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 <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.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
