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From Mich Talebzadeh <mich.talebza...@gmail.com>
Subject Re: getting error: value toDF is not a member of Seq[columns]
Date Thu, 06 Sep 2018 16:44:38 GMT
Ok somehow this worked!

             // Save prices to mongoDB collection
             val document = sparkContext.parallelize((1 to 1).
                            map(i =>
Document.parse(s"{key:'$key',ticker:'$ticker',timeissued:'$timeissued',price:$price,CURRENCY:'$CURRENCY',op_type:$op_type,op_time:'$op_time'}")))
             //
             // Writing document to Mongo collection
             //
             MongoSpark.save(document, writeConfig)

Note that all non numeric columns are enclosed with '$column'

I just created a dummy map with one single mapping (1 to 1)

These are the results in MongoDB document

{
    "_id" : ObjectId("5b915796d3c6071e82fdca2b"),
    "key" : "23c39917-08a9-4845-ba74-51997707d374",
    "ticker" : "IBM",
    "timeissued" : "2018-09-06T17:51:21",
    "price" : 207.23,
    "CURRENCY" : "GBP",
    "op_type" : NumberInt(1),
    "op_time" : "1536251798114"
}
{
    "_id" : ObjectId("5b915796d3c6071e82fdca2c"),
    "key" : "22f353f9-9b28-463c-9f1c-64213ded7cd5",
    "ticker" : "TSCO",
    "timeissued" : "2018-09-06T17:51:21",
    "price" : 179.52,
    "CURRENCY" : "GBP",
    "op_type" : NumberInt(1),
    "op_time" : "1536251798162"
}


Dr Mich Talebzadeh



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On Thu, 6 Sep 2018 at 10:24, Mich Talebzadeh <mich.talebzadeh@gmail.com>
wrote:

> thanks if you define columns class as below
>
>
> scala> case class columns(KEY: String, TICKER: String, TIMEISSUED:
> String, *PRICE: Double)*
> defined class columns
> scala> var df = Seq(columns("key", "ticker", "timeissued", 1.23f)).toDF
> df: org.apache.spark.sql.DataFrame = [KEY: string, TICKER: string ... 2
> more fields]
> scala> df.printSchema
> root
>  |-- KEY: string (nullable = true)
>  |-- TICKER: string (nullable = true)
>  |-- TIMEISSUED: string (nullable = true)
>  |-- PRICE: double (nullable = false)
>
> looks better
>
> Dr Mich Talebzadeh
>
>
>
> LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>*
>
>
>
> http://talebzadehmich.wordpress.com
>
>
> *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 Thu, 6 Sep 2018 at 10:10, Jungtaek Lim <kabhwan@gmail.com> wrote:
>
>> This code works with Spark 2.3.0 via spark-shell.
>>
>> scala> case class columns(KEY: String, TICKER: String, TIMEISSUED:
>> String, PRICE: Float)
>> defined class columns
>>
>> scala> import spark.implicits._
>> import spark.implicits._
>>
>> scala> var df = Seq(columns("key", "ticker", "timeissued", 1.23f)).toDF
>> 18/09/06 18:02:23 WARN ObjectStore: Failed to get database global_temp,
>> returning NoSuchObjectException
>> df: org.apache.spark.sql.DataFrame = [KEY: string, TICKER: string ... 2
>> more fields]
>>
>> scala> df
>> res0: org.apache.spark.sql.DataFrame = [KEY: string, TICKER: string ... 2
>> more fields]
>>
>> Maybe need to know about actual type of key, ticker, timeissued, price
>> from your variables.
>>
>> Jungtaek Lim (HeartSaVioR)
>>
>> 2018년 9월 6일 (목) 오후 5:57, Mich Talebzadeh <mich.talebzadeh@gmail.com>님이
>> 작성:
>>
>>> I am trying to understand why spark cannot convert a simple comma
>>> separated columns as DF.
>>>
>>> I did a test
>>>
>>> I took one line of print and stored it as a one liner csv file as below
>>>
>>> var allInOne = key+","+ticker+","+timeissued+","+price
>>> println(allInOne)
>>>
>>> cat crap.csv
>>> 6e84b11d-cb03-44c0-aab6-37e06e06c996,MRW,2018-09-06T09:35:53,275.45
>>>
>>> Then after storing it in HDFS, I read that file as below
>>>
>>> import org.apache.spark.sql.functions._
>>> val location="hdfs://rhes75:9000/tmp/crap.csv"
>>> val df1 = spark.read.option("header", false).csv(location)
>>> case class columns(KEY: String, TICKER: String, TIMEISSUED: String,
>>> PRICE: Double)
>>> val df2 = df1.map(p => columns(p(0).toString,p(1).toString,
>>> p(2).toString,p(3).toString.toDouble))
>>> df2.printSchema
>>>
>>> This is the result I get
>>>
>>> df1: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 2
>>> more fields]
>>> defined class columns
>>> df2: org.apache.spark.sql.Dataset[columns] = [KEY: string, TICKER:
>>> string ... 2 more fields]
>>> root
>>>  |-- KEY: string (nullable = true)
>>>  |-- TICKER: string (nullable = true)
>>>  |-- TIMEISSUED: string (nullable = true)
>>>  |-- PRICE: double (nullable = false)
>>>
>>> So in my case the only difference is that that comma separated line is
>>> stored in a String as opposed to csv.
>>>
>>> How can I achieve this simple transformation?
>>>
>>> Thanks
>>>
>>> Dr Mich Talebzadeh
>>>
>>>
>>>
>>> LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>>> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>*
>>>
>>>
>>>
>>> http://talebzadehmich.wordpress.com
>>>
>>>
>>> *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 Thu, 6 Sep 2018 at 03:38, Manu Zhang <owenzhang1990@gmail.com> wrote:
>>>
>>>> Have you tried adding Encoder for columns as suggested by Jungtaek Lim
>>>> ?
>>>>
>>>> On Thu, Sep 6, 2018 at 6:24 AM Mich Talebzadeh <
>>>> mich.talebzadeh@gmail.com> wrote:
>>>>
>>>>>
>>>>> Dr Mich Talebzadeh
>>>>>
>>>>>
>>>>>
>>>>> LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>>>>> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>*
>>>>>
>>>>>
>>>>>
>>>>> http://talebzadehmich.wordpress.com
>>>>>
>>>>>
>>>>> *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.
>>>>>
>>>>>
>>>>>
>>>>> I can rebuild the comma separated list as follows:
>>>>>
>>>>>
>>>>>    case class columns(KEY: String, TICKER: String, TIMEISSUED:
>>>>> String, PRICE: Float)
>>>>>     val sqlContext= new org.apache.spark.sql.SQLContext(sparkContext)
>>>>>     import sqlContext.implicits._
>>>>>
>>>>>
>>>>>          for(line <- pricesRDD.collect.toArray)
>>>>>          {
>>>>>            var key = line._2.split(',').view(0).toString
>>>>>            var ticker =  line._2.split(',').view(1).toString
>>>>>            var timeissued = line._2.split(',').view(2).toString
>>>>>            var price = line._2.split(',').view(3).toFloat
