unsubscribe

[Moise]

unsubscribe

Re: ********Spark streaming issue to Elastic data**********

[Mich Talebzadeh]

Hi Kartrick,

Unfortunately Materialised views are not available in Spark as yet. I
raised Jira [SPARK-48117] Spark Materialized Views: Improve Query
Performance and Data Management - ASF JIRA (apache.org)
 as a feature request.

Let me think about another way and revert

HTH

Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Mon, 6 May 2024 at 07:54, Karthick Nk  wrote:

> Thanks Mich,
>
> can you please confirm me is my understanding correct?
>
> First, we have to create the materialized view based on the mapping
> details we have by using multiple tables as source(since we have multiple
> join condition from different tables). From the materialised view we can
> stream the view data into elastic index by using cdc?
>
> Thanks in advance.
>
> On Fri, May 3, 2024 at 3:39 PM Mich Talebzadeh 
> wrote:
>
>> My recommendation! is using materialized views (MVs) created in Hive with
>> Spark Structured Streaming and Change Data Capture (CDC) is a good
>> combination for efficiently streaming view data updates in your scenario.
>>
>> HTH
>>
>> Mich Talebzadeh,
>> