Re: Using Lambda function to generate random data in PySpark throws not defined error
Hey Mich
glad to know u got to the bottom
In python, if you want to run a module - same as if you would use
Java/Scala -you will have to define a def main() method
You'll notice that the snippet i sent you had this syntax -
if __name__ == "main":
main()
I am guessing you just choose an unfortunate name for your class. Had you
called it
class pincopallino:
.
Your IDE would not have called it because it could not find a main method,
and then you would have been on the right track
I am guessing your main() class somehow confused your IDE.
The best way to run your spark code would be via a unit test though the
code below might give you a head start - (you'll need to configure your IDE
for this though..)
have fun
kr
marco
import logging
from pyspark.sql import SparkSession
from pyspark import HiveContext
from pyspark import SparkConf
from pyspark import SparkContext
import pyspark
from pyspark.sql import SparkSession
import pytest
import shutil
def spark_session():
return SparkSession.builder \
.master('local[1]') \
.appName('SparkByExamples.com') \
.getOrCreate()
def test_create_table(spark_session):
df = spark_session.createDataFrame([['one',
'two']]).toDF(*['first', 'second'])
print(df.show())
df2 = spark_session.createDataFrame([['one',
'two']]).toDF(*['first', 'second'])
df.createOrReplaceTempView('sample')
assert df.subtract(df2).count() == 0
On Sun, Dec 13, 2020 at 8:43 PM Mich Talebzadeh
wrote:
>
> Thanks all.
>
> Found out the problem :(
>
> I defined the runner.py as
>
> class main()
>
> I replaced it with
>
> def main():
>
> and it worked without declaring numRows as global.
>
> I am still wondering the reason for it working with def main()?
>
>
> regards,
>
> Mich
>
>
> *Disclaimer:* Use it at your own risk. Any and all responsibility for any
> loss, damage or destruction of data or any other property which may arise
> from relying on this email's technical content is explicitly disclaimed.
> The author will in no case be liable for any monetary damages arising from
> such loss, damage or destruction.
>
>
>
>
> On Sun, 13 Dec 2020 at 15:10, Sean Owen wrote:
>
>> I don't believe you'll be able to use globals in a Spark task, as they
>> won't exist on the remote executor machines.
>>
>> On Sun, Dec 13, 2020 at 3:46 AM Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>> thanks Marco.
>>>
>>> When I stripped down spark etc and ran your map, it came back OK (no
>>> errors) WITHOUT global numRows
>>>
>>> However, with full code, this is the unresolved reference notification I
>>> am getting as attached embedded your code WITHOUT global numRows
>>>
>>> regards,
>>>
>>>
>>> Mich
>>>
>>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>>> any loss, damage or destruction of data or any other property which may
>>> arise from relying on this email's technical content is explicitly
>>> disclaimed. The author will in no case be liable for any monetary damages
>>> arising from such loss, damage or destruction.
>>>
>>>
>>>
>>>
>>> On Sat, 12 Dec 2020 at 21:48, Sofia’s World wrote:
>>>
Hi Mich
i dont think it's a good idea... I believe your IDE is playing tricks
on you.
Take spark out of the equation this is a python issue only.
i am guessing your IDE is somehow messing up your environment.
if you take out the whole spark code and replace it by this code
map(lambda x: (x, uf.clustered(x,numRows), \
uf.scattered(x,numRows), \
uf.randomised(x, numRows), \
uf.randomString(50), \
uf.padString(x," ",50), \
uf.padSingleChar("x",4000)), [1,2,3,4,5])
you should get exactly the same error...
Send me a zip with the tfconstants,py and a trimmed donw version of
your main,py and i'll plug it in my IDE and see if i can reproduce
It worked fine in Jupyter, but then i have all functins in same
notebook
hth
marco
On Sat, Dec 12, 2020 at 9:02 PM Mich Talebzadeh <
mich.talebza...@gmail.com> wrote:
> I solved the issue of variable numRows within the lambda function not
> defined by defining it as a Global variable
>
> global numRows
> numRows = 10 ## do in increment of 50K rows otherwise you blow up
> driver memory!
> #
>
> Then I could call it within the lambda function as follows
>
>
> rdd = sc.parallelize(Range). \
> map(lambda x: (x, uf.clustered(x,numRows), \
>uf.scattered(x,numRows), \
>uf.randomised(x, numRows), \
>uf.randomString(50), \
>uf.padString(x," ",50), \
Re: Using Lambda function to generate random data in PySpark throws not defined error
Thanks all.
Found out the problem :(
I defined the runner.py as
class main()
I replaced it with
def main():
and it worked without declaring numRows as global.
I am still wondering the reason for it working with def main()?
regards,
Mich
*Disclaimer:* Use it at your own risk. Any and all responsibility for any
loss, damage or destruction of data or any other property which may arise
from relying on this email's technical content is explicitly disclaimed.
The author will in no case be liable for any monetary damages arising from
such loss, damage or destruction.
On Sun, 13 Dec 2020 at 15:10, Sean Owen wrote:
> I don't believe you'll be able to use globals in a Spark task, as they
> won't exist on the remote executor machines.
>
> On Sun, Dec 13, 2020 at 3:46 AM Mich Talebzadeh
> wrote:
>
>> thanks Marco.
>>
>> When I stripped down spark etc and ran your map, it came back OK (no
>> errors) WITHOUT global numRows
>>
>> However, with full code, this is the unresolved reference notification I
>> am getting as attached embedded your code WITHOUT global numRows
>>
>> regards,
>>
>>
>> Mich
>>
>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>> any loss, damage or destruction of data or any other property which may
>> arise from relying on this email's technical content is explicitly
>> disclaimed. The author will in no case be liable for any monetary damages
>> arising from such loss, damage or destruction.
>>
>>
>>
>>
>> On Sat, 12 Dec 2020 at 21:48, Sofia’s World wrote:
>>
>>> Hi Mich
>>> i dont think it's a good idea... I believe your IDE is playing tricks
>>> on you.
>>> Take spark out of the equation this is a python issue only.
>>> i am guessing your IDE is somehow messing up your environment.
>>>
>>> if you take out the whole spark code and replace it by this code
>>>
>>> map(lambda x: (x, uf.clustered(x,numRows), \
>>>uf.scattered(x,numRows), \
>>>uf.randomised(x, numRows), \
>>>uf.randomString(50), \
>>>uf.padString(x," ",50), \
>>>uf.padSingleChar("x",4000)), [1,2,3,4,5])
>>>
>>> you should get exactly the same error...
