which is preferred to yarn-client
yarn-cluster: Equivalent to yarn with --deploy-mode cluster, which is preferred to yarn-cluster



In [90]:

    
# To try the SparkContext with other masters first stop the one that is already running
# sc.stop()

Lambda functions

Lambda expressions are an easy way to write short functions in Python.



In [7]:

    
f = lambda line: 'Spark' in line



In [10]:

    
f("we are learning park")









    Out[10]:





False



In [102]:

    
def f(line):
    return 'Spark' in line
f("we are learning Spark")









    Out[102]:





True

Creating RDDS

We saw that we can create RDDs by loading files from disk. We can also create RDDs from Python collections or transforming other RDDs.



In [12]:

    
my_list = [0,1,2,3,4,5,6,7,8,9]



In [13]:

    
data = sc.parallelize(my_list) # create RDD from Python collection



In [15]:

    
data_squared = data.map(lambda num: num ** 2) # transformation



In [17]:

    
data_squared.collect()









    Out[17]:





[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

RDD operations

There are two types of RDD operations in Spark: transformations and actions. Transfromations create new RDDs from other RDDs. Actions extract information from RDDs and return it to the driver program.



In [105]:

    
data = sc.parallelize([0,1,2,3,4,5,6,7,8,9]) # creation of RDD
data_squared = data.map(lambda num: num ** 2) # transformation
data_squared.collect() # action









    Out[105]:





[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]



In [19]:

    
text = sc.textFile("myfile.txt",1) # load data









    Out[19]:





95



In [21]:

    
text = sc.textFile("myfile.txt") # load data



In [22]:

    
# count only lines that mention "Spark"
spark_lines = text.filter(lambda line: 'Spark' in line)



In [23]:

    
spark_lines.collect()









    Out[23]:





['# Apache Spark',
 'Spark is a fast and general cluster computing system for Big Data. It provides',
 'rich set of higher-level tools including Spark SQL for SQL and DataFrames,',
 'and Spark Streaming for stream processing.',
 'You can find the latest Spark documentation, including a programming',
 '## Building Spark',
 'Spark is built using [Apache Maven](http://maven.apache.org/).',
 'To build Spark and its example programs, run:',
 '["Building Spark"](http://spark.apache.org/docs/latest/building-spark.html).',
 'The easiest way to start using Spark is through the Scala shell:',
 'Spark also comes with several sample programs in the `examples` directory.',
 '    ./bin/run-example SparkPi',
 '    MASTER=spark://host:7077 ./bin/run-example SparkPi',
 'Testing first requires [building Spark](#building-spark). Once Spark is built, tests',
 'Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported',
 'Hadoop, you must build Spark against the same version that your cluster runs.',
 'in the online documentation for an overview on how to configure Spark.']

Lazy evaluation

RDDs are evaluated lazily. This means that Spark will not materialize an RDD until it has to perform an action. In the example below, primesRDD is not evaluated until action collect() is performed on it.



In [24]:

    
def is_prime(num):
    """ return True if num is prime, False otherwise"""
    if num < 1 or num % 1 != 0:
        raise Exception("invalid argument")
    for d in range(2, int(np.sqrt(num) + 1)):
        if num % d == 0:
            return False
    return True



In [28]:

    
numbersRDD = sc.parallelize(range(1, 1000000)) # creation of RDD
primesRDD = numbersRDD.filter(is_prime) # transformation



In [29]:

    
# primesRDD has not been materialized until this point
primes = primesRDD.collect() # action



In [36]:

    
type(primesRDD)









    Out[36]:





pyspark.rdd.PipelinedRDD



In [33]:

    
print(primes[0:15])
print(primesRDD.take(15))









    



[1, 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43]
[1, 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43]

Persistence

RDDs are ephemeral by default, i.e. there is no guarantee they will remain in memory after they are materialized. If we want them to persist in memory, possibly to query them repeatedly or use them in multiple operations, we can ask Spark to do this by calling persist() on them.



In [38]:

    
primesRDD.persist()









    Out[38]:





PythonRDD[13] at collect at <ipython-input-29-e29460052702>:2



In [39]:

    
primesRDD.persist() # we're asking Spark to keep this RDD in memory. Note that cache is the same but as using persist for memory. However, persist allows you to define other types of storage

print("Found", primesRDD.count(), "prime numbers") # first action -- causes primesRDD to be materialized
print("Here are some of them:")









    



Found 78499 prime numbers
Here are some of them:
[1, 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67]



In [40]:

    
print(primesRDD.take(20)) # second action - RDD is already in memory









    



[1, 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67]

If we do not need primesRDD in memory anymore, we can tell Spark to discard it.



In [41]:

    
primesRDD.unpersist()









    Out[41]:





PythonRDD[13] at collect at <ipython-input-29-e29460052702>:2

How long does it take to collect primesRDD? Let's time the operation.



In [42]:

    
%%timeit
primes = primesRDD.collect()









    



1 loop, best of 3: 19.2 s per loop

When I ran the above on my laptop, it took about more than 10s. That's because Spark had to evaluate primesRDD before performing collect on it.

How long would it take if primesRDD was already in memory?



In [43]:

    
primesRDD.persist()









    Out[43]:





PythonRDD[13] at collect at <ipython-input-29-e29460052702>:2



In [44]:

    
%%timeit
primes = primesRDD.collect()









    



The slowest run took 17.60 times longer than the fastest. This could mean that an intermediate result is being cached.
1 loop, best of 3: 1.07 s per loop

When I ran the above on my laptop, it took about 1s to collect primesRDD - that's almost $10$ times faster compared to when the RDD had to be recomputed from scratch.

Passing functions

When we pass a function as a parameter to an RDD operation, the function can be specified either as a lambda function or as a reference to a function defined elsewhere.



In [45]:

    
data = sc.parallelize(range(10))
squares = data.map(lambda x: x**2)
squares.collect()









    Out[45]:





[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]



In [46]:

    
def f(x):
    """ return the square of a number"""
    return x**2

data = sc.parallelize(range(10))
squares = data.map(f)
squares.collect()









    Out[46]:





[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Be careful, though: if the function that you pass as argument to an RDD operation

is an object method, or
references an object field,

then Spark will ship the entire object to the cluster nodes along with the function.

This is demonstrated in the piece of code below.



In [121]:

    
class SearchFunctions(object):
    def __init__(self, query):
        self.query
        
    def is_match(self, s):
        return self.query in s
    
    def get_matches_in_rdd_v1(self, rdd):
        return rdd.filter(self.is_match) # the function is an object method
    
    def get_matches_in_rdd_v2(self, rdd):
        return rdd.filter(lambda x: self.query in x) # the function references an object field

The following is a better way to implement the two methods above (get_matches_in_rdd_v1 and get_matches_in_rdd_v2), if we want to avoid sending a SearchFunctions object for computation to the cluster.



In [122]:

    
class SearchFunctions(object):
    def __init__(self, query):
        self.query
        
    def is_match(self, s):
        return self.query in s
    
    def get_matches_in_rdd(self, rdd):
        query = self.query
        return rdd.filter(lambda x: query in x)

map and flatmap



In [47]:

    
phrases = sc.parallelize(["hello world", "terve terve", "how are you"])

words_map = phrases.map(lambda phrase: phrase.split(" "))

words_map.collect() # This returns a list of lists









    Out[47]:





[['hello', 'world'], ['terve', 'terve'], ['how', 'are', 'you']]



In [48]:

    
phrases = sc.parallelize(["hello world", "terve terve", "how are you"])

words_flatmap = phrases.flatMap(lambda phrase: phrase.split(" "))

words_flatmap.collect() # This returns a list withe the combined elements of the list









    Out[48]:





['hello', 'world', 'terve', 'terve', 'how', 'are', 'you']



In [226]:

    
# We can use the flatmap to make a word count
words_flatmap.map(
    lambda x: (x,1)
).reduceByKey(
    lambda x,y: x+y
).collect()









    Out[226]:





[('are', 1), ('hello', 1), ('you', 1), ('how', 1), ('terve', 2), ('world', 1)]

(Pseudo) set operations



In [50]:

    
oneRDD = sc.parallelize([1, 1, 1, 2, 3, 3, 4, 4])
oneRDD.persist()









    Out[50]:





ParallelCollectionRDD[27] at parallelize at PythonRDD.scala:423



In [51]:

    
otherRDD = sc.parallelize([1, 4, 4, 7])
otherRDD.persist()









