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 <[email protected]> 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 <[email protected]>
> 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 <[email protected]>
>> 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 <[email protected]> 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 <
>>>> [email protected]> 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.
>>>>>
>>>>>
>>>>>
>>>>
from pyspark import SparkContext
from pyspark.sql import SQLContext
from pyspark.sql import HiveContext
from pyspark.sql.types import StringType, ArrayType
from pyspark.sql.functions import udf, col
import random
import string
import math
class UsedFunctions:
def randomString(self,length):
letters = string.ascii_letters
result_str = ''.join(random.choice(letters) for i in range(length))
return result_str
def clustered(self,x,numRows):
return math.floor(x -1)/numRows
def scattered(self,x,numRows):
return abs((x -1 % numRows))* 1.0
def randomised(self,seed,numRows):
random.seed(seed)
return abs(random.randint(0, numRows) % numRows) * 1.0
def padString(self,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(self,chars,length):
result_str = ''.join(chars for i in range(length))
return result_str
def println(self,lst):
for ll in lst:
print(ll[0])
usedFunctions = UsedFunctions()
sc = SparkContext.getOrCreate()
sqlContext = SQLContext(sc)
HiveContext = HiveContext(sc)
lst = (sqlContext.sql("SELECT FROM_unixtime(unix_timestamp(), 'dd/MM/yyyy
HH:mm:ss.ss') ")).collect()
print("\nStarted at");usedFunctions.println(lst)
numRows = 100000 ## do in increment of 50K rows otherwise you blow up driver
memory!
#
## Check if table exist otherwise create it
#
DB = "test"
tableName = "randomDataPy"
fullyQualifiedTableName = DB + "."+ tableName
rows = 0
sqltext = ""
if (sqlContext.sql("SHOW TABLES IN test like 'randomDataPy'").count() == 1):
rows = sqlContext.sql("SELECT COUNT(1) FROM
test.randomDataPy").collect()[0][0]
print ("number of rows is ",rows)
else:
print("\nTable test.randomDataPy does not exist, creating table ")
sqltext = """
CREATE TABLE test.randomDataPy(
ID INT
, CLUSTERED INT
, SCATTERED INT
, RANDOMISED INT
, RANDOM_STRING VARCHAR(50)
, SMALL_VC VARCHAR(50)
, PADDING VARCHAR(4000)
)
STORED AS PARQUET
"""
sqlContext.sql(sqltext)
start = 0
if (rows == 0):
start = 1
else:
maxID = sqlContext.sql("SELECT MAX(id) FROM
test.randomDataPy").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
rdd = sc.parallelize(Range). \
map(lambda x: (x, usedFunctions.clustered(x,numRows), \
usedFunctions.scattered(x,numRows), \
usedFunctions.randomised(x,numRows), \
usedFunctions.randomString(50), \
usedFunctions.padString(x," ",50), \
usedFunctions.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("test.ABCD")
df.printSchema()
df.explain()
df.createOrReplaceTempView("tmp")
sqltext = """
INSERT INTO TABLE test.randomDataPy
SELECT
ID
, CLUSTERED
, SCATTERED
, RANDOMISED
, RANDOM_STRING
, SMALL_VC
, PADDING
FROM tmp
"""
sqlContext.sql(sqltext)
sqlContext.sql("""SELECT MIN(id) AS minID, MAX(id) AS maxID FROM
test.randomDataPy""").show(n=20,truncate=False,vertical=False)
##sqlContext.sql("""SELECT * FROM test.randomDataPy ORDER BY
id""").show(n=20,truncate=False,vertical=False)
lst = (sqlContext.sql("SELECT FROM_unixtime(unix_timestamp(), 'dd/MM/yyyy
HH:mm:ss.ss') ")).collect()
print("\nFinished at");usedFunctions.println(lst)
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