>>>
>>> Send me a zip with the tfconstants,py and a trimmed donw version of your
>>> main,py and i'll plug it in my IDE and see if i can reproduce
>>> It worked fine in Jupyter, but then i have all functins in same notebook
>>> hth
>>> marco
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> On Sat, Dec 12, 2020 at 9:02 PM Mich Talebzadeh <
>>> mich.talebza...@gmail.com> wrote:
>>>
I solved the issue of variable numRows within the lambda function not
defined by defining it as a Global variable
global numRows
numRows = 10 ## do in increment of 50K rows otherwise you blow up driver
memory!
#
Then I could call it within the lambda function as follows
rdd = sc.parallelize(Range). \
map(lambda x: (x, uf.clustered(x,numRows), \
uf.scattered(x,numRows), \
uf.randomised(x, numRows), \
uf.randomString(50), \
uf.padString(x," ",50), \
uf.padSingleChar("x",4000)))
This then worked. I am not convinced this is *the correct* solution but
somehow it worked.
Thanks
Mich
*Disclaimer:* Use it at your own risk. Any and all responsibility for
any loss, damage or destruction of data or any other property which may
arise from relying on this email's technical content is explicitly
disclaimed. The author will in no case be liable for any monetary damages
arising from such loss, damage or destruction.
On Fri, 11 Dec 2020 at 18:52, Mich Talebzadeh <
mich.talebza...@gmail.com> wrote:
> many thanks KR.
>
> If i call the clusterted function on its own it works
>
> numRows = 10
>
> print(uf.clustered(200,numRows))
>
> and returns
>
> 0.00199
> If I run all in one including the UsedFunctions claa in the same py
> file it works. The code is attached
>
> However, in PyCharm, I do the following
>
> UsedFunctions.py. Note that this file only contains functions and no
> class
>
> import logging
> import random
> import string
> import math
>
> def randomString(length):
> letters = string.ascii_letters
> result_str = ''.join(random.choice(letters) for i in range(length))
> return result_str
>
> def clustered(x,numRows):
> return math.floor(x -1)/numRows
>
> def scattered(x,numRows):
> return abs((x -1 % numRows))* 1.0
>
> def randomised(seed,numRows):
> random.seed(seed)
> return abs(random.r
Re: Using Lambda function to generate random data in PySpark throws not defined error
I don't believe you'll be able to use globals in a Spark task, as they
won't exist on the remote executor machines.
On Sun, Dec 13, 2020 at 3:46 AM Mich Talebzadeh
wrote:
> thanks Marco.
>
> When I stripped down spark etc and ran your map, it came back OK (no
> errors) WITHOUT global numRows
>
> However, with full code, this is the unresolved reference notification I
> am getting as attached embedded your code WITHOUT global numRows
>
> regards,
>
>
> Mich
>
> *Disclaimer:* Use it at your own risk. Any and all responsibility for any
> loss, damage or destruction of data or any other property which may arise
> from relying on this email's technical content is explicitly disclaimed.
> The author will in no case be liable for any monetary damages arising from
> such loss, damage or destruction.
>
>
>
>
> On Sat, 12 Dec 2020 at 21:48, Sofia’s World wrote:
>
>> Hi Mich
>> i dont think it's a good idea... I believe your IDE is playing tricks
>> on you.
>> Take spark out of the equation this is a python issue only.
>> i am guessing your IDE is somehow messing up your environment.
>>
>> if you take out the whole spark code and replace it by this code
>>
>> map(lambda x: (x, uf.clustered(x,numRows), \
>>uf.scattered(x,numRows), \
>>uf.randomised(x, numRows), \
>>uf.randomString(50), \
>>uf.padString(x," ",50), \
>>uf.padSingleChar("x",4000)), [1,2,3,4,5])
>>
>> you should get exactly the same error...
>>
>> Send me a zip with the tfconstants,py and a trimmed donw version of your
>> main,py and i'll plug it in my IDE and see if i can reproduce
>> It worked fine in Jupyter, but then i have all functins in same notebook
>> hth
>> marco
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> On Sat, Dec 12, 2020 at 9:02 PM Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>> I solved the issue of variable numRows within the lambda function not
>>> defined by defining it as a Global variable
>>>
>>> global numRows
>>> numRows = 10 ## do in increment of 50K rows otherwise you blow up driver
>>> memory!
>>> #
>>>
>>> Then I could call it within the lambda function as follows
>>>
>>>
>>> rdd = sc.parallelize(Range). \
>>> map(lambda x: (x, uf.clustered(x,numRows), \
>>>uf.scattered(x,numRows), \
>>>uf.randomised(x, numRows), \
>>>uf.randomString(50), \
>>>uf.padString(x," ",50), \
>>>uf.padSingleChar("x",4000)))
>>>
>>> This then worked. I am not convinced this is *the correct* solution but
>>> somehow it worked.
>>>
>>>
>>> Thanks
>>>
>>>
>>> Mich
>>>
>>>
>>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>>> any loss, damage or destruction of data or any other property which may
>>> arise from relying on this email's technical content is explicitly
>>> disclaimed. The author will in no case be liable for any monetary damages
>>> arising from such loss, damage or destruction.
>>>
>>>
>>>
>>>
>>> On Fri, 11 Dec 2020 at 18:52, Mich Talebzadeh
>>> wrote:
>>>
many thanks KR.
If i call the clusterted function on its own it works
numRows = 10
print(uf.clustered(200,numRows))
and returns
0.00199
If I run all in one including the UsedFunctions claa in the same py
file it works. The code is attached
However, in PyCharm, I do the following
UsedFunctions.py. Note that this file only contains functions and no
class
import logging
import random
import string
import math
def randomString(length):
letters = string.ascii_letters
result_str = ''.join(random.choice(letters) for i in range(length))
return result_str
def clustered(x,numRows):
return math.floor(x -1)/numRows
def scattered(x,numRows):
return abs((x -1 % numRows))* 1.0
def randomised(seed,numRows):
random.seed(seed)
return abs(random.randint(0, numRows) % numRows) * 1.0
def padString(x,chars,length):
n = int(math.log10(x) + 1)
result_str = ''.join(random.choice(chars) for i in
range(length-n)) + str(x)
return result_str
def padSingleChar(chars,length):
result_str = ''.join(chars for i in range(length))
return result_str
def println(lst):
for ll in lst:
print(ll[0])
In the main.py(PyCharm) I have this code which is failing
from pyspark import SparkContext, SparkConf
from pyspark.sql import SQLContext
from pyspark.sql import HiveContext
from pyspark.sql import SparkSession
from pyspark.sql import Row
from pyspark.sql.types import StringType, ArrayType
from p
Re: Using Lambda function to generate random data in PySpark throws not defined error
Sure Mich...uhm...let me try to run your code in my IDE. .. I m intrigued
by the error..