    Out[51]:





ParallelCollectionRDD[28] at parallelize at PythonRDD.scala:423



In [56]:

    
unionRDD = oneRDD.union(otherRDD)
unionRDD.persist()









    Out[56]:





UnionRDD[33] at union at null:-2



In [24]:

    
oneRDD.subtract(otherRDD).collect()









    Out[24]:





[2, 3, 3]



In [57]:

    
oneRDD.distinct().collect()









    Out[57]:





[1, 2, 3, 4]



In [26]:

    
oneRDD.intersection(otherRDD).collect() # removes duplicates









    Out[26]:





[4, 1]



In [27]:

    
oneRDD.cartesian(otherRDD).collect()[:5]









    Out[27]:





[(1, 1), (1, 4), (1, 4), (1, 7), (1, 1)]

reduce



In [61]:

    
np.sum([1,43,62,23,52])









    Out[61]:





181



In [62]:

    
data = sc.parallelize([1,43,62,23,52])
data.reduce(lambda x, y: x + y)









    Out[62]:





181



In [63]:

    
data.reduce(lambda x, y: x * y)









    Out[63]:





3188536



In [64]:

    
data.reduce(lambda x, y: x**2 + y**2) # this does NOT compute the sum of squares of RDD elements









    Out[64]:





137823683725010149883130929



In [137]:

    
((((1 ** 2 + 43 ** 2) ** 2 + 62 ** 2) **2 + 23 ** 2) **2 + 52 **2)









    Out[137]:





137823683725010149883130929



In [200]:

    
data.reduce(lambda x, y: np.sqrt(x**2 + y**2)) ** 2









    Out[200]:





8927.0



In [198]:

    
np.sum(np.array([1,43,62,23,52]) ** 2)









    Out[198]:





8927

aggregate



In [65]:

    
help(data.aggregate)









    



Help on method aggregate in module pyspark.rdd:

aggregate(zeroValue, seqOp, combOp) method of pyspark.rdd.RDD instance
    Aggregate the elements of each partition, and then the results for all
    the partitions, using a given combine functions and a neutral "zero
    value."
    
    The functions C{op(t1, t2)} is allowed to modify C{t1} and return it
    as its result value to avoid object allocation; however, it should not
    modify C{t2}.
    
    The first function (seqOp) can return a different result type, U, than
    the type of this RDD. Thus, we need one operation for merging a T into
    an U and one operation for merging two U
    
    >>> seqOp = (lambda x, y: (x[0] + y, x[1] + 1))
    >>> combOp = (lambda x, y: (x[0] + y[0], x[1] + y[1]))
    >>> sc.parallelize([1, 2, 3, 4]).aggregate((0, 0), seqOp, combOp)
    (10, 4)
    >>> sc.parallelize([]).aggregate((0, 0), seqOp, combOp)
    (0, 0)



In [68]:

    
def seq(x,y):
    return x[0] + y, x[1] + 1



In [69]:

    
def comb(x,y):
    print(x,y,"comb")
    return x[0] + y[0], x[1] + y[1]



In [73]:

    
data = sc.parallelize([1,43,62,23,52], 1) # Try different levels of paralellism
aggr = data.aggregate(zeroValue = (0,0),
                      seqOp = seq, #
                      combOp = comb)









    



(0, 0) (181, 5) comb



In [74]:

    
aggr









    Out[74]:





(181, 5)



In [75]:

    
aggr[0] / aggr[1] # average value of RDD elements









    Out[75]:





36.2

reduceByKey



In [189]:

    
help(pairRDD.reduceByKey)









    



Help on method reduceByKey in module pyspark.rdd:

reduceByKey(func, numPartitions=None, partitionFunc=<function portable_hash at 0x000001E7C579D378>) method of pyspark.rdd.RDD instance
    Merge the values for each key using an associative reduce function.
    
    This will also perform the merging locally on each mapper before
    sending results to a reducer, similarly to a "combiner" in MapReduce.
    
    Output will be partitioned with C{numPartitions} partitions, or
    the default parallelism level if C{numPartitions} is not specified.
    Default partitioner is hash-partition.
    
