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 <mich.talebza...@gmail.com>
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 <sro...@gmail.com> 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 <mmistr...@gmail.com> 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 = 100000
>>>>>>
>>>>>> 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", "100000"),
>>>>>>
>>>>>> ("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/yyyy HH:mm:ss.ss') ")).collect()
>>>>>>
>>>>>> print("\nStarted at");uf.println(lst)
>>>>>>
>>>>>>
>>>>>> numRows = 100000 ## 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,10000), \
>>>>>>
>>>>>> uf.randomised(x,10000), \
>>>>>>
>>>>>> 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")
>>>>>>
>>>>>> df.write.mode("overwrite").saveAsTable("pycharm.ABCD")
>>>>>>
>>>>>> df.printSchema()
>>>>>>
>>>>>> df.explain()
>>>>>>
>>>>>> df.createOrReplaceTempView("tmp")
>>>>>>
>>>>>> sqltext = f"""
>>>>>>
>>>>>> INSERT INTO TABLE {fullyQualifiedTableName}
>>>>>>
>>>>>> SELECT
>>>>>>
>>>>>> ID
>>>>>>
>>>>>> , CLUSTERED
>>>>>>
>>>>>> , SCATTERED
>>>>>>
>>>>>> , RANDOMISED
>>>>>>
>>>>>> , RANDOM_STRING
>>>>>>
>>>>>> , SMALL_VC
>>>>>>
>>>>>> , PADDING
>>>>>>
>>>>>> FROM tmp
>>>>>>
>>>>>> """
>>>>>>
>>>>>> spark.sql(sqltext)
>>>>>>
>>>>>> spark.sql(f"SELECT MIN(id) AS minID, MAX(id) AS maxID FROM
>>>>>> {fullyQualifiedTableName}").show(n=20,truncate=False,vertical=False)
>>>>>>
>>>>>> ##sqlContext.sql("""SELECT * FROM pycharm.randomDataPy ORDER BY
>>>>>> id""").show(n=20,truncate=False,vertical=False)
>>>>>>
>>>>>> lst = (spark.sql("SELECT FROM_unixtime(unix_timestamp(),
>>>>>> 'dd/MM/yyyy HH:mm:ss.ss') ")).collect()
>>>>>>
>>>>>> print("\nFinished at");usedFunctions.println(lst)
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> On Fri, 11 Dec 2020 at 18:04, Sofia’s World <mmistr...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> 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 <
>>>>>>> mich.talebza...@gmail.com> 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,10000), \
>>>>>>>> uf.randomised(x,10000), \
>>>>>>>> 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 <
>>>>>>>> mich.talebza...@gmail.com> wrote:
>>>>>>>>
>>>>>>>>> Thanks Sean,
>>>>>>>>>
>>>>>>>>> This is the code
>>>>>>>>>
>>>>>>>>> numRows = 100000 ## 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,10000), \
>>>>>>>>> uf.randomised(x,10000), \
>>>>>>>>> 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 4500000
>>>>>>>>>
>>>>>>>>> starting at ID = 4500001 ,ending on = 4600000
>>>>>>>>>
>>>>>>>>> 100000
>>>>>>>>>
>>>>>>>>> 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 <lambda>
>>>>>>>>>
>>>>>>>>> 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 <sro...@gmail.com> 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 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 = 100000 ## do in increment of 100K rows
>>>>>>>>>>> rdd = sc.parallelize(Range). \
>>>>>>>>>>> map(lambda x: (x, uf.clustered(x, numRows), \
>>>>>>>>>>> uf.scattered(x,10000), \
>>>>>>>>>>> uf.randomised(x,10000), \
>>>>>>>>>>> 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 <lambda>
>>>>>>>>>>> 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
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> *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.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
