import IPython
print("pyspark version:" + str(sc.version))
print("Ipython version:" + str(IPython.__version__))
pyspark version:1.6.1 Ipython version:4.2.0
# map
x = sc.parallelize([1,2,3]) # sc = spark context, parallelize creates an RDD from the passed object
y = x.map(lambda x: (x,x**2))
print(x.collect()) # collect copies RDD elements to a list on the driver
print(y.collect())
[1, 2, 3] [(1, 1), (2, 4), (3, 9)]
# flatMap
x = sc.parallelize([1,2,3])
y = x.flatMap(lambda x: (x, 100*x, x**2))
print(x.collect())
print(y.collect())
[1, 2, 3] [1, 100, 1, 2, 200, 4, 3, 300, 9]
# mapPartitions
x = sc.parallelize([1,2,3], 2)
def f(iterator): yield sum(iterator)
y = x.mapPartitions(f)
print(x.glom().collect()) # glom() flattens elements on the same partition
print(y.glom().collect())
[[1], [2, 3]] [[1], [5]]
# mapPartitionsWithIndex
x = sc.parallelize([1,2,3], 2)
def f(partitionIndex, iterator): yield (partitionIndex,sum(iterator))
y = x.mapPartitionsWithIndex(f)
print(x.glom().collect()) # glom() flattens elements on the same partition
print(y.glom().collect())
[[1], [2, 3]] [[(0, 1)], [(1, 5)]]
# getNumPartitions
x = sc.parallelize([1,2,3], 2)
y = x.getNumPartitions()
print(x.glom().collect())
print(y)
[[1], [2, 3]] 2
# filter
x = sc.parallelize([1,2,3])
y = x.filter(lambda x: x%2 == 1) # filters out even elements
print(x.collect())
print(y.collect())
[1, 2, 3] [1, 3]
# distinct
x = sc.parallelize(['A','A','B'])
y = x.distinct()
print(x.collect())
print(y.collect())
['A', 'A', 'B'] ['A', 'B']
# sample
x = sc.parallelize(range(7))
ylist = [x.sample(withReplacement=False, fraction=0.5) for i in range(5)] # call 'sample' 5 times
print('x = ' + str(x.collect()))
for cnt,y in zip(range(len(ylist)), ylist):
print('sample:' + str(cnt) + ' y = ' + str(y.collect()))
x = [0, 1, 2, 3, 4, 5, 6] sample:0 y = [0, 6] sample:1 y = [4] sample:2 y = [1, 2, 3] sample:3 y = [2, 3, 5, 6] sample:4 y = [1, 2]
# takeSample
x = sc.parallelize(range(7))
ylist = [x.takeSample(withReplacement=False, num=3) for i in range(5)] # call 'sample' 5 times
print('x = ' + str(x.collect()))
for cnt,y in zip(range(len(ylist)), ylist):
print('sample:' + str(cnt) + ' y = ' + str(y)) # no collect on y
x = [0, 1, 2, 3, 4, 5, 6] sample:0 y = [5, 4, 3] sample:1 y = [4, 0, 2] sample:2 y = [1, 2, 4] sample:3 y = [5, 6, 0] sample:4 y = [3, 1, 6]
# union
x = sc.parallelize(['A','A','B'])
y = sc.parallelize(['D','C','A'])
z = x.union(y)
print(x.collect())
print(y.collect())
print(z.collect())
['A', 'A', 'B'] ['D', 'C', 'A'] ['A', 'A', 'B', 'D', 'C', 'A']
# intersection
x = sc.parallelize(['A','A','B'])
y = sc.parallelize(['A','C','D'])
z = x.intersection(y)
print(x.collect())
print(y.collect())
print(z.collect())
['A', 'A', 'B'] ['A', 'C', 'D'] ['A']
# sortByKey
x = sc.parallelize([('B',1),('A',2),('C',3)])
y = x.sortByKey()
print(x.collect())
print(y.collect())
[('B', 1), ('A', 2), ('C', 3)] [('A', 2), ('B', 1), ('C', 3)]
# sortBy
x = sc.parallelize(['Cat','Apple','Bat'])
def keyGen(val): return val[0]
y = x.sortBy(keyGen)
print(y.collect())
['Apple', 'Bat', 'Cat']
# glom
x = sc.parallelize(['C','B','A'], 2)
y = x.glom()
print(x.collect())
print(y.collect())
['C', 'B', 'A'] [['C'], ['B', 'A']]
# cartesian
x = sc.parallelize(['A','B'])
y = sc.parallelize(['C','D'])
z = x.cartesian(y)
print(x.collect())
print(y.collect())
print(z.collect())
['A', 'B'] ['C', 'D'] [('A', 'C'), ('A', 'D'), ('B', 'C'), ('B', 'D')]
# groupBy
x = sc.parallelize([1,2,3])
y = x.groupBy(lambda x: 'A' if (x%2 == 1) else 'B' )
print(x.collect())
print([(j[0],[i for i in j[1]]) for j in y.collect()]) # y is nested, this iterates through it
[1, 2, 3] [('A', [1, 3]), ('B', [2])]
# pipe
x = sc.parallelize(['A', 'Ba', 'C', 'AD'])
y = x.pipe('grep -i "A"') # calls out to grep, may fail under Windows
print(x.collect())
print(y.collect())
['A', 'Ba', 'C', 'AD'] [u'A', u'Ba', u'AD']
# foreach
from __future__ import print_function
x = sc.parallelize([1,2,3])
def f(el):
'''side effect: append the current RDD elements to a file'''
f1=open("./foreachExample.txt", 'a+')
print(el,file=f1)
open('./foreachExample.txt', 'w').close() # first clear the file contents
y = x.foreach(f) # writes into foreachExample.txt
print(x.collect())
print(y) # foreach returns 'None'
# print the contents of foreachExample.txt
with open("./foreachExample.txt", "r") as foreachExample:
print (foreachExample.read())
[1, 2, 3] None 1 3 2
# foreachPartition
from __future__ import print_function
x = sc.parallelize([1,2,3],5)
def f(parition):
'''side effect: append the current RDD partition contents to a file'''
f1=open("./foreachPartitionExample.txt", 'a+')
print([el for el in parition],file=f1)
open('./foreachPartitionExample.txt', 'w').close() # first clear the file contents
y = x.foreachPartition(f) # writes into foreachExample.txt
print(x.glom().collect())
print(y) # foreach returns 'None'
# print the contents of foreachExample.txt
with open("./foreachPartitionExample.txt", "r") as foreachExample:
print (foreachExample.read())
[[], [1], [], [2], [3]] None [] [1] [] [2] [3]
# collect
x = sc.parallelize([1,2,3])
y = x.collect()
print(x) # distributed
print(y) # not distributed
ParallelCollectionRDD[84] at parallelize at PythonRDD.scala:423 [1, 2, 3]
# reduce
x = sc.parallelize([1,2,3])
y = x.reduce(lambda obj, accumulated: obj + accumulated) # computes a cumulative sum
print(x.collect())
print(y)
[1, 2, 3] 6
# fold
x = sc.parallelize([1,2,3])
neutral_zero_value = 0 # 0 for sum, 1 for multiplication
y = x.fold(neutral_zero_value,lambda obj, accumulated: accumulated + obj) # computes cumulative sum
print(x.collect())
print(y)
[1, 2, 3] 6
# aggregate
x = sc.parallelize([2,3,4])
neutral_zero_value = (0,1) # sum: x+0 = x, product: 1*x = x
seqOp = (lambda aggregated, el: (aggregated[0] + el, aggregated[1] * el))
combOp = (lambda aggregated, el: (aggregated[0] + el[0], aggregated[1] * el[1]))
y = x.aggregate(neutral_zero_value,seqOp,combOp) # computes (cumulative sum, cumulative product)
print(x.collect())
print(y)
[2, 3, 4] (9, 24)
# max
x = sc.parallelize([1,3,2])
y = x.max()
print(x.collect())
print(y)
[1, 3, 2] 3
# min
x = sc.parallelize([1,3,2])
y = x.min()
print(x.collect())
print(y)
[1, 3, 2] 1
