Guys, We are moving in tangent here. The question was what is the easiest way of finding key words in a string column as in transactiondescription?
I am aware of functions like regexp, instr, patindex etc. How in general this is done and not necessarily in Spark? For example. A naive question. When I type "regexp in Spark" in Google it come back with many urls. Granted some of them may be repeat url. How does it do that in general? Is it through hashtag/keyword search or build an index of tags in its massive library for later use. I gather if you hit the same keyword many times its hit gets higher etc. 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 3 August 2016 at 06:32, Jörn Franke <jornfra...@gmail.com> wrote: > I agree with you. > > On 03 Aug 2016, at 01:20, ayan guha <guha.a...@gmail.com> wrote: > > I would stay away from transaction tables until they are fully baked. I do > not see why you need to update vs keep inserting with timestamp and while > joining derive latest value on the fly. > > But I guess it has became a religious question now :) and I am not > unbiased. > On 3 Aug 2016 08:51, "Mich Talebzadeh" <mich.talebza...@gmail.com> wrote: > >> There are many ways of addressing this issue. >> >> Using Hbase with Phoenix adds another layer to the stack which is not >> necessary for handful of table and will add to cost (someone else has to >> know about Hbase, Phoenix etc. (BTW I would rather work directly on Hbase >> table. It is faster) >> >> There may be say 100 new entries into this catalog table with multiple >> updates (not a single DML) to get hashtag right. sometimes it is an >> iterative process which results in many deltas. >> >> If that is needed done once a day or on demand, an alternative would be >> to insert overwrite the transactional hive table with deltas into a text >> table in Hive and present that one to Spark. This allows Spark to see the >> data. >> >> Remember if I use Hive to do the analytics/windowing, there is no issue. >> The issue is with Spark that neither Spark SQL or Spark shell can use that >> table. >> >> Sounds like an issue for Spark to resolve later. >> >> Another alternative one can leave the transactional table in RDBMS for >> this purpose and load it into DF through JDBC interface. It works fine and >> pretty fast. >> >> Again these are all workarounds. I discussed this in Hive forum. There >> should be a way" to manually compact a transactional table in Hive" (not >> possible now) and second point if Hive can see the data in Hive table, why >> not Spark? >> >> HTH >> >> >> 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 2 August 2016 at 23:10, Ted Yu <yuzhih...@gmail.com> wrote: >> >>> +1 >>> >>> On Aug 2, 2016, at 2:29 PM, Jörn Franke <jornfra...@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.talebza...@gmail.com> >>> wrote: >>> >>> Hi, >>> >>> I decided to create a catalog table in Hive ORC and transactional. That >>> table has two columns of value >>> >>> >>> 1. transactiondescription === account_table.transactiondescription >>> 2. 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 >>> <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 <sonalgoy...@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 <http://www.nubetech.co> >>>> Reifier at Strata Hadoop World >>>> <https://www.youtube.com/watch?v=eD3LkpPQIgM> >>>> Reifier at Spark Summit 2015 >>>> <https://spark-summit.org/2015/events/real-time-fuzzy-matching-with-spark-and-elastic-search/> >>>> >>>> <http://in.linkedin.com/in/sonalgoyal> >>>> >>>> >>>> >>>> On Tue, Aug 2, 2016 at 9:55 PM, Mich Talebzadeh < >>>> mich.talebza...@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 >>>>> <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 <java8...@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.talebza...@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 >>>>>> <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. >>>>>> >>>>>> >>>>>> >>>>> >>>>> >>>> >>> >>