    >>> from operator import add
    >>> rdd = sc.parallelize([("a", 1), ("b", 1), ("a", 1)])
    >>> sorted(rdd.reduceByKey(add).collect())
    [('a', 2), ('b', 1)]



In [76]:

    
pairRDD = sc.parallelize([('$APPL', 100.64), 
                          ('$APPL', 100.52), 
                          ('$GOOG', 706.2), 
                          ('$AMZN', 552.32), 
                          ('$AMZN', 552.32) ])

pairRDD.reduceByKey(lambda x,y: x + y).collect() # sum of values per key









    Out[76]:





[('$APPL', 201.16), ('$AMZN', 1104.64), ('$GOOG', 706.2)]

From https://github.com/vaquarkhan/vk-wiki-notes/wiki/reduceByKey--vs-groupBykey-vs-aggregateByKey-vs-combineByKey

ReduceByKey will aggregate y key before shuffling:

GroupByKey will shuffle all the value key pairs as the diagrams show:

combineByKey



In [191]:

    
help(pairRDD.combineByKey)









    



Help on method combineByKey in module pyspark.rdd:

combineByKey(createCombiner, mergeValue, mergeCombiners, numPartitions=None, partitionFunc=<function portable_hash at 0x000001E7C579D378>) method of pyspark.rdd.RDD instance
    Generic function to combine the elements for each key using a custom
    set of aggregation functions.
    
    Turns an RDD[(K, V)] into a result of type RDD[(K, C)], for a "combined
    type" C.  Note that V and C can be different -- for example, one might
    group an RDD of type (Int, Int) into an RDD of type (Int, List[Int]).
    
    Users provide three functions:
    
        - C{createCombiner}, which turns a V into a C (e.g., creates
          a one-element list)
        - C{mergeValue}, to merge a V into a C (e.g., adds it to the end of
          a list)
        - C{mergeCombiners}, to combine two C's into a single one.
    
    In addition, users can control the partitioning of the output RDD.
    
    >>> x = sc.parallelize([("a", 1), ("b", 1), ("a", 1)])
    >>> def add(a, b): return a + str(b)
    >>> sorted(x.combineByKey(str, add, add).collect())
    [('a', '11'), ('b', '1')]



In [78]:

    
pairRDD = sc.parallelize([ ('$APPL', 100.64), ('$GOOG', 706.2), ('$AMZN', 552.32), ('$APPL', 100.52), ('$AMZN', 552.32) ])

aggr = pairRDD.combineByKey(createCombiner = lambda x: (x, 1),
                           mergeValue = lambda x,y: (x[0] + y, x[1] + 1),
                           mergeCombiners = lambda x,y: (x[0] + y[0], x[1] + y[1]))



In [79]:

    
aggr.collect()









    Out[79]:





[('$APPL', (201.16, 2)), ('$AMZN', (1104.64, 2)), ('$GOOG', (706.2, 1))]



In [80]:

    
aggr.map(lambda x: (x[0], x[1][0]/x[1][1])).collect() # average value per key









    Out[80]:





[('$APPL', 100.58), ('$AMZN', 552.32), ('$GOOG', 706.2)]

(inner) join



In [84]:

    
help(course_a.join)









    



Help on method join in module pyspark.rdd:

join(other, numPartitions=None) method of pyspark.rdd.RDD instance
    Return an RDD containing all pairs of elements with matching keys in
    C{self} and C{other}.
    
    Each pair of elements will be returned as a (k, (v1, v2)) tuple, where
    (k, v1) is in C{self} and (k, v2) is in C{other}.
    
    Performs a hash join across the cluster.
    
    >>> x = sc.parallelize([("a", 1), ("b", 4)])
    >>> y = sc.parallelize([("a", 2), ("a", 3)])
    >>> sorted(x.join(y).collect())
    [('a', (1, 2)), ('a', (1, 3))]



In [83]:

    
course_a = sc.parallelize([ ("Antti", 8), ("Tuukka", 10), ("Leena", 9)])
course_b = sc.parallelize([ ("Leena", 10), ("Tuukka", 10)])

result = course_a.join(course_b)
result.collect()









    Out[83]:





[('Tuukka', (10, 10)), ('Leena', (9, 10))]

Accumulators

This example demonstrates how to use accumulators. The map operations creates an RDD that contains the length of lines in the text file - and while the RDD is materialized, an accumulator keeps track of how many lines are long (longer than $30$ characters).