>>>>>            var allInOne = key+","+ticker+","+timeissued+","+price
>>>>>            println(allInOne)
>>>>>
>>>>> and the print shows the columns separated by ","
>>>>>
>>>>>
>>>>> 34e07d9f-829a-446a-93ab-8b93aa8eda41,SAP,2018-09-05T23:22:34,56.89
>>>>>
>>>>> So I just need to convert that line of rowinto a DataFrame
>>>>>
>>>>> I try this conversion to DF to write to MongoDB document with MongoSpark.save(df,
>>>>> writeConfig)
>>>>>
>>>>> var df = sparkContext.parallelize(Seq(columns(key, ticker, timeissued,
>>>>> price))).toDF
>>>>>
>>>>> [error]
>>>>> /data6/hduser/scala/md_streaming_mongoDB/src/main/scala/myPackage/md_streaming_mongoDB.scala:235:
>>>>> value toDF is not a member of org.apache.spark.rdd.RDD[columns]
>>>>> [error]             var df = sparkContext.parallelize(Seq(columns(key,
>>>>> ticker, timeissued, price))).toDF
>>>>> [
>>>>>
>>>>>
>>>>> frustrating!
>>>>>
>>>>>  has anyone come across this?
>>>>>
>>>>> thanks
>>>>>
>>>>> On Wed, 5 Sep 2018 at 13:30, Mich Talebzadeh <
>>>>> mich.talebzadeh@gmail.com> wrote:
>>>>>
>>>>>> yep already tried it and it did not work.
>>>>>>
>>>>>> thanks
>>>>>>
>>>>>> Dr Mich Talebzadeh
>>>>>>
>>>>>>
>>>>>>
>>>>>> LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>>>>>> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>*
>>>>>>
>>>>>>
>>>>>>
>>>>>> http://talebzadehmich.wordpress.com
>>>>>>
>>>>>>
>>>>>> *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 Wed, 5 Sep 2018 at 10:10, Deepak Sharma <deepakmca05@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Try this:
>>>>>>>
>>>>>>> *import **spark*.implicits._
>>>>>>>
>>>>>>> df.toDF()
>>>>>>>
>>>>>>>
>>>>>>> On Wed, Sep 5, 2018 at 2:31 PM Mich Talebzadeh <
>>>>>>> mich.talebzadeh@gmail.com> wrote:
>>>>>>>
>>>>>>>> With the following
>>>>>>>>
>>>>>>>> case class columns(KEY: String, TICKER: String, TIMEISSUED:
String,
>>>>>>>> PRICE: Float)
>>>>>>>>
>>>>>>>>  var key = line._2.split(',').view(0).toString
>>>>>>>>  var ticker =  line._2.split(',').view(1).toString
>>>>>>>>  var timeissued = line._2.split(',').view(2).toString
>>>>>>>>  var price = line._2.split(',').view(3).toFloat
>>>>>>>>
>>>>>>>>   var df = Seq(columns(key, ticker, timeissued, price))
>>>>>>>>  println(df)
>>>>>>>>
>>>>>>>> I get
>>>>>>>>
>>>>>>>>
>>>>>>>> List(columns(ac11a78d-82df-4b37-bf58-7e3388aa64cd,MKS,2018-09-05T10:10:15,676.5))
>>>>>>>>
>>>>>>>> So just need to convert that list to DF
>>>>>>>>
>>>>>>>> Dr Mich Talebzadeh
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>>>>>>>> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>*
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> http://talebzadehmich.wordpress.com
>>>>>>>>
>>>>>>>>
>>>>>>>> *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 Wed, 5 Sep 2018 at 09:49, Mich Talebzadeh <
>>>>>>>> mich.talebzadeh@gmail.com> wrote:
>>>>>>>>
>>>>>>>>> Thanks!
>>>>>>>>>
>>>>>>>>> The spark  is version 2.3.0
>>>>>>>>>
>>>>>>>>> Dr Mich Talebzadeh
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>>>>>>>>> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>*
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> http://talebzadehmich.wordpress.com
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> *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 Wed, 5 Sep 2018 at 09:41, Jungtaek Lim <kabhwan@gmail.com>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>>> You may also find below link useful (though it looks
far old),
>>>>>>>>>> since case class is the thing which Encoder is available,
so there may be
>>>>>>>>>> another reason which prevent implicit conversion.
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> https://community.cloudera.com/t5/Advanced-Analytics-Apache-Spark/Spark-Scala-Error-value-toDF-is-not-a-member-of-org-apache/m-p/29994/highlight/true#M973
>>>>>>>>>>
>>>>>>>>>> And which Spark version do you use?
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> 2018년 9월 5일 (수) 오후 5:32, Jungtaek Lim
<kabhwan@gmail.com>님이 작성:
>>>>>>>>>>
>>>>>>>>>>> Sorry I guess I pasted another method. the code
is...
>>>>>>>>>>>
>>>>>>>>>>> implicit def localSeqToDatasetHolder[T : Encoder](s:
Seq[T]): DatasetHolder[T] = {
>>>>>>>>>>>   DatasetHolder(_sqlContext.createDataset(s))
>>>>>>>>>>> }
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> 2018년 9월 5일 (수) 오후 5:30, Jungtaek
Lim <kabhwan@gmail.com>님이 작성:
>>>>>>>>>>>
>>>>>>>>>>>> I guess you need to have encoder for the
type of result for
>>>>>>>>>>>> columns().
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> https://github.com/apache/spark/blob/2119e518d31331e65415e0f817a6f28ff18d2b42/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala#L227-L229
