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From Ted Yu <yuzhih...@gmail.com>
Subject Re: Extracting key word from a textual column
Date Tue, 02 Aug 2016 22:10:35 GMT
+1

> On Aug 2, 2016, at 2:29 PM, Jörn Franke <jornfranke@gmail.com> wrote:
> 
> If you need to use single inserts, updates, deletes, select why not use hbase with Phoenix?
I see it as complementary to the hive / warehouse offering 
> 
>> On 02 Aug 2016, at 22:34, Mich Talebzadeh <mich.talebzadeh@gmail.com> wrote:
>> 
>> Hi,
>> 
>> I decided to create a catalog table in Hive ORC and transactional. That table has
two columns of value
>> 
>> transactiondescription === account_table.transactiondescription
>> hashtag String column created from a semi automated process of deriving it from account_table.transactiondescription
>> Once the process is complete in populating the catalog table then we just need to
create a new DF based on join between catalog table and the account_table. The join will use
hashtag in catalog table to loop over debit column in account_table for a given hashtag. That
is pretty fast as going through pattern matching is pretty intensive in any application and
database in real time.
>> 
>> So one can build up the catalog table over time as a reference table. I am sure such
tables exist in commercial world.
>> 
>> Anyway after getting results out I know how I am wasting my money on different things,
especially on clothing  etc :)
>> 
>> 
>> HTH
>> 
>> P.S. Also there is an issue with Spark not being able to read data through Hive transactional
tables that have not been compacted yet. Spark just crashes. If these tables need to be updated
regularly say catalog table and they are pretty small, one might maintain them in an RDBMS
and read them once through JDBC into a DataFrame in Spark before doing analytics.
>> 
>> 
>> Dr Mich Talebzadeh
>>  
>> LinkedIn  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 2 August 2016 at 17:56, Sonal Goyal <sonalgoyal4@gmail.com> wrote:
>>> Hi Mich,
>>> 
>>> It seems like an entity resolution problem - looking at different representations
of an entity - SAINSBURY in this case and matching them all together. How dirty is your data
in the description - are there stop words like SACAT/SMKT etc you can strip off and get the
base retailer entity ?
>>> 
>>> Best Regards,
>>> Sonal
>>> Founder, Nube Technologies 
>>> Reifier at Strata Hadoop World
>>> Reifier at Spark Summit 2015
>>> 
>>> 
>>> 
>>> 
>>> 
>>>> On Tue, Aug 2, 2016 at 9:55 PM, Mich Talebzadeh <mich.talebzadeh@gmail.com>
wrote:
>>>> Thanks.
>>>> 
>>>> I believe there is some catalog of companies that I can get and store it
in a table and math the company name to transactiondesciption column.
>>>> 
>>>> That catalog should have sectors in it. For example company XYZ is under
Grocers etc which will make search and grouping much easier.
>>>> 
>>>> I believe Spark can do it, though I am generally interested on alternative
ideas.
>>>> 
>>>> 
>>>> 
>>>> 
>>>> 
>>>> Dr Mich Talebzadeh
>>>>  
>>>> LinkedIn  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 2 August 2016 at 16:26, Yong Zhang <java8964@hotmail.com> wrote:
>>>>> Well, if you still want to use windows function for your logic, then
you need to derive a new column out, like "catalog", and use it as part of grouping logic.
>>>>> 
>>>>> 
>>>>> Maybe you can use regex for deriving out this new column. The implementation
needs to depend on your data in "transactiondescription", and regex gives you the most powerful
way to handle your data.
>>>>> 
>>>>> 
>>>>> This is really not a Spark question, but how to you process your logic
based on the data given.
>>>>> 
>>>>> 
>>>>> Yong
>>>>> 
>>>>> 
>>>>> From: Mich Talebzadeh <mich.talebzadeh@gmail.com>
>>>>> Sent: Tuesday, August 2, 2016 10:00 AM
>>>>> To: user @spark
>>>>> Subject: Extracting key word from a textual column
>>>>>  
>>>>> Hi,
>>>>> 
>>>>> Need some ideas.
>>>>> 
>>>>> Summary:
>>>>> 
>>>>> I am working on a tool to slice and dice the amount of money I have spent
so far (meaning the whole data sample) on a given retailer so I have a better idea of where
I am wasting the money
>>>>> 
>>>>> Approach
>>>>> 
>>>>> Downloaded my bank statements from a given account in csv format from
inception till end of July. Read the data and stored it in ORC table.