# sum
x = sc.parallelize([1,3,2])
y = x.sum()
print(x.collect())
print(y)
[1, 3, 2] 6
# count
x = sc.parallelize([1,3,2])
y = x.count()
print(x.collect())
print(y)
[1, 3, 2] 3
# histogram (example #1)
x = sc.parallelize([1,3,1,2,3])
y = x.histogram(buckets = 2)
print(x.collect())
print(y)
[1, 3, 1, 2, 3] ([1, 2, 3], [2, 3])
# histogram (example #2)
x = sc.parallelize([1,3,1,2,3])
y = x.histogram([0,0.5,1,1.5,2,2.5,3,3.5])
print(x.collect())
print(y)
[1, 3, 1, 2, 3] ([0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5], [0, 0, 2, 0, 1, 0, 2])
# mean
x = sc.parallelize([1,3,2])
y = x.mean()
print(x.collect())
print(y)
[1, 3, 2] 2.0
# variance
x = sc.parallelize([1,3,2])
y = x.variance() # divides by N
print(x.collect())
print(y)
[1, 3, 2] 0.666666666667
# stdev
x = sc.parallelize([1,3,2])
y = x.stdev() # divides by N
print(x.collect())
print(y)
[1, 3, 2] 0.816496580928
# sampleStdev
x = sc.parallelize([1,3,2])
y = x.sampleStdev() # divides by N-1
print(x.collect())
print(y)
[1, 3, 2] 1.0
# sampleVariance
x = sc.parallelize([1,3,2])
y = x.sampleVariance() # divides by N-1
print(x.collect())
print(y)
[1, 3, 2] 1.0
# countByValue
x = sc.parallelize([1,3,1,2,3])
y = x.countByValue()
print(x.collect())
print(y)
[1, 3, 1, 2, 3] defaultdict(<type 'int'>, {1: 2, 2: 1, 3: 2})
# top
x = sc.parallelize([1,3,1,2,3])
y = x.top(num = 3)
print(x.collect())
print(y)
[1, 3, 1, 2, 3] [3, 3, 2]
# takeOrdered
x = sc.parallelize([1,3,1,2,3])
y = x.takeOrdered(num = 3)
print(x.collect())
print(y)
[1, 3, 1, 2, 3] [1, 1, 2]
# take
x = sc.parallelize([1,3,1,2,3])
y = x.take(num = 3)
print(x.collect())
print(y)
[1, 3, 1, 2, 3] [1, 3, 1]
# first
x = sc.parallelize([1,3,1,2,3])
y = x.first()
print(x.collect())
print(y)
[1, 3, 1, 2, 3] 1
# collectAsMap
x = sc.parallelize([('C',3),('A',1),('B',2)])
y = x.collectAsMap()
print(x.collect())
print(y)
[('C', 3), ('A', 1), ('B', 2)] {'A': 1, 'C': 3, 'B': 2}
# keys
x = sc.parallelize([('C',3),('A',1),('B',2)])
y = x.keys()
print(x.collect())
print(y.collect())
[('C', 3), ('A', 1), ('B', 2)] ['C', 'A', 'B']
# values
x = sc.parallelize([('C',3),('A',1),('B',2)])
y = x.values()
print(x.collect())
print(y.collect())
[('C', 3), ('A', 1), ('B', 2)] [3, 1, 2]
# reduceByKey
x = sc.parallelize([('B',1),('B',2),('A',3),('A',4),('A',5)])
y = x.reduceByKey(lambda agg, obj: agg + obj)
print(x.collect())
print(y.collect())
[('B', 1), ('B', 2), ('A', 3), ('A', 4), ('A', 5)] [('A', 12), ('B', 3)]
# reduceByKeyLocally
x = sc.parallelize([('B',1),('B',2),('A',3),('A',4),('A',5)])
y = x.reduceByKeyLocally(lambda agg, obj: agg + obj)
print(x.collect())
print(y)
[('B', 1), ('B', 2), ('A', 3), ('A', 4), ('A', 5)] {'A': 12, 'B': 3}
# countByKey
x = sc.parallelize([('B',1),('B',2),('A',3),('A',4),('A',5)])
y = x.countByKey()
print(x.collect())
print(y)
[('B', 1), ('B', 2), ('A', 3), ('A', 4), ('A', 5)] defaultdict(<type 'int'>, {'A': 3, 'B': 2})
# join
x = sc.parallelize([('C',4),('B',3),('A',2),('A',1)])
y = sc.parallelize([('A',8),('B',7),('A',6),('D',5)])
z = x.join(y)
print(x.collect())
print(y.collect())
print(z.collect())