>>>>>>>>>>>>
>>>>>>>>>>>> implicit def rddToDatasetHolder[T : Encoder](rdd:
RDD[T]): DatasetHolder[T] = {
>>>>>>>>>>>>   DatasetHolder(_sqlContext.createDataset(rdd))
>>>>>>>>>>>> }
>>>>>>>>>>>>
>>>>>>>>>>>> You can see lots of Encoder implementations
in the scala code.
>>>>>>>>>>>> If your type doesn't match anything it may
not work and you need to provide
>>>>>>>>>>>> custom Encoder.
>>>>>>>>>>>>
>>>>>>>>>>>> -Jungtaek Lim (HeartSaVioR)
>>>>>>>>>>>>
>>>>>>>>>>>> 2018년 9월 5일 (수) 오후 5:24, Mich
Talebzadeh <
>>>>>>>>>>>> mich.talebzadeh@gmail.com>님이 작성:
>>>>>>>>>>>>
>>>>>>>>>>>>> Thanks
>>>>>>>>>>>>>
>>>>>>>>>>>>> I already do that as below
>>>>>>>>>>>>>
>>>>>>>>>>>>>     val sqlContext= new
>>>>>>>>>>>>> org.apache.spark.sql.SQLContext(sparkContext)
>>>>>>>>>>>>>   import sqlContext.implicits._
>>>>>>>>>>>>>
>>>>>>>>>>>>> but still getting the error!
>>>>>>>>>>>>>
>>>>>>>>>>>>> Dr Mich Talebzadeh
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>>>>>>>>>>>>> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>*
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> http://talebzadehmich.wordpress.com
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> *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 Wed, 5 Sep 2018 at 09:17, Jungtaek
Lim <kabhwan@gmail.com>
>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> You may need to import implicits
from your spark session like
>>>>>>>>>>>>>> below:
>>>>>>>>>>>>>> (Below code is borrowed from
>>>>>>>>>>>>>> https://spark.apache.org/docs/latest/sql-programming-guide.html
>>>>>>>>>>>>>> )
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> import org.apache.spark.sql.SparkSession
>>>>>>>>>>>>>> val spark = SparkSession
>>>>>>>>>>>>>>   .builder()
>>>>>>>>>>>>>>   .appName("Spark SQL basic example")
>>>>>>>>>>>>>>   .config("spark.some.config.option",
"some-value")
>>>>>>>>>>>>>>   .getOrCreate()
>>>>>>>>>>>>>> // For implicit conversions like
converting RDDs to DataFramesimport spark.implicits._
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> 2018년 9월 5일 (수) 오후 5:11,
Mich Talebzadeh <
>>>>>>>>>>>>>> mich.talebzadeh@gmail.com>님이
작성:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I have spark streaming that send
data and I need to put that
>>>>>>>>>>>>>>> data into MongoDB for test purposes.
The easiest way is to create a DF from
>>>>>>>>>>>>>>> the individual list of columns
as below
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I loop over individual rows in
RDD and perform the following
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>     case class columns(KEY: String,
TICKER: String,
>>>>>>>>>>>>>>> TIMEISSUED: String, PRICE: Float)
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>          for(line <- pricesRDD.collect.toArray)
>>>>>>>>>>>>>>>          {
>>>>>>>>>>>>>>>             var key = line._2.split(',').view(0).toString
>>>>>>>>>>>>>>>            var ticker =  line._2.split(',').view(1).toString
>>>>>>>>>>>>>>>            var timeissued =
>>>>>>>>>>>>>>> line._2.split(',').view(2).toString
>>>>>>>>>>>>>>>            var price = line._2.split(',').view(3).toFloat
>>>>>>>>>>>>>>>            val priceToString
= line._2.split(',').view(3)
>>>>>>>>>>>>>>>            if (price > 90.0)
>>>>>>>>>>>>>>>            {
>>>>>>>>>>>>>>>              println ("price
> 90.0, saving to MongoDB
>>>>>>>>>>>>>>> collection!")
>>>>>>>>>>>>>>>             // Save prices to
mongoDB collection
>>>>>>>>>>>>>>>            * var df = Seq(columns(key,
ticker, timeissued,
>>>>>>>>>>>>>>> price)).toDF*
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> but it fails with message
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>  value toDF is not a member of
Seq[columns].
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> What would be the easiest way
of resolving this please?
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> thanks
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Dr Mich Talebzadeh
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>>>>>>>>>>>>>>> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>*
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> http://talebzadehmich.wordpress.com
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> *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.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>
>>>>>>> --
>>>>>>> Thanks
>>>>>>> Deepak
>>>>>>> www.bigdatabig.com
>>>>>>> www.keosha.net
>>>>>>>
>>>>>>

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