Will report back either if I find something or not.
Kind regards
On Sun, Dec 13, 2020, 9:46 AM Mich Talebzadeh
wrote:
> thanks Marco.
>
> When I stripped down spark etc and ran your map, it came back OK (no
> errors) WITHOUT global numRows
>
> However, with full code, this is the unresolved reference notification I
> am getting as attached embedded your code WITHOUT global numRows
>
> regards,
>
>
> Mich
>
> *Disclaimer:* Use it at your own risk. Any and all responsibility for any
> loss, damage or destruction of data or any other property which may arise
> from relying on this email's technical content is explicitly disclaimed.
> The author will in no case be liable for any monetary damages arising from
> such loss, damage or destruction.
>
>
>
>
> On Sat, 12 Dec 2020 at 21:48, Sofia’s World wrote:
>
>> Hi Mich
>> i dont think it's a good idea... I believe your IDE is playing tricks
>> on you.
>> Take spark out of the equation this is a python issue only.
>> i am guessing your IDE is somehow messing up your environment.
>>
>> if you take out the whole spark code and replace it by this code
>>
>> map(lambda x: (x, uf.clustered(x,numRows), \
>>uf.scattered(x,numRows), \
>>uf.randomised(x, numRows), \
>>uf.randomString(50), \
>>uf.padString(x," ",50), \
>>uf.padSingleChar("x",4000)), [1,2,3,4,5])
>>
>> you should get exactly the same error...
>>
>> Send me a zip with the tfconstants,py and a trimmed donw version of your
>> main,py and i'll plug it in my IDE and see if i can reproduce
>> It worked fine in Jupyter, but then i have all functins in same notebook
>> hth
>> marco
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> On Sat, Dec 12, 2020 at 9:02 PM Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>> I solved the issue of variable numRows within the lambda function not
>>> defined by defining it as a Global variable
>>>
>>> global numRows
>>> numRows = 10 ## do in increment of 50K rows otherwise you blow up driver
>>> memory!
>>> #
>>>
>>> Then I could call it within the lambda function as follows
>>>
>>>
>>> rdd = sc.parallelize(Range). \
>>> map(lambda x: (x, uf.clustered(x,numRows), \
>>>uf.scattered(x,numRows), \
>>>uf.randomised(x, numRows), \
>>>uf.randomString(50), \
>>>uf.padString(x," ",50), \
>>>uf.padSingleChar("x",4000)))
>>>
>>> This then worked. I am not convinced this is *the correct* solution but
>>> somehow it worked.
>>>
>>>
>>> Thanks
>>>
>>>
>>> Mich
>>>
>>>
>>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>>> any loss, damage or destruction of data or any other property which may
>>> arise from relying on this email's technical content is explicitly
>>> disclaimed. The author will in no case be liable for any monetary damages
>>> arising from such loss, damage or destruction.
>>>
>>>
>>>
>>>
>>> On Fri, 11 Dec 2020 at 18:52, Mich Talebzadeh
>>> wrote:
>>>
many thanks KR.
If i call the clusterted function on its own it works
numRows = 10
print(uf.clustered(200,numRows))
and returns
0.00199
If I run all in one including the UsedFunctions claa in the same py
file it works. The code is attached
However, in PyCharm, I do the following
UsedFunctions.py. Note that this file only contains functions and no
class
import logging
import random
import string
import math
def randomString(length):
letters = string.ascii_letters
result_str = ''.join(random.choice(letters) for i in range(length))
return result_str
def clustered(x,numRows):
return math.floor(x -1)/numRows
def scattered(x,numRows):
return abs((x -1 % numRows))* 1.0
def randomised(seed,numRows):
random.seed(seed)
return abs(random.randint(0, numRows) % numRows) * 1.0
def padString(x,chars,length):
n = int(math.log10(x) + 1)
result_str = ''.join(random.choice(chars) for i in
range(length-n)) + str(x)
return result_str
def padSingleChar(chars,length):
result_str = ''.join(chars for i in range(length))
return result_str
def println(lst):
for ll in lst:
print(ll[0])
In the main.py(PyCharm) I have this code which is failing
from pyspark import SparkContext, SparkConf
from pyspark.sql import SQLContext
from pyspark.sql import HiveContext
from pyspark.sql import SparkSession
from pyspark.sql import Row
from pyspark.sql.types import St
Re: Using Lambda function to generate random data in PySpark throws not defined error
Hi Mich
i dont think it's a good idea... I believe your IDE is playing tricks on
you.
Take spark out of the equation this is a python issue only.
i am guessing your IDE is somehow messing up your environment.
if you take out the whole spark code and replace it by this code
map(lambda x: (x, uf.clustered(x,numRows), \
uf.scattered(x,numRows), \
uf.randomised(x, numRows), \
uf.randomString(50), \
uf.padString(x," ",50), \
uf.padSingleChar("x",4000)), [1,2,3,4,5])
you should get exactly the same error...
Send me a zip with the tfconstants,py and a trimmed donw version of your
main,py and i'll plug it in my IDE and see if i can reproduce
It worked fine in Jupyter, but then i have all functins in same notebook
hth
marco
On Sat, Dec 12, 2020 at 9:02 PM Mich Talebzadeh
wrote:
> I solved the issue of variable numRows within the lambda function not
> defined by defining it as a Global variable
>
> global numRows
> numRows = 10 ## do in increment of 50K rows otherwise you blow up driver
> memory!
> #
>
> Then I could call it within the lambda function as follows
>
>
> rdd = sc.parallelize(Range). \
> map(lambda x: (x, uf.clustered(x,numRows), \
>uf.scattered(x,numRows), \
>uf.randomised(x, numRows), \
>uf.randomString(50), \
>uf.padString(x," ",50), \
>uf.padSingleChar("x",4000)))
>
> This then worked. I am not convinced this is *the correct* solution but
> somehow it worked.