In [85]:

    
text = sc.textFile("myfile.txt")
long_lines = sc.accumulator(0) # create accumulator

def line_len(line):
    global long_lines # to reference an accumulator, declare it as global variable
    length = len(line)
    if length > 30:
        long_lines += 1 # update the accumulator
    return length

llengthRDD = text.map(line_len)
llengthRDD.count()









    Out[85]:





95



In [87]:

    
long_lines.value # this is how we obtain the value of the accumulator in the driver program









    Out[87]:





45



In [261]:

    
help(long_lines)









    



Help on Accumulator in module pyspark.accumulators object:

class Accumulator(builtins.object)
 |  A shared variable that can be accumulated, i.e., has a commutative and associative "add"
 |  operation. Worker tasks on a Spark cluster can add values to an Accumulator with the C{+=}
 |  operator, but only the driver program is allowed to access its value, using C{value}.
 |  Updates from the workers get propagated automatically to the driver program.
 |  
 |  While C{SparkContext} supports accumulators for primitive data types like C{int} and
 |  C{float}, users can also define accumulators for custom types by providing a custom
 |  L{AccumulatorParam} object. Refer to the doctest of this module for an example.
 |  
 |  Methods defined here:
 |  
 |  __iadd__(self, term)
 |      The += operator; adds a term to this accumulator's value
 |  
 |  __init__(self, aid, value, accum_param)
 |      Create a new Accumulator with a given initial value and AccumulatorParam object
 |  
 |  __reduce__(self)
 |      Custom serialization; saves the zero value from our AccumulatorParam
 |  
 |  __repr__(self)
 |      Return repr(self).
 |  
 |  __str__(self)
 |      Return str(self).
 |  
 |  add(self, term)
 |      Adds a term to this accumulator's value
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)
 |  
 |  value
 |      Get the accumulator's value; only usable in driver program



In [262]:

    
long_lines.value # this is how we obtain the value of the accumulator in the driver program









    Out[262]:





45

Warning

In the example above, we update the value of an accumulator within a transformation (map). This is not recommended, unless for debugging purposes! The reason is that, if there are failures during the materialization of llengthRDD, some of its partitions will be re-computed, possibly causing the accumulator to double-count some the the long lines.

It is advisable to use accumulators within actions - and particularly with the foreach action, as demonstrated below.



In [265]:

    
text = sc.textFile("myfile.txt")
long_lines_2 = sc.accumulator(0)

def line_len(line):
    global long_lines_2
    length = len(line)
    if length > 30:
        long_lines_2 += 1

text.foreach(line_len)

long_lines_2.value









    Out[265]:





45

Broadcast variable

We use broadcast variables when many operations depend on the same large static object - e.g., a large lookup table that does not change but provides information for other operations. In such cases, we can make a broadcast variable out of the object and thus make sure that the object will be shipped to the cluster only once - and not for each of the operations we'll be using it for.

The example below demonstrates the usage of broadcast variables. In this case, we make a broadcast variable out of a dictionary that represents an address table. The tablke is shipped to cluster nodes only once across multiple operations.



In [88]:

    
def load_address_table():
    return {"Anu": "Chem. A143", "Karmen": "VTT, 74", "Michael": "OIH, B253.2",
            "Anwar": "T, B103", "Orestis": "T, A341", "Darshan": "T, A325"}

address_table = sc.broadcast(load_address_table())



In [267]:

    
def find_address(name):
    res = None
    if name in address_table.value:
        res = address_table.value[name]
    return res

people = sc.parallelize(["Anwar", "Michael", "Orestis", "Darshan"])
pairRDD = people.map(lambda name: (name, find_address(name))) # first operation that uses the address table
print(pairRDD.collectAsMap())

other_people = sc.parallelize(["Karmen", "Michael", "Anu"])
pairRDD = other_people.map(lambda name: (name, find_address(name))) # second operation that uses the address table
print(pairRDD.collectAsMap())









    



{'Anwar': 'T, B103', 'Michael': 'OIH, B253.2', 'Darshan': 'T, A325', 'Orestis': 'T, A341'}
{'Anu': 'Chem. A143', 'Karmen': 'VTT, 74', 'Michael': 'OIH, B253.2'}

Stopping

Call stop() on the SparkContext object to shut it down.



