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pyspark-session-2021-10-06.txt
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pyspark-session-2021-10-06.txt
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The files I used in class:
$ ls -l /tmp/scu/
total 24
-rw-r--r-- 1 mparsian dev 52 Oct 6 20:18 file1
-rw-r--r-- 1 mparsian dev 102 Oct 6 20:18 file2
-rw-r--r-- 1 mparsian dev 84 Oct 6 20:20 file3
$ cat /tmp/scu/file1
a red fox jumped
a fox jumped and jumped
a red fox
$ cat /tmp/scu/file2
a red fox jumped a red fox jumped
a fox jumped and jumped a fox jumped and jumped
a red fox a red fox
$ cat /tmp/scu/file3
red red fox jumped
fox fox jumped and jumped
fox a red fox
a red fox of fox of fox
$ ./bin/pyspark
Python 3.7.2 (v3.7.2:9a3ffc0492, Dec 24 2018, 02:44:43)
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__ / .__/\_,_/_/ /_/\_\ version 3.1.2
/_/
Using Python version 3.7.2 (v3.7.2:9a3ffc0492, Dec 24 2018 02:44:43)
Spark context Web UI available at http://10.0.0.94:4040
Spark context available as 'sc' (master = local[*], app id = local-1633406278060).
SparkSession available as 'spark'.
>>>
>>>
>>> sc
<SparkContext master=local[*] appName=PySparkShell>
>>> spark
<pyspark.sql.session.SparkSession object at 0x7fb56018b518>
>>>
>>>
>>> numbers = [("A", 1), ("A", 4), ("A", 7), ("B", 10), ("B", 20), ("B", 30), ("B", 40)]
>>> numbers
[('A', 1), ('A', 4), ('A', 7), ('B', 10), ('B', 20), ('B', 30), ('B', 40)]
>>> numbers[0]
('A', 1)
>>> numbers[1]
('A', 4)
>>>
>>>
>>>
>>> rdd = sc.parallelize(numbers)
>>> rdd.collect()
[('A', 1), ('A', 4), ('A', 7), ('B', 10), ('B', 20), ('B', 30), ('B', 40)]
>>>
>>>
>>> rdd.count()
7
>>> sum_per_key = rdd.reduceByKey(lambda x, y: x+y)
>>> sum_per_key.collect()
[('B', 100), ('A', 12)]
>>> sum_per_key.count()
2
>>>
>>>
>>>
>>> file3_path = "/tmp/scu/file3"
>>> rdd3 = sc.textFile(file3_path)
>>>
>>> rdd3.collect()
['red red fox jumped', 'fox fox jumped and jumped', 'fox a red fox ', 'a red fox of fox of fox']
>>>
>>>
>>>
>>> scu_path = "/tmp/scu"
>>> rdd = sc.textFile(scu_path)
>>>
>>>
>>> rdd.collect()
['red red fox jumped', 'fox fox jumped and jumped', 'fox a red fox ', 'a red fox of fox of fox', 'a red fox jumped a red fox jumped', 'a fox jumped and jumped a fox jumped and jumped', 'a red fox a red fox', 'a red fox jumped', 'a fox jumped and jumped', 'a red fox ']
>>> rdd.count()
10
>>> rdd3.count()
4
>>>
>>> key_values = [("A", 10), ("A", 20), ("B", 30), ("B", 40), ("B", 50)]
>>> key_values
[('A', 10), ('A', 20), ('B', 30), ('B', 40), ('B', 50)]
>>> kv_rdd = sc.parallelize(key_values)
>>> kv_rdd.collect()
[('A', 10), ('A', 20), ('B', 30), ('B', 40), ('B', 50)]
>>> key_values2 = [("A", (1, 10)), ("A", (2, 20)), ("B", (3, 30)), ("B", (4, 40)), ("B", (10, 50))]
>>> kv_rdd2 = sc.parallelize(key_values2)
>>>
>>> kv_rdd2.collect()
[('A', (1, 10)), ('A', (2, 20)), ('B', (3, 30)), ('B', (4, 40)), ('B', (10, 50))]
>>>
>>>
>>>
>>> rdd.collect()
[
'red red fox jumped',
'fox fox jumped and jumped',
'fox a red fox ',
'a red fox of fox of fox',
'a red fox jumped a red fox jumped',
'a fox jumped and jumped a fox jumped and jumped',
'a red fox a red fox',
'a red fox jumped',
'a fox jumped and jumped',
'a red fox '
]
>>> rdd.count()
10
>>> rdd.take(3)
[
'red red fox jumped',
'fox fox jumped and jumped',
'fox a red fox '
]
>>> rdd.take(5)
[
'red red fox jumped',
'fox fox jumped and jumped',
'fox a red fox ',
'a red fox of fox of fox',
'a red fox jumped a red fox jumped'
]
>>> tokenized = rdd.map(lambda x: x.split(" "))
>>> tokenized.collect()
[
['red', 'red', 'fox', 'jumped'],
['fox', 'fox', 'jumped', 'and', 'jumped'],
['fox', 'a', 'red', 'fox', ''],
['a', 'red', 'fox', 'of', 'fox', 'of', 'fox'],
['a', 'red', 'fox', 'jumped', 'a', 'red', 'fox', 'jumped'],
['a', 'fox', 'jumped', 'and', 'jumped', 'a', 'fox', 'jumped', 'and', 'jumped'],
['a', 'red', 'fox', 'a', 'red', 'fox'],
['a', 'red', 'fox', 'jumped'],
['a', 'fox', 'jumped', 'and', 'jumped'],
['a', 'red', 'fox', '']
]
>>> tokenized
PythonRDD[12] at collect at <stdin>:1
>>>
...