>
>
> Thanks
>
>
> Mich
>
>
> *Disclaimer:* Use it at your own risk. Any and all responsibility for any
> loss, damage or destruction of data or any other property which may arise
> from relying on this email's technical content is explicitly disclaimed.
> The author will in no case be liable for any monetary damages arising from
> such loss, damage or destruction.
>
>
>
>
> On Fri, 11 Dec 2020 at 18:52, Mich Talebzadeh
> wrote:
>
>> many thanks KR.
>>
>> If i call the clusterted function on its own it works
>>
>> numRows = 10
>>
>> print(uf.clustered(200,numRows))
>>
>> and returns
>>
>> 0.00199
>> If I run all in one including the UsedFunctions claa in the same py file
>> it works. The code is attached
>>
>> However, in PyCharm, I do the following
>>
>> UsedFunctions.py. Note that this file only contains functions and no class
>>
>> import logging
>> import random
>> import string
>> import math
>>
>> def randomString(length):
>> letters = string.ascii_letters
>> result_str = ''.join(random.choice(letters) for i in range(length))
>> return result_str
>>
>> def clustered(x,numRows):
>> return math.floor(x -1)/numRows
>>
>> def scattered(x,numRows):
>> return abs((x -1 % numRows))* 1.0
>>
>> def randomised(seed,numRows):
>> random.seed(seed)
>> return abs(random.randint(0, numRows) % numRows) * 1.0
>>
>> def padString(x,chars,length):
>> n = int(math.log10(x) + 1)
>> result_str = ''.join(random.choice(chars) for i in range(length-n))
>> + str(x)
>> return result_str
>>
>> def padSingleChar(chars,length):
>> result_str = ''.join(chars for i in range(length))
>> return result_str
>>
>> def println(lst):
>> for ll in lst:
>> print(ll[0])
>>
>> In the main.py(PyCharm) I have this code which is failing
>>
>> from pyspark import SparkContext, SparkConf
>>
>> from pyspark.sql import SQLContext
>>
>> from pyspark.sql import HiveContext
>>
>> from pyspark.sql import SparkSession
>>
>> from pyspark.sql import Row
>>
>> from pyspark.sql.types import StringType, ArrayType
>>
>> from pyspark.sql.functions import udf, col, max as max, to_date,
>> date_add, \
>>
>> add_months
>>
>> from datetime import datetime, timedelta
>>
>> import os
>>
>> from os.path import join, abspath
>>
>> from typing import Optional
>>
>> import logging
>>
>> import random
>>
>> import string
>>
>> import math
>>
>> import mathOperations as mo
>>
>> import UsedFunctions as uf
>>
>> ##import test_oracle as to
>>
>>
>> class main:
>>
>> rec = {}
>>
>> settings = [
>>
>> ("hive.exec.dynamic.partition", "true"),
>>
>> ("hive.exec.dynamic.partition.mode", "nonstrict"),
>>
>> ("spark.sql.orc.filterPushdown", "true"),
>>
>> ("hive.msck.path.validation", "ignore"),
>>
>> ("spark.sql.caseSensitive", "true"),
>>
>> ("spark.speculation", "false"),
>>
>> ("hive.metastore.authorization.storage.checks", "false"),
>>
>> ("hive.metastore.client.connect.retry.delay", "5s"),
>>
>> ("hive.metastore.client.socket.timeout", "1800s"),
>>
>> ("hive.metastore.connect.retries", "12"),
>>
>> ("hive.metastore.execute.setugi", "false"),
>>
>>
Re: Using Lambda function to generate random data in PySpark throws not defined error
I solved the issue of variable numRows within the lambda function not
defined by defining it as a Global variable
global numRows
numRows = 10 ## do in increment of 50K rows otherwise you blow up
driver memory!
#
Then I could call it within the lambda function as follows
rdd = sc.parallelize(Range). \
map(lambda x: (x, uf.clustered(x,numRows), \
uf.scattered(x,numRows), \
uf.randomised(x, numRows), \
uf.randomString(50), \
uf.padString(x," ",50), \
uf.padSingleChar("x",4000)))
This then worked. I am not convinced this is *the correct* solution but
somehow it worked.
Thanks
Mich
*Disclaimer:* Use it at your own risk. Any and all responsibility for any
loss, damage or destruction of data or any other property which may arise
from relying on this email's technical content is explicitly disclaimed.
The author will in no case be liable for any monetary damages arising from
such loss, damage or destruction.
On Fri, 11 Dec 2020 at 18:52, Mich Talebzadeh
wrote:
> many thanks KR.