In [40]:

    
sc.stop()

Example: Estimating Pi



In [41]:

    
import random

NUM_SAMPLES = 10000000

def inside(p):
    x, y = random.random(), random.random()
    return x*x + y*y < 1

count = sc.parallelize(range(0, NUM_SAMPLES)).filter(inside).count()
print("Pi is roughly %f" % (4.0 * count / NUM_SAMPLES))









    



Pi is roughly 3.141248

Example: Computing PageRank



In [5]:

    
# Details of the algorithm can be found here: http://www.cs.princeton.edu/~chazelle/courses/BIB/pagerank.htm
iterations = 5



In [23]:

    
def computeContribs(urls, rank):
    """Calculates URL contributions to the rank of other URLs."""
    num_urls = len(urls)
    for url in urls:
        yield (url, rank / num_urls)

def parseNeighbors(urls):
    """Parses a urls pair string into urls pair."""
    parts = urls.split(',')
    return parts[0], parts[1]



In [24]:

    
# Read the lines
lines = sc.textFile("higgs-mention_network.txt").persist()



In [25]:

    
lines.collect()









    Out[25]:





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In [26]:

    
# Loads all URLs from input file and initialize their neighbors.
links = lines.map(lambda urls: parseNeighbors(urls)).distinct().groupByKey().cache()



In [27]:

    
links.collect()









    Out[27]:





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In [28]:

    
# Loads all URLs with other URL(s) link to from input file and initialize ranks of them to one.
ranks = links.map(lambda url_neighbors: (url_neighbors[0], 1.0))



In [29]:

    
ranks.collect()









    Out[29]:





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 ('185112', 1.0),
 ...]



In [33]:

    
# Calculates and updates URL ranks continuously using PageRank algorithm.
for iteration in range(iterations):
    # Calculates URL contributions to the rank of other URLs.
    contribs = links.join(ranks).flatMap(
        lambda url_urls_rank: computeContribs(url_urls_rank[1][0], url_urls_rank[1][1]))
    # Re-calculates URL ranks based on neighbor contributions.
    ranks = contribs.reduceByKey(lambda x,y: x+y).mapValues(lambda rank: rank * 0.85 + 0.15)
# Collects all URL ranks and dump them to console.
for (link, rank) in ranks.collect():
    print("%s has rank: %s." % (link, rank))









    