>>>>> 
>>>>> I am interested for all bills that I paid using Debit Card ( transactiontype
= "DEB") that comes out the account directly. Transactiontype is the three character code
lookup that I download as well.
>>>>> 
>>>>> scala> ll_18740868.printSchema
>>>>> root
>>>>>  |-- transactiondate: date (nullable = true)
>>>>>  |-- transactiontype: string (nullable = true)
>>>>>  |-- sortcode: string (nullable = true)
>>>>>  |-- accountnumber: string (nullable = true)
>>>>>  |-- transactiondescription: string (nullable = true)
>>>>>  |-- debitamount: double (nullable = true)
>>>>>  |-- creditamount: double (nullable = true)
>>>>>  |-- balance: double (nullable = true)
>>>>> 
>>>>> The important fields are transactiondate, transactiontype, transactiondescription
and debitamount
>>>>> 
>>>>> So using analytics. windowing I can do all sorts of things. For example
this one gives me the last time I spent money on retailer XYZ and the amount
>>>>> 
>>>>> SELECT *
>>>>> FROM (
>>>>>       select transactiondate, transactiondescription, debitamount
>>>>>       , rank() over (order by transactiondate desc) AS rank
>>>>>       from accounts.ll_18740868 where transactiondescription like '%XYZ%'
>>>>>      ) tmp
>>>>> where rank <= 1
>>>>> 
>>>>> And its equivalent using Windowing in FP
>>>>> 
>>>>> import org.apache.spark.sql.expressions.Window
>>>>> val wSpec = Window.partitionBy("transactiontype").orderBy(desc("transactiondate"))
>>>>> ll_18740868.filter(col("transactiondescription").contains("XYZ")).select($"transactiondate",$"transactiondescription",
rank().over(wSpec).as("rank")).filter($"rank"===1).show
>>>>> 
>>>>> 
>>>>> +---------------+----------------------+----+
>>>>> |transactiondate|transactiondescription|rank|
>>>>> +---------------+----------------------+----+
>>>>> |     2015-12-15|  XYZ LTD CD 4636 |   1|
>>>>> +---------------+----------------------+----+
>>>>> 
>>>>> So far so good. But if I want to find all I spent on each retailer, then
it gets trickier as a retailer appears like below in the column transactiondescription:
>>>>> 
>>>>> ll_18740868.where($"transactiondescription".contains("SAINSBURY")).select($"transactiondescription").show(5)
>>>>> +----------------------+
>>>>> |transactiondescription|
>>>>> +----------------------+
>>>>> |  SAINSBURYS SMKT C...|
>>>>> |  SACAT SAINSBURYS ...|
>>>>> |  SAINSBURY'S SMKT ...|
>>>>> |  SAINSBURYS S/MKT ...|
>>>>> |  SACAT SAINSBURYS ...|
>>>>> +----------------------+
>>>>> 
>>>>> If I look at them I know they all belong to SAINBURYS (food retailer).
I have done some crude grouping and it works somehow
>>>>> 
>>>>> //define UDF here to handle substring
>>>>> val SubstrUDF = udf { (s: String, start: Int, end: Int) => s.substring(start,
end) }
>>>>> var cutoff = "CD"  // currently used in the statement
>>>>> val wSpec2 = Window.partitionBy(SubstrUDF($"transactiondescription",lit(0),instr($"transactiondescription",
cutoff)-1))
>>>>> ll_18740868.where($"transactiontype" === "DEB" && ($"transactiondescription").isNotNull).select(SubstrUDF($"transactiondescription",lit(0),instr($"transactiondescription",
cutoff)-1).as("Retailer"),sum($"debitamount").over(wSpec2).as("Spent")).distinct.orderBy($"Spent").collect.foreach(println)
>>>>> 
>>>>> However, I really need to extract the "keyword" retailer name from transactiondescription
column And I need some ideas about the best way of doing it. Is this possible in Spark?
>>>>> 
>>>>> Thanks
>>>>> 
>>>>> Dr Mich Talebzadeh
>>>>>  
>>>>> LinkedIn  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.
>> 

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