>
> If i call the clusterted function on its own it works
>
> numRows = 10
>
> print(uf.clustered(200,numRows))
>
> and returns
>
> 0.00199
> If I run all in one including the UsedFunctions claa in the same py file
> it works. The code is attached
>
> However, in PyCharm, I do the following
>
> UsedFunctions.py. Note that this file only contains functions and no class
>
> import logging
> import random
> import string
> import math
>
> def randomString(length):
> letters = string.ascii_letters
> result_str = ''.join(random.choice(letters) for i in range(length))
> return result_str
>
> def clustered(x,numRows):
> return math.floor(x -1)/numRows
>
> def scattered(x,numRows):
> return abs((x -1 % numRows))* 1.0
>
> def randomised(seed,numRows):
> random.seed(seed)
> return abs(random.randint(0, numRows) % numRows) * 1.0
>
> def padString(x,chars,length):
> n = int(math.log10(x) + 1)
> result_str = ''.join(random.choice(chars) for i in range(length-n)) +
> str(x)
> return result_str
>
> def padSingleChar(chars,length):
> result_str = ''.join(chars for i in range(length))
> return result_str
>
> def println(lst):
> for ll in lst:
> print(ll[0])
>
> In the main.py(PyCharm) I have this code which is failing
>
> from pyspark import SparkContext, SparkConf
>
> from pyspark.sql import SQLContext
>
> from pyspark.sql import HiveContext
>
> from pyspark.sql import SparkSession
>
> from pyspark.sql import Row
>
> from pyspark.sql.types import StringType, ArrayType
>
> from pyspark.sql.functions import udf, col, max as max, to_date, date_add,
> \
>
> add_months
>
> from datetime import datetime, timedelta
>
> import os
>
> from os.path import join, abspath
>
> from typing import Optional
>
> import logging
>
> import random
>
> import string
>
> import math
>
> import mathOperations as mo
>
> import UsedFunctions as uf
>
> ##import test_oracle as to
>
>
> class main:
>
> rec = {}
>
> settings = [
>
> ("hive.exec.dynamic.partition", "true"),
>
> ("hive.exec.dynamic.partition.mode", "nonstrict"),
>
> ("spark.sql.orc.filterPushdown", "true"),
>
> ("hive.msck.path.validation", "ignore"),
>
> ("spark.sql.caseSensitive", "true"),
>
> ("spark.speculation", "false"),
>
> ("hive.metastore.authorization.storage.checks", "false"),
>
> ("hive.metastore.client.connect.retry.delay", "5s"),
>
> ("hive.metastore.client.socket.timeout", "1800s"),
>
> ("hive.metastore.connect.retries", "12"),
>
> ("hive.metastore.execute.setugi", "false"),
>
> ("hive.metastore.failure.retries", "12"),
>
> ("hive.metastore.schema.verification", "false"),
>
> ("hive.metastore.schema.verification.record.version",
> "false"),
>
> ("hive.metastore.server.max.threads", "10"),
>
> ("hive.metastore.authorization.storage.checks",
> "/apps/hive/warehouse")
>
> ]
>
> configs = {"DB":"pycharm",
>
>"tableName":"randomDataPy"}
>
> DB = "pycharm"
>
> tableName = "randomDataPy"
>
> fullyQualifiedTableName = DB +"."+tableName
>
> spark = SparkSession.builder \
>
> .appName("app1") \
>
> .enableHiveSupport() \
>
> .getOrCreate()
>
>
> spark.sparkContext._conf.setAll(settings)
>
>
> sc = SparkContext.getOrCreate()
>
> print(sc.getConf().getAll())
>
> sqlContext = SQLContext(sc)
>
> HiveContext = HiveContext(sc)
>
> lst = (spark.sql("SELECT FROM_unixtime(unix_timestamp(), 'dd/MM/
> HH:mm:ss.ss') ")).collect()
>
> print("\nStarted at");uf.println(lst)
>
>
> numRows = 10 ## do in increment of 50K rows otherwise you blow up
> driver memory!
>
> #
Re: Using Lambda function to generate random data in PySpark throws not defined error
many thanks KR.
If i call the clusterted function on its own it works
numRows = 10
print(uf.clustered(200,numRows))
and returns
0.00199
If I run all in one including the UsedFunctions claa in the same py file it
works. The code is attached
However, in PyCharm, I do the following
UsedFunctions.py. Note that this file only contains functions and no class
import logging
import random
import string
import math
def randomString(length):
letters = string.ascii_letters
result_str = ''.join(random.choice(letters) for i in range(length))
return result_str
def clustered(x,numRows):
return math.floor(x -1)/numRows
def scattered(x,numRows):
return abs((x -1 % numRows))* 1.0
def randomised(seed,numRows):
random.seed(seed)
return abs(random.randint(0, numRows) % numRows) * 1.0
def padString(x,chars,length):
n = int(math.log10(x) + 1)
result_str = ''.join(random.choice(chars) for i in range(length-n)) +
str(x)
return result_str
def padSingleChar(chars,length):
result_str = ''.join(chars for i in range(length))
return result_str
def println(lst):
for ll in lst:
print(ll[0])
In the main.py(PyCharm) I have this code which is failing
from pyspark import SparkContext, SparkConf
from pyspark.sql import SQLContext
from pyspark.sql import HiveContext
from pyspark.sql import SparkSession
from pyspark.sql import Row
from pyspark.sql.types import StringType, ArrayType
from pyspark.sql.functions import udf, col, max as max, to_date, date_add, \
add_months
from datetime import datetime, timedelta
import os
from os.path import join, abspath
from typing import Optional
import logging
import random
import string
import math
import mathOperations as mo
import UsedFunctions as uf
##import test_oracle as to
class main:
rec = {}
settings = [
("hive.exec.dynamic.partition", "true"),
("hive.exec.dynamic.partition.mode", "nonstrict"),
("spark.sql.orc.filterPushdown", "true"),
("hive.msck.path.validation", "ignore"),
("spark.sql.caseSensitive", "true"),
("spark.speculation", "false"),
("hive.metastore.authorization.storage.checks", "false"),
("hive.metastore.client.connect.retry.delay", "5s"),
("hive.metastore.client.socket.timeout", "1800s"),
("hive.metastore.connect.retries", "12"),
("hive.metastore.execute.setugi", "false"),
("hive.metastore.failure.retries", "12"),
("hive.metastore.schema.verification", "false"),
("hive.metastore.schema.verification.record.version",
"false"),
("hive.metastore.server.max.threads", "10"),
("hive.metastore.authorization.storage.checks",
"/apps/hive/warehouse")
]
configs = {"DB":"pycharm",
"tableName":"randomDataPy"}
DB = "pycharm"
tableName = "randomDataPy"
fullyQualifiedTableName = DB +"."+tableName
spark = SparkSession.builder \
.appName("app1") \
.enableHiveSupport() \
.getOrCreate()
spark.sparkContext._conf.setAll(settings)
sc = SparkContext.getOrCreate()
print(sc.getConf().getAll())
sqlContext = SQLContext(sc)
HiveContext = HiveContext(sc)
lst = (spark.sql("SELECT FROM_unixtime(unix_timestamp(), 'dd/MM/
HH:mm:ss.ss') ")).collect()
print("\nStarted at");uf.println(lst)
numRows = 10 ## do in increment of 50K rows otherwise you blow up
driver memory!