316785 has rank: 0.32887921963500977.
27868 has rank: 0.2817196222974318.
132749 has rank: 0.7035119357910156.
261872 has rank: 1.1748629910836759.
85113 has rank: 2.5085980625.
59817 has rank: 0.4159359744037455.
373297 has rank: 0.1835724325019531.
175682 has rank: 1.0.
5772 has rank: 1.3771495156249993.
22991 has rank: 0.6018105371623558.
17866 has rank: 0.7410292739900823.
372414 has rank: 0.3745169113159179.
169034 has rank: 1.3771495156249993.
89946 has rank: 0.25175104831252915.
69902 has rank: 0.21126345012431413.
22371 has rank: 0.8213711594238279.
321667 has rank: 0.7035119357910156.
35941 has rank: 0.7559792050808376.
306525 has rank: 1.3771495156249993.
241712 has rank: 1.0.
317 has rank: 52.27317648159579.
4380 has rank: 14.055130840607882.
39862 has rank: 1.918843536458333.
24341 has rank: 0.24433453718171294.
321295 has rank: 0.7035119357910156.
55950 has rank: 1.2828621367187494.
54798 has rank: 0.3802194771947873.
357068 has rank: 0.8272641206054686.
217327 has rank: 1.8208548281249992.
160665 has rank: 0.2770111545410156.
381348 has rank: 0.3660055859375.
46825 has rank: 1.3142912630208328.
156356 has rank: 0.26915387296549476.
184276 has rank: 1.7542990312499995.
330420 has rank: 0.7035119357910156.
256198 has rank: 1.0.
277606 has rank: 1.3771495156249993.
106399 has rank: 0.2355343363342285.
198577 has rank: 1.0.
46055 has rank: 1.3771495156249993.
5376 has rank: 0.2814058712811104.
28137 has rank: 0.24458131410916267.
45729 has rank: 0.1698316866676102.
14462 has rank: 1.0.
446092 has rank: 1.0.
3650 has rank: 0.18502203023627972.
16224 has rank: 2.2471283366614085.
22814 has rank: 4.478127212348737.
45297 has rank: 1.0.
301588 has rank: 0.3660055859375.
178886 has rank: 0.9757420334472655.
30097 has rank: 0.2951153586054539.
47770 has rank: 2.5751538593749994.
64656 has rank: 1.4226567109201498.
39669 has rank: 0.1835724325019531.
110223 has rank: 1.8633296685330285.
172489 has rank: 1.3771495156249993.
4177 has rank: 2.306807235245554.
204528 has rank: 1.3771495156249993.
296046 has rank: 0.26915387296549476.
52194 has rank: 0.5948558549765836.
217388 has rank: 0.16342211434634613.
69413 has rank: 1.0.
32146 has rank: 0.25962611374561834.
29542 has rank: 3.798718516693673.
217068 has rank: 0.24394044284435457.
53306 has rank: 0.44313145894417116.
147994 has rank: 0.3425699445800781.
24704 has rank: 3.269376445352236.
4917 has rank: 1.0.
3861 has rank: 4.131939615544816.
42912 has rank: 1.0.
14677 has rank: 0.7315688501165625.
152952 has rank: 0.7793349997642317.
971 has rank: 0.22720180461516204.
49716 has rank: 6.211688649739583.
370945 has rank: 1.0.
77070 has rank: 0.4115281625893513.
87399 has rank: 1.0.
228257 has rank: 1.7542990312499995.
70102 has rank: 0.3590308757904407.
372964 has rank: 0.8569127265624998.
48077 has rank: 0.3660055859375.
7606 has rank: 0.1537335287241375.
57782 has rank: 0.6205820285779242.
116924 has rank: 0.26522523217773436.
202696 has rank: 0.37196356456652574.
29296 has rank: 3.3950233388434548.
176916 has rank: 0.3660055859375.
9876 has rank: 0.1614389172718744.
235068 has rank: 0.26522523217773436.
36997 has rank: 0.28732789907375356.
15493 has rank: 0.17386987884196528.
77042 has rank: 0.26522523217773436.
425732 has rank: 1.0.
216624 has rank: 0.8528762662353514.
79410 has rank: 0.3804504643554687.
237114 has rank: 1.296488064208984.
18028 has rank: 6.03815185669431.
132632 has rank: 3.391752928617174.
66928 has rank: 0.27712670279947915.
151930 has rank: 1.0.
39362 has rank: 0.2755952246169818.
101963 has rank: 1.3771495156249993.
43597 has rank: 0.5973675589843818.
45169 has rank: 0.3895774306640625.
10822 has rank: 0.199183878845875.
201087 has rank: 1.0.
58179 has rank: 0.46110474804687496.
50977 has rank: 0.2944207838255956.
376727 has rank: 2.1314485468749993.
17175 has rank: 3.4498105932835146.
100351 has rank: 0.26522523217773436.
6657 has rank: 0.20832113122868162.
231527 has rank: 0.2595487828674316.
279098 has rank: 1.0.
16166 has rank: 0.28244478258540723.
44054 has rank: 1.1030822727856788.
104600 has rank: 1.1885747578124997.
154889 has rank: 0.26522523217773436.
83266 has rank: 0.3660055859375.
377471 has rank: 0.2770111545410156.
162866 has rank: 0.39542750203323357.
108152 has rank: 1.7542990312499995.
432945 has rank: 0.46110474804687496.
66685 has rank: 1.0.
73230 has rank: 0.22429254869732024.
395015 has rank: 0.21159589642610366.
54833 has rank: 0.8569127265624998.
159783 has rank: 1.0.
34566 has rank: 0.20209013368743522.