[('C', 4), ('B', 3), ('A', 2), ('A', 1)] [('A', 8), ('B', 7), ('A', 6), ('D', 5)] [('A', (2, 8)), ('A', (2, 6)), ('A', (1, 8)), ('A', (1, 6)), ('B', (3, 7))]
# leftOuterJoin
x = sc.parallelize([('C',4),('B',3),('A',2),('A',1)])
y = sc.parallelize([('A',8),('B',7),('A',6),('D',5)])
z = x.leftOuterJoin(y)
print(x.collect())
print(y.collect())
print(z.collect())
[('C', 4), ('B', 3), ('A', 2), ('A', 1)] [('A', 8), ('B', 7), ('A', 6), ('D', 5)] [('A', (2, 8)), ('A', (2, 6)), ('A', (1, 8)), ('A', (1, 6)), ('C', (4, None)), ('B', (3, 7))]
# rightOuterJoin
x = sc.parallelize([('C',4),('B',3),('A',2),('A',1)])
y = sc.parallelize([('A',8),('B',7),('A',6),('D',5)])
z = x.rightOuterJoin(y)
print(x.collect())
print(y.collect())
print(z.collect())
[('C', 4), ('B', 3), ('A', 2), ('A', 1)] [('A', 8), ('B', 7), ('A', 6), ('D', 5)] [('A', (2, 8)), ('A', (2, 6)), ('A', (1, 8)), ('A', (1, 6)), ('B', (3, 7)), ('D', (None, 5))]
# partitionBy
x = sc.parallelize([(0,1),(1,2),(2,3)],2)
y = x.partitionBy(numPartitions = 3, partitionFunc = lambda x: x) # only key is passed to paritionFunc
print(x.glom().collect())
print(y.glom().collect())
[[(0, 1)], [(1, 2), (2, 3)]] [[(0, 1)], [(1, 2)], [(2, 3)]]
# combineByKey
x = sc.parallelize([('B',1),('B',2),('A',3),('A',4),('A',5)])
createCombiner = (lambda el: [(el,el**2)])
mergeVal = (lambda aggregated, el: aggregated + [(el,el**2)]) # append to aggregated
mergeComb = (lambda agg1,agg2: agg1 + agg2 ) # append agg1 with agg2
y = x.combineByKey(createCombiner,mergeVal,mergeComb)
print(x.collect())
print(y.collect())
[('B', 1), ('B', 2), ('A', 3), ('A', 4), ('A', 5)] [('A', [(3, 9), (4, 16), (5, 25)]), ('B', [(1, 1), (2, 4)])]
# aggregateByKey
x = sc.parallelize([('B',1),('B',2),('A',3),('A',4),('A',5)])
zeroValue = [] # empty list is 'zero value' for append operation
mergeVal = (lambda aggregated, el: aggregated + [(el,el**2)])
mergeComb = (lambda agg1,agg2: agg1 + agg2 )
y = x.aggregateByKey(zeroValue,mergeVal,mergeComb)
print(x.collect())
print(y.collect())
[('B', 1), ('B', 2), ('A', 3), ('A', 4), ('A', 5)] [('A', [(3, 9), (4, 16), (5, 25)]), ('B', [(1, 1), (2, 4)])]
# foldByKey
x = sc.parallelize([('B',1),('B',2),('A',3),('A',4),('A',5)])
zeroValue = 1 # one is 'zero value' for multiplication
y = x.foldByKey(zeroValue,lambda agg,x: agg*x ) # computes cumulative product within each key
print(x.collect())
print(y.collect())
[('B', 1), ('B', 2), ('A', 3), ('A', 4), ('A', 5)] [('A', 60), ('B', 2)]
# groupByKey
x = sc.parallelize([('B',5),('B',4),('A',3),('A',2),('A',1)])
y = x.groupByKey()
print(x.collect())
print([(j[0],[i for i in j[1]]) for j in y.collect()])
[('B', 5), ('B', 4), ('A', 3), ('A', 2), ('A', 1)] [('A', [3, 2, 1]), ('B', [5, 4])]
# flatMapValues
x = sc.parallelize([('A',(1,2,3)),('B',(4,5))])
y = x.flatMapValues(lambda x: [i**2 for i in x]) # function is applied to entire value, then result is flattened
print(x.collect())
print(y.collect())
[('A', (1, 2, 3)), ('B', (4, 5))] [('A', 1), ('A', 4), ('A', 9), ('B', 16), ('B', 25)]
# mapValues
x = sc.parallelize([('A',(1,2,3)),('B',(4,5))])