#
## Check if table exist otherwise create it
rows = 0
sqltext = ""
if (spark.sql(f"SHOW TABLES IN {DB} like '{tableName}'").count() == 1):
rows = spark.sql(f"""SELECT COUNT(1) FROM
{fullyQualifiedTableName}""").collect()[0][0]
print ("number of rows is ",rows)
else:
print(f"\nTable {fullyQualifiedTableName} does not exist, creating
table ")
sqltext = """
CREATE TABLE {DB}.{tableName}(
ID INT
, CLUSTERED INT
, SCATTERED INT
, RANDOMISED INT
, RANDOM_STRING VARCHAR(50)
, SMALL_VC VARCHAR(50)
, PADDING VARCHAR(4000)
)
STORED AS PARQUET
"""
spark.sql(sqltext)
start = 0
if (rows == 0):
start = 1
else:
maxID = spark.sql(f"SELECT MAX(id) FROM
{fullyQualifiedTableName}").collect()[0][0]
start = maxID + 1
end = start + numRows - 1
print ("starting at ID = ",start, ",ending on = ",end)
Range = range(start, end+1)
## This traverses through the Range and increment "x" by one unit each
time, and that x value is used in the code to generate random data through
Python functions in a class
print(numRows)
print(uf.clustered(200,numRows))
rdd = sc.parallelize(Range). \
map(lambda x: (x, uf.clustered(x, numRows), \
uf.scattered(x,1), \
uf.randomised(x,1), \
Re: Using Lambda function to generate random data in PySpark throws not defined error
copying and pasting your code code in a jup notebook works fine. that is,
using my own version of Range which is simply a list of numbers
how bout this.. does this work fine?
list(map(lambda x: (x, clustered(x, numRows)),[1,2,3,4]))
If it does, i'd look in what's inside your Range and what you get out of
it. I suspect something wrong in there
If there was something with the clustered function, then you should be able
to take it out of the map() and still have the code working..
Could you try that as well?
kr
On Fri, Dec 11, 2020 at 5:04 PM Mich Talebzadeh
wrote:
> Sorry, part of the code is not that visible
>
> rdd = sc.parallelize(Range). \
>map(lambda x: (x, uf.clustered(x, numRows), \
> uf.scattered(x,1), \
> uf.randomised(x,1), \
> uf.randomString(50), \
> uf.padString(x," ",50), \
> uf.padSingleChar("x",4000)))
>
>
>
> *Disclaimer:* Use it at your own risk. Any and all responsibility for any
> loss, damage or destruction of data or any other property which may arise
> from relying on this email's technical content is explicitly disclaimed.
> The author will in no case be liable for any monetary damages arising from
> such loss, damage or destruction.
>
>
>
>
> On Fri, 11 Dec 2020 at 16:56, Mich Talebzadeh
> wrote:
>
>> Thanks Sean,
>>
>> This is the code
>>
>> numRows = 10 ## do in increment of 50K rows otherwise you blow up
>> driver memory!
>> #
>> ## Check if table exist otherwise create it
>>
>>
>> rows = 0
>> sqltext = ""
>> if (spark.sql(f"SHOW TABLES IN {DB} like '{tableName}'").count() == 1):
>> rows = spark.sql(f"""SELECT COUNT(1) FROM
>> {fullyQualifiedTableName}""").collect()[0][0]
>> print ("number of rows is ",rows)
>> else:
>> print(f"\nTable {fullyQualifiedTableName} does not exist, creating table ")
>> sqltext = """
>> CREATE TABLE {DB}.{tableName}(
>> ID INT
>> , CLUSTERED INT
>> , SCATTERED INT
>> , RANDOMISED INT
>> , RANDOM_STRING VARCHAR(50)
>> , SMALL_VC VARCHAR(50)
>> , PADDING VARCHAR(4000)
>> )
>> STORED AS PARQUET
>> """
>> spark.sql(sqltext)
>>
>> start = 0
>> if (rows == 0):
>> start = 1
>> else:
>> maxID = spark.sql(f"SELECT MAX(id) FROM
>> {fullyQualifiedTableName}").collect()[0][0]
>> start = maxID + 1
>> end = start + numRows - 1
>> print ("starting at ID = ",start, ",ending on = ",end)
>> Range = range(start, end+1)
>> ## This traverses through the Range and increment "x" by one unit each time,
>> and that x value is used in the code to generate random data through Python
>> functions in a class
>> print(numRows)
>> print(uf.clustered(200,numRows))
>> rdd = sc.parallelize(Range). \
>> map(lambda x: (x, uf.clustered(x, numRows), \
>>uf.scattered(x,1), \
>>uf.randomised(x,1), \
>>uf.randomString(50), \
>>uf.padString(x," ",50), \
>>uf.padSingleChar("x",4000)))
>> df = rdd.toDF(). \
>> withColumnRenamed("_1","ID"). \
>> withColumnRenamed("_2", "CLUSTERED"). \
>> withColumnRenamed("_3", "SCATTERED"). \
>> withColumnRenamed("_4", "RANDOMISED"). \
>> withColumnRenamed("_5", "RANDOM_STRING"). \
>> withColumnRenamed("_6", "SMALL_VC"). \
>> withColumnRenamed("_7", "PADDING")
>>
>>
>> And this is the run with error
>>
>>
>> Started at
>>
>> 11/12/2020 14:42:45.45
>>
>> number of rows is 450
>>
>> starting at ID = 451 ,ending on = 460
>>
>> 10
>>
>> 0.00199
>>
>> 20/12/11 14:42:56 ERROR Executor: Exception in task 0.0 in stage 7.0 (TID
>> 33)
>>
>> org.apache.spark.api.python.PythonException: Traceback (most recent call
>> last):
>>
>> File
>> "C:\spark-3.0.1-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py",
>> line 605, in main
>>
>> File
>> "C:\spark-3.0.1-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py",
>> line 597, in process
>>
>> File
>> "C:\spark-3.0.1-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\serializers.py",
>> line 271, in dump_stream
>>
>> vs = list(itertools.islice(iterator, batch))
>>
>> File "C:\spark-3.0.1-bin-hadoop2.7\python\pyspark\rdd.py", line 1440,
>> in takeUpToNumLeft
>>
>> yield next(iterator)
>>
>> File
>> "C:\spark-3.0.1-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\util.py", line
>> 107, in wrapper
>>
>> return f(*args, **kwargs)
>>
>> File "C:/Users/admin/PycharmProjects/pythonProject2/pilot/src/main.py",
>> line 101, in
>>
>> map(lambda x: (x, uf.clustered(x, numRows), \
>>
>> NameError: name 'numRows' is not defined
>>
>> Regards,
>>
>> Mich
>>
>>
>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>> any loss, damage or destruction of data or any other property which may
>> arise from relying on this email's technical content is explicitly
>> disclaimed.