133894 has rank: 1.0.
112788 has rank: 1.0.
139269 has rank: 0.18287019136694443.
41313 has rank: 0.20270998922164352.
83315 has rank: 0.7035119357910156.
206275 has rank: 0.2887970769042969.
18018 has rank: 0.5745146085963013.
183493 has rank: 1.3771495156249993.
38920 has rank: 1.0.
162613 has rank: 1.0.
92650 has rank: 0.16393474155123436.
102917 has rank: 0.26399405092592587.
184258 has rank: 1.0.
13284 has rank: 1.5439924338378903.
38015 has rank: 0.35515562854560173.
6695 has rank: 0.7846585277104525.
139082 has rank: 0.3660055859375.
27635 has rank: 3.105751462239583.
165551 has rank: 0.22406299384899997.
262320 has rank: 0.8026574733427909.
268162 has rank: 1.8518988203124997.
230914 has rank: 1.7542990312499995.
329954 has rank: 1.0.
10018 has rank: 0.2829041157226562.
46842 has rank: 0.15799407743043237.
88039 has rank: 0.34405501056670096.
89734 has rank: 0.1900811964992947.
77182 has rank: 0.2829041157226562.
440948 has rank: 1.0.
7057 has rank: 0.47631395895336337.
395281 has rank: 1.0.
995 has rank: 0.2210082736322686.
12564 has rank: 0.27167719714009236.
344093 has rank: 1.0.
235067 has rank: 0.26522523217773436.
293259 has rank: 1.3771495156249993.
132220 has rank: 1.0.
59958 has rank: 2.185327049107142.
239934 has rank: 1.3771495156249993.
321070 has rank: 0.3181792798744484.
104774 has rank: 0.6162261392822266.
59231 has rank: 0.16211236280977895.
405829 has rank: 0.7035119357910156.
26486 has rank: 19.51506975553515.
222967 has rank: 0.26522523217773436.
8786 has rank: 0.4414310913749999.
67814 has rank: 0.20500618284324362.
151279 has rank: 0.43672112011718744.
58596 has rank: 0.4291180800241384.
115093 has rank: 0.18995814784473314.
35509 has rank: 0.24922463156918517.
118309 has rank: 0.2185208003275836.
265060 has rank: 0.20971139450445817.
175440 has rank: 0.8569127265624998.
36124 has rank: 1.3771495156249993.
142840 has rank: 2.5097168237119654.
129450 has rank: 0.4057527002037524.
285037 has rank: 1.0.
33493 has rank: 0.5553921269531249.
74518 has rank: 1.0.
310318 has rank: 1.3771495156249993.
110708 has rank: 0.3071718857708293.
278853 has rank: 0.20971139450445817.
62575 has rank: 0.30855582910156243.
34387 has rank: 0.6612323896095459.
278433 has rank: 1.5657242734374994.
61549 has rank: 2.7200105937499997.
55075 has rank: 0.3748981854718578.
137135 has rank: 1.0213263991810917.
210703 has rank: 0.46110474804687496.
59245 has rank: 0.2064804831782644.
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39883 has rank: 0.5026336257151669.
48876 has rank: 0.26382283628095676.
86643 has rank: 0.3291341835133745.
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59945 has rank: 0.4297566008697087.
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166311 has rank: 0.26522523217773436.
47714 has rank: 0.41166158159044053.
392415 has rank: 0.1537335287241375.
35235 has rank: 0.17624345275452108.
15155 has rank: 0.5554084670353222.
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35795 has rank: 1.0.
118534 has rank: 0.2493130728426733.
281184 has rank: 0.374394943287037.
134082 has rank: 0.20052064181518553.
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19153 has rank: 0.7012474283024456.
54444 has rank: 1.5028660208333329.
5795 has rank: 2.141464360314517.
32534 has rank: 0.213325448955758.
11333 has rank: 0.29923027836612653.
11135 has rank: 0.7451638276109724.
42924 has rank: 0.27817759017139737.
186831 has rank: 0.6656406902600308.
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353247 has rank: 0.334305865234375.
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34669 has rank: 1.0602112309641307.
76027 has rank: 1.0.
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212534 has rank: 0.3171966728291745.
402405 has rank: 1.0.
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331888 has rank: 1.0.
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272841 has rank: 1.296488064208984.
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449209 has rank: 1.0.
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441500 has rank: 1.0.
59771 has rank: 1.0.
124732 has rank: 1.0.
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391413 has rank: 0.34405501056670096.
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45389 has rank: 0.174226835899353.
261734 has rank: 0.374394943287037.
49007 has rank: 1.8957703816759828.
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58950 has rank: 0.16215369148456849.
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