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pyspark-session-2018-04-12.txt
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pyspark-session-2018-04-12.txt
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$ cat /Users/mparsian/spark-2.2.1/zbin/foxdata.txt
red fox jumped high
fox jumped over high fence
red fox jumped
NOTE: I have formatted output of PySpark's collect()
for better reading/understanding.
$ ./bin/pysaprk
>>>
>>> spark
<pyspark.sql.session.SparkSession object at 0x109672a50>
>>>
>>>
>>> sc = spark.sparkContext
>>>
>>> sc
<SparkContext master=local[*] appName=PySparkShell>
>>>
>>> rdd = sc.textFile("file:///Users/mparsian/spark-2.2.1/zbin/foxdata.txt")
>>> rdd.collect()
[
u'red fox jumped high',
u'fox jumped over high fence',
u'red fox jumped'
]
>>>
>>> rdd.count()
3
>>>
>>>
>>> rdd2 = rdd.map(lambda x: (x, len(x)) )
>>>
>>> rdd2.collect()
[
(u'red fox jumped high', 19),
(u'fox jumped over high fence', 26),
(u'red fox jumped', 14)
]
>>>
>>>
>>> rdd3 = rdd2.map(lambda (k, v) : (v, k, k))
>>>
>>> rdd3.collect()
[
(19, u'red fox jumped high', u'red fox jumped high'),
(26, u'fox jumped over high fence', u'fox jumped over high fence'),
(14, u'red fox jumped', u'red fox jumped')
]
>>>
>>>
>>> rdd.collect()
[
u'red fox jumped high',
u'fox jumped over high fence',
u'red fox jumped'
]
>>>
>>>
>>> words = rdd.flatMap(lambda x: x.split(" "))
>>> words.collect()
[
u'red',
u'fox',
u'jumped',
u'high',
u'fox',
u'jumped',
u'over',
u'high',
u'fence',
u'red',
u'fox',
u'jumped'
]
>>> words.count()
12
>>>
>>>
>>> pairs = words.map(lambda x: (x, 1))
>>> pairs.collect()
[
(u'red', 1),
(u'fox', 1),
(u'jumped', 1),
(u'high', 1),
(u'fox', 1),
(u'jumped', 1),
(u'over', 1),
(u'high', 1),
(u'fence', 1),
(u'red', 1),
(u'fox', 1),
(u'jumped', 1)
]
>>>
>>>
>>>
>>> freq = pairs.reduceByKey(lambda x, y: x+y)
>>> freq.collect()
[
(u'high', 2),
(u'over', 1),
(u'fox', 3),
(u'red', 2),
(u'fence', 1),
(u'jumped', 3)
]
>>>
>>>
>>>
>>> grouped = pairs.groupByKey()
>>> grouped.collect()
[
(u'high', <pyspark.resultiterable.ResultIterable object at 0x109680250>),
(u'over', <pyspark.resultiterable.ResultIterable object at 0x1096800d0>),
(u'fox', <pyspark.resultiterable.ResultIterable object at 0x1096df1d0>),
(u'red', <pyspark.resultiterable.ResultIterable object at 0x1096df090>),
(u'fence', <pyspark.resultiterable.ResultIterable object at 0x1096df110>),
(u'jumped', <pyspark.resultiterable.ResultIterable object at 0x1096df0d0>)
]
>>>
>>> grouped.map(lambda (k,v): (k, list(v))).collect()
[
(u'high', [1, 1]),
(u'over', [1]),
(u'fox', [1, 1, 1]),
(u'red', [1, 1]),
(u'fence', [1]),
(u'jumped', [1, 1, 1])
]
>>>
>>>
>>> freqByList = grouped.map(lambda (k,v): (k, list(v)))
>>> freqByList.collect()
[
(u'high', [1, 1]),
(u'over', [1]),
(u'fox', [1, 1, 1]),
(u'red', [1, 1]),
(u'fence', [1]),
(u'jumped', [1, 1, 1])
]
>>>
>>> freq2 = freqByList.mapValues(lambda x : sum(x))
>>> freq2.collect()
[
(u'high', 2),
(u'over', 1),
(u'fox', 3),
(u'red', 2),
(u'fence', 1),
(u'jumped', 3)
]
>>>
>>> spark
<pyspark.sql.session.SparkSession object at 0x109672a50>
>>>
>>>
>>>
>>> sc = spark.sparkContext
>>>
>>> sc
<SparkContext master=local[*] appName=PySparkShell>
>>>
>>>
>>>
>>> rdd = sc.textFile("file:///Users/mparsian/spark-2.2.1/zbin/foxdata.txt")
>>> rdd.collect()
[
u'red fox jumped high',
u'fox jumped over high fence',