Re: Using Lambda function to generate random data in PySpark throws not defined error
Sorry, part of the code is not that visible
rdd = sc.parallelize(Range). \
map(lambda x: (x, uf.clustered(x, numRows), \
uf.scattered(x,1), \
uf.randomised(x,1), \
uf.randomString(50), \
uf.padString(x," ",50), \
uf.padSingleChar("x",4000)))
*Disclaimer:* Use it at your own risk. Any and all responsibility for any
loss, damage or destruction of data or any other property which may arise
from relying on this email's technical content is explicitly disclaimed.
The author will in no case be liable for any monetary damages arising from
such loss, damage or destruction.
On Fri, 11 Dec 2020 at 16:56, Mich Talebzadeh
wrote:
> Thanks Sean,
>
> This is the code
>
> numRows = 10 ## do in increment of 50K rows otherwise you blow up
> driver memory!
> #
> ## Check if table exist otherwise create it
>
>
> rows = 0
> sqltext = ""
> if (spark.sql(f"SHOW TABLES IN {DB} like '{tableName}'").count() == 1):
> rows = spark.sql(f"""SELECT COUNT(1) FROM
> {fullyQualifiedTableName}""").collect()[0][0]
> print ("number of rows is ",rows)
> else:
> print(f"\nTable {fullyQualifiedTableName} does not exist, creating table ")
> sqltext = """
> CREATE TABLE {DB}.{tableName}(
> ID INT
> , CLUSTERED INT
> , SCATTERED INT
> , RANDOMISED INT
> , RANDOM_STRING VARCHAR(50)
> , SMALL_VC VARCHAR(50)
> , PADDING VARCHAR(4000)
> )
> STORED AS PARQUET
> """
> spark.sql(sqltext)
>
> start = 0
> if (rows == 0):
> start = 1
> else:
> maxID = spark.sql(f"SELECT MAX(id) FROM
> {fullyQualifiedTableName}").collect()[0][0]
> start = maxID + 1
> end = start + numRows - 1
> print ("starting at ID = ",start, ",ending on = ",end)
> Range = range(start, end+1)
> ## This traverses through the Range and increment "x" by one unit each time,
> and that x value is used in the code to generate random data through Python
> functions in a class
> print(numRows)
> print(uf.clustered(200,numRows))
> rdd = sc.parallelize(Range). \
> map(lambda x: (x, uf.clustered(x, numRows), \
>uf.scattered(x,1), \
>uf.randomised(x,1), \
>uf.randomString(50), \
>uf.padString(x," ",50), \
>uf.padSingleChar("x",4000)))
> df = rdd.toDF(). \
> withColumnRenamed("_1","ID"). \
> withColumnRenamed("_2", "CLUSTERED"). \
> withColumnRenamed("_3", "SCATTERED"). \
> withColumnRenamed("_4", "RANDOMISED"). \
> withColumnRenamed("_5", "RANDOM_STRING"). \
> withColumnRenamed("_6", "SMALL_VC"). \
> withColumnRenamed("_7", "PADDING")
>
>
> And this is the run with error
>
>
> Started at
>
> 11/12/2020 14:42:45.45
>
> number of rows is 450
>
> starting at ID = 451 ,ending on = 460
>
> 10
>
> 0.00199
>
> 20/12/11 14:42:56 ERROR Executor: Exception in task 0.0 in stage 7.0 (TID
> 33)
>
> org.apache.spark.api.python.PythonException: Traceback (most recent call
> last):
>
> File
> "C:\spark-3.0.1-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py",
> line 605, in main
>
> File
> "C:\spark-3.0.1-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py",
> line 597, in process
>
> File
> "C:\spark-3.0.1-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\serializers.py",
> line 271, in dump_stream
>
> vs = list(itertools.islice(iterator, batch))
>
> File "C:\spark-3.0.1-bin-hadoop2.7\python\pyspark\rdd.py", line 1440,
> in takeUpToNumLeft
>
> yield next(iterator)
>
> File
> "C:\spark-3.0.1-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\util.py", line
> 107, in wrapper
>
> return f(*args, **kwargs)
>
> File "C:/Users/admin/PycharmProjects/pythonProject2/pilot/src/main.py",
> line 101, in
>
> map(lambda x: (x, uf.clustered(x, numRows), \
>
> NameError: name 'numRows' is not defined
>
> Regards,
>
> Mich
>
>
> *Disclaimer:* Use it at your own risk. Any and all responsibility for any
> loss, damage or destruction of data or any other property which may arise
> from relying on this email's technical content is explicitly disclaimed.
> The author will in no case be liable for any monetary damages arising from
> such loss, damage or destruction.
>
>
>
>
> On Fri, 11 Dec 2020 at 16:47, Sean Owen wrote:
>
>> Looks like a simple Python error - you haven't shown the code that
>> produces it. Indeed, I suspect you'll find there is no such symbol.
>>
>> On Fri, Dec 11, 2020 at 9:09 AM Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>> Hi,
>>>
>>> This used to work but not anymore.
>>>
>>> I have UsedFunctions.py file that has these functions
>>>
>>> import random
>>> import string
>>> import math
>>>
>>> def randomString(length):
>>> letters = string.ascii_letters
>>> result_str = ''.join(random.choice(letters) for i in range(length))
>>> return
Re: Using Lambda function to generate random data in PySpark throws not defined error
Thanks Sean,
This is the code
numRows = 10 ## do in increment of 50K rows otherwise you blow
up driver memory!