>>> words = tokenized.flatMap(lambda x: x)
>>>
>>> wrds.collect()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'wrds' is not defined
>>> words.collect()
['red', 'red', 'fox', 'jumped', 'fox', 'fox', 'jumped', 'and', 'jumped', 'fox', 'a', 'red', 'fox', '', 'a', 'red', 'fox', 'of', 'fox', 'of', 'fox', 'a', 'red', 'fox', 'jumped', 'a', 'red', 'fox', 'jumped', 'a', 'fox', 'jumped', 'and', 'jumped', 'a', 'fox', 'jumped', 'and', 'jumped', 'a', 'red', 'fox', 'a', 'red', 'fox', 'a', 'red', 'fox', 'jumped', 'a', 'fox', 'jumped', 'and', 'jumped', 'a', 'red', 'fox', '']
>>> words.count()
58
>>> pairs = words.map(lambda word: (word, 1))
>>> pairs.collect()
[('red', 1), ('red', 1), ('fox', 1), ('jumped', 1), ('fox', 1), ('fox', 1), ('jumped', 1), ('and', 1), ('jumped', 1), ('fox', 1), ('a', 1), ('red', 1), ('fox', 1), ('', 1), ('a', 1), ('red', 1), ('fox', 1), ('of', 1), ('fox', 1), ('of', 1), ('fox', 1), ('a', 1), ('red', 1), ('fox', 1), ('jumped', 1), ('a', 1), ('red', 1), ('fox', 1), ('jumped', 1), ('a', 1), ('fox', 1), ('jumped', 1), ('and', 1), ('jumped', 1), ('a', 1), ('fox', 1), ('jumped', 1), ('and', 1), ('jumped', 1), ('a', 1), ('red', 1), ('fox', 1), ('a', 1), ('red', 1), ('fox', 1), ('a', 1), ('red', 1), ('fox', 1), ('jumped', 1), ('a', 1), ('fox', 1), ('jumped', 1), ('and', 1), ('jumped', 1), ('a', 1), ('red', 1), ('fox', 1), ('', 1)]
>>>
>>> freq = pairs.reduceByKey(lambda x, y: x+y)
>>> freq.collect()
[('fox', 17), ('', 2), ('of', 2), ('and', 4), ('red', 10), ('jumped', 12), ('a', 11)]
>>>
>>>
>>>
>>> groupedbykey = pairs.groupByKey()
>>> groupedbykey.collect()
[
('fox', <pyspark.resultiterable.ResultIterable object at 0x7f990f1fd0b8>),
('', <pyspark.resultiterable.ResultIterable object at 0x7f990f1fd518>),
('of', <pyspark.resultiterable.ResultIterable object at 0x7f990f1fd470>),
('and', <pyspark.resultiterable.ResultIterable object at 0x7f990f1fd080>),
('red', <pyspark.resultiterable.ResultIterable object at 0x7f990f1fd390>),
('jumped', <pyspark.resultiterable.ResultIterable object at 0x7f990f1fd358>),
('a', <pyspark.resultiterable.ResultIterable object at 0x7f990f1fd2b0>)
]
>>>
>>> groupedbykey.mapValues(lambda iter: list(iter)).collect()
[
('fox', [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
('', [1, 1]),
('of', [1, 1]),
('and', [1, 1, 1, 1]),
('red', [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
('jumped', [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
('a', [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
]
>>>
>>> freq_by_grouping = groupedbykey.mapValues(lambda iter: sum(iter))
>>> freq_by_grouping.collect()
[('fox', 17), ('', 2), ('of', 2), ('and', 4), ('red', 10), ('jumped', 12), ('a', 11)]
>>> # drop all (K, V) if len(K) is < 2
...
>>> filtered = freq_by_grouping.filter(lambda x: len(x[0]) >= 2)
>>> filtered.collect()
[('fox', 17), ('of', 2), ('and', 4), ('red', 10), ('jumped', 12)]
>>>
>>>
>>> # keep (K,V) if V > 5
...
>>> filtered_by_value = filtered.filter(lambda x: x[1] > 5)
>>> filtered_by_value.collect()
[('fox', 17), ('red', 10), ('jumped', 12)]
>>>