u'red fox jumped'
]
>>> rdd.count()
3
>>> rdd.take(2)
[
u'red fox jumped high',
u'fox jumped over high fence'
]
>>> rdd.take(1)
[u'red fox jumped high']
>>>
>>> words = rdd.flatMap(lambda x: x.split(" "))
>>> words.collect()
[
u'red',
u'fox',
u'jumped',
u'high',
u'fox',
u'jumped',
u'over',
u'high',
u'fence',
u'red',
u'fox',
u'jumped'
]
>>> words.count()
12
>>> pairs = words.map(lambda x: (x, 1))
>>> pairs.collect()
[
(u'red', 1),
(u'fox', 1),
(u'jumped', 1),
(u'high', 1),
(u'fox', 1),
(u'jumped', 1),
(u'over', 1),
(u'high', 1),
(u'fence', 1),
(u'red', 1),
(u'fox', 1),
(u'jumped', 1)
]
>>>
>>>
>>> grouped = pairs.groupByKey()
>>> grouped.collect()
[
(u'high', <pyspark.resultiterable.ResultIterable object at 0x1096ecb50>),
(u'over', <pyspark.resultiterable.ResultIterable object at 0x1096ecbd0>),
(u'fox', <pyspark.resultiterable.ResultIterable object at 0x1096f6110>),
(u'red', <pyspark.resultiterable.ResultIterable object at 0x1096f6190>),
(u'fence', <pyspark.resultiterable.ResultIterable object at 0x1096f61d0>),
(u'jumped', <pyspark.resultiterable.ResultIterable object at 0x1096ecf50>)
]
>>>
>>> groupedList = grouped.map(lambda (k,v): (k, list(v)))
>>> groupedList.collect()
[
(u'high', [1, 1]),
(u'over', [1]),
(u'fox', [1, 1, 1]),
(u'red', [1, 1]),
(u'fence', [1]),
(u'jumped', [1, 1, 1])
]
>>>
>>> freq = groupedList.mapValues(lambda x : sum(x))
>>> freq.collect()
[
(u'high', 2),
(u'over', 1),
(u'fox', 3),
(u'red', 2),
(u'fence', 1),
(u'jumped', 3)
]
>>>
>>>
>>> pairs.collect()
[
(u'red', 1),
(u'fox', 1),
(u'jumped', 1),
(u'high', 1),
(u'fox', 1),
(u'jumped', 1),
(u'over', 1),
(u'high', 1),
(u'fence', 1),
(u'red', 1),
(u'fox', 1),
(u'jumped', 1)
]
>>> freq22 = pairs.reduceByKey(lambda x, y : x+y)
>>> freq22.collect()
[
(u'high', 2),
(u'over', 1),
(u'fox', 3),
(u'red', 2),
(u'fence', 1),
(u'jumped', 3)
]
>>>
>>>
>>> myrdd = freq22.map(lambda (k,v): (k, (v,v)))
>>> myrdd.collect()
[
(u'high', (2, 2)),
(u'over', (1, 1)),
(u'fox', (3, 3)),
(u'red', (2, 2)),
(u'fence', (1, 1)),
(u'jumped', (3, 3))
]
>>> myrdd2 = myrdd.map(lambda (k, (v1,v2)): (k, v1+v2))
>>> myrdd2.collect()
[
(u'high', 4),
(u'over', 2),
(u'fox', 6),
(u'red', 4),
(u'fence', 2),
(u'jumped', 6)
]
>>> myrdd2 = myrdd.map(lambda (k, (v1,v2)): (k, v1+v2+100))
>>> myrdd2.collect()
[
(u'high', 104),
(u'over', 102),
(u'fox', 106),
(u'red', 104),
(u'fence', 102),
(u'jumped', 106)
]
>>> grouped.collect()
[
(u'high', <pyspark.resultiterable.ResultIterable object at 0x1096d4fd0>),
(u'over', <pyspark.resultiterable.ResultIterable object at 0x109672f90>),
(u'fox', <pyspark.resultiterable.ResultIterable object at 0x1096cae50>),
(u'red', <pyspark.resultiterable.ResultIterable object at 0x1096cac50>),
(u'fence', <pyspark.resultiterable.ResultIterable object at 0x1096ca1d0>),
(u'jumped', <pyspark.resultiterable.ResultIterable object at 0x1096caed0>)
]
>>> sumitup = grouped.mapValues(lambda i : sum(i))
>>> sumitup.collect()
[
(u'high', 2),
(u'over', 1),
(u'fox', 3),
(u'red', 2),
(u'fence', 1),
(u'jumped', 3)
]
>>>
>>>
>>>
>>> def myfunc(x):
... y = x + 100
... return y
...
>>> z = myfunc(21)
>>> z
121
>>> rdd55 = sumitup.map(lambda (k,v): (k, myfunc(v)))
>>> rdd55.collect()
[
(u'high', 102),
(u'over', 101),
(u'fox', 103),
(u'red', 102),
(u'fence', 101),
(u'jumped', 103)
]
>>> rdd55.lookup('high')
[102]
>>> rdd55.lookup('fox')
[103]
>>> rdd55.lookup('foxy')
[]