#
## Check if table exist otherwise create it
rows = 0
sqltext = ""
if (spark.sql(f"SHOW TABLES IN {DB} like '{tableName}'").count() == 1):
rows = spark.sql(f"""SELECT COUNT(1) FROM
{fullyQualifiedTableName}""").collect()[0][0]
print ("number of rows is ",rows)
else:
print(f"\nTable {fullyQualifiedTableName} does not exist, creating table ")
sqltext = """
CREATE TABLE {DB}.{tableName}(
ID INT
, CLUSTERED INT
, SCATTERED INT
, RANDOMISED INT
, RANDOM_STRING VARCHAR(50)
, SMALL_VC VARCHAR(50)
, PADDING VARCHAR(4000)
)
STORED AS PARQUET
"""
spark.sql(sqltext)
start = 0
if (rows == 0):
start = 1
else:
maxID = spark.sql(f"SELECT MAX(id) FROM
{fullyQualifiedTableName}").collect()[0][0]
start = maxID + 1
end = start + numRows - 1
print ("starting at ID = ",start, ",ending on = ",end)
Range = range(start, end+1)
## This traverses through the Range and increment "x" by one unit each
time, and that x value is used in the code to generate random data
through Python functions in a class
print(numRows)
print(uf.clustered(200,numRows))
rdd = sc.parallelize(Range). \
map(lambda x: (x, uf.clustered(x, numRows), \
uf.scattered(x,1), \
uf.randomised(x,1), \
uf.randomString(50), \
uf.padString(x," ",50), \
uf.padSingleChar("x",4000)))
df = rdd.toDF(). \
withColumnRenamed("_1","ID"). \
withColumnRenamed("_2", "CLUSTERED"). \
withColumnRenamed("_3", "SCATTERED"). \
withColumnRenamed("_4", "RANDOMISED"). \
withColumnRenamed("_5", "RANDOM_STRING"). \
withColumnRenamed("_6", "SMALL_VC"). \
withColumnRenamed("_7", "PADDING")
And this is the run with error
Started at
11/12/2020 14:42:45.45
number of rows is 450
starting at ID = 451 ,ending on = 460
10
0.00199
20/12/11 14:42:56 ERROR Executor: Exception in task 0.0 in stage 7.0 (TID
33)
org.apache.spark.api.python.PythonException: Traceback (most recent call
last):
File
"C:\spark-3.0.1-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py",
line 605, in main
File
"C:\spark-3.0.1-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py",
line 597, in process
File
"C:\spark-3.0.1-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\serializers.py",
line 271, in dump_stream
vs = list(itertools.islice(iterator, batch))
File "C:\spark-3.0.1-bin-hadoop2.7\python\pyspark\rdd.py", line 1440, in
takeUpToNumLeft
yield next(iterator)
File
"C:\spark-3.0.1-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\util.py", line
107, in wrapper
return f(*args, **kwargs)
File "C:/Users/admin/PycharmProjects/pythonProject2/pilot/src/main.py",
line 101, in
map(lambda x: (x, uf.clustered(x, numRows), \
NameError: name 'numRows' is not defined
Regards,
Mich
*Disclaimer:* Use it at your own risk. Any and all responsibility for any
loss, damage or destruction of data or any other property which may arise
from relying on this email's technical content is explicitly disclaimed.
The author will in no case be liable for any monetary damages arising from
such loss, damage or destruction.
On Fri, 11 Dec 2020 at 16:47, Sean Owen wrote:
> Looks like a simple Python error - you haven't shown the code that
> produces it. Indeed, I suspect you'll find there is no such symbol.
>
> On Fri, Dec 11, 2020 at 9:09 AM Mich Talebzadeh
> wrote:
>
>> Hi,
>>
>> This used to work but not anymore.
>>
>> I have UsedFunctions.py file that has these functions
>>
>> import random
>> import string
>> import math
>>
>> def randomString(length):
>> letters = string.ascii_letters
>> result_str = ''.join(random.choice(letters) for i in range(length))
>> return result_str
>>
>> def clustered(x,numRows):
>> return math.floor(x -1)/numRows
>>
>> def scattered(x,numRows):
>> return abs((x -1 % numRows))* 1.0
>>
>> def randomised(seed,numRows):
>> random.seed(seed)
>> return abs(random.randint(0, numRows) % numRows) * 1.0
>>
>> def padString(x,chars,length):
>> n = int(math.log10(x) + 1)
>> result_str = ''.join(random.choice(chars) for i in range(length-n)) +
>> str(x)
>> return result_str
>>
>> def padSingleChar(chars,length):
>> result_str = ''.join(chars for i in range(length))
>> return result_str
>>
>> def println(lst):
>> for ll in lst:
>> print(ll[0])
>>
>> Now in the main().py module I import this file as follows:
>>
>> import UsedFunctions as uf
>>
>> Then I try the following
>>
>> import UsedFunctions as uf
>>
>> numRows = 10 ## do in increment of 100K rows
>> rdd = sc.parallelize(Range). \
>>map(lambda x: (x, uf.clustered(x, numRows), \
>> uf.scattered(x,1), \
>> uf.randomised(x,1), \
Re: Using Lambda function to generate random data in PySpark throws not defined error
Looks like a simple Python error - you haven't shown the code that produces
it. Indeed, I suspect you'll find there is no such symbol.
On Fri, Dec 11, 2020 at 9:09 AM Mich Talebzadeh
wrote:
> Hi,
>
> This used to work but not anymore.
>
> I have UsedFunctions.py file that has these functions
>
> import random
> import string
> import math
>
> def randomString(length):
> letters = string.ascii_letters
> result_str = ''.join(random.choice(letters) for i in range(length))
> return result_str
>
> def clustered(x,numRows):
> return math.floor(x -1)/numRows
>
> def scattered(x,numRows):
> return abs((x -1 % numRows))* 1.0
>
> def randomised(seed,numRows):
> random.seed(seed)
> return abs(random.randint(0, numRows) % numRows) * 1.0
>
> def padString(x,chars,length):
> n = int(math.log10(x) + 1)
> result_str = ''.join(random.choice(chars) for i in range(length-n)) +
> str(x)
> return result_str
>
> def padSingleChar(chars,length):
> result_str = ''.join(chars for i in range(length))
> return result_str
>
> def println(lst):
> for ll in lst:
> print(ll[0])
>
> Now in the main().py module I import this file as follows:
>
> import UsedFunctions as uf
>
> Then I try the following
>
> import UsedFunctions as uf
>
> numRows = 10 ## do in increment of 100K rows
> rdd = sc.parallelize(Range). \
>map(lambda x: (x, uf.clustered(x, numRows), \
> uf.scattered(x,1), \
> uf.randomised(x,1), \
> uf.randomString(50), \
> uf.padString(x," ",50), \
> uf.padSingleChar("x",4000)))
> The problem is that now it throws error for numRows as below
>
>
> File "C:/Users/admin/PycharmProjects/pythonProject2/pilot/src/main.py",
> line 101, in
> map(lambda x: (x, uf.clustered(x, numRows), \
> NameError: name 'numRows' is not defined
>
> I don't know why this error is coming!
>
> Appreciate any ideas
>
> Thanks,
>
> Mich
>
>
>
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