y = x.mapValues(lambda x: [i**2 for i in x]) # function is applied to entire value
print(x.collect())
print(y.collect())
[('A', (1, 2, 3)), ('B', (4, 5))] [('A', [1, 4, 9]), ('B', [16, 25])]
# groupWith
x = sc.parallelize([('C',4),('B',(3,3)),('A',2),('A',(1,1))])
y = sc.parallelize([('B',(7,7)),('A',6),('D',(5,5))])
z = sc.parallelize([('D',9),('B',(8,8))])
a = x.groupWith(y,z)
print(x.collect())
print(y.collect())
print(z.collect())
print("Result:")
for key,val in list(a.collect()):
print(key, [list(i) for i in val])
[('C', 4), ('B', (3, 3)), ('A', 2), ('A', (1, 1))] [('B', (7, 7)), ('A', 6), ('D', (5, 5))] [('D', 9), ('B', (8, 8))] Result: D [[], [(5, 5)], [9]] C [[4], [], []] B [[(3, 3)], [(7, 7)], [(8, 8)]] A [[2, (1, 1)], [6], []]
# cogroup
x = sc.parallelize([('C',4),('B',(3,3)),('A',2),('A',(1,1))])
y = sc.parallelize([('A',8),('B',7),('A',6),('D',(5,5))])
z = x.cogroup(y)
print(x.collect())
print(y.collect())
for key,val in list(z.collect()):
print(key, [list(i) for i in val])
[('C', 4), ('B', (3, 3)), ('A', 2), ('A', (1, 1))] [('A', 8), ('B', 7), ('A', 6), ('D', (5, 5))] A [[2, (1, 1)], [8, 6]] C [[4], []] B [[(3, 3)], [7]] D [[], [(5, 5)]]
# sampleByKey
x = sc.parallelize([('A',1),('B',2),('C',3),('B',4),('A',5)])
y = x.sampleByKey(withReplacement=False, fractions={'A':0.5, 'B':1, 'C':0.2})
print(x.collect())
print(y.collect())
[('A', 1), ('B', 2), ('C', 3), ('B', 4), ('A', 5)] [('A', 1), ('B', 2), ('B', 4)]
# subtractByKey
x = sc.parallelize([('C',1),('B',2),('A',3),('A',4)])
y = sc.parallelize([('A',5),('D',6),('A',7),('D',8)])
z = x.subtractByKey(y)
print(x.collect())
print(y.collect())
print(z.collect())
[('C', 1), ('B', 2), ('A', 3), ('A', 4)] [('A', 5), ('D', 6), ('A', 7), ('D', 8)] [('C', 1), ('B', 2)]
# subtract
x = sc.parallelize([('C',4),('B',3),('A',2),('A',1)])
y = sc.parallelize([('C',8),('A',2),('D',1)])
z = x.subtract(y)
print(x.collect())
print(y.collect())
print(z.collect())
[('C', 4), ('B', 3), ('A', 2), ('A', 1)] [('C', 8), ('A', 2), ('D', 1)] [('A', 1), ('C', 4), ('B', 3)]
# keyBy
x = sc.parallelize([1,2,3])
y = x.keyBy(lambda x: x**2)
print(x.collect())
print(y.collect())
[1, 2, 3] [(1, 1), (4, 2), (9, 3)]
# repartition
x = sc.parallelize([1,2,3,4,5],2)
y = x.repartition(numPartitions=3)
print(x.glom().collect())
print(y.glom().collect())
[[1, 2], [3, 4, 5]] [[], [1, 2, 3, 4], [5]]
# coalesce
x = sc.parallelize([1,2,3,4,5],2)
y = x.coalesce(numPartitions=1)
print(x.glom().collect())
print(y.glom().collect())
[[1, 2], [3, 4, 5]] [[1, 2, 3, 4, 5]]
# zip
x = sc.parallelize(['B','A','A'])
y = x.map(lambda x: ord(x)) # zip expects x and y to have same #partitions and #elements/partition
z = x.zip(y)
print(x.collect())
print(y.collect())
print(z.collect())
['B', 'A', 'A'] [66, 65, 65] [('B', 66), ('A', 65), ('A', 65)]
# zipWithIndex
x = sc.parallelize(['B','A','A'],2)
y = x.zipWithIndex()
print(x.glom().collect())
print(y.collect())
[['B'], ['A', 'A']] [('B', 0), ('A', 1), ('A', 2)]
# zipWithUniqueId
x = sc.parallelize(['B','A','A'],2)
y = x.zipWithUniqueId()
print(x.glom().collect())
print(y.collect())
[['B'], ['A', 'A']] [('B', 0), ('A', 1), ('A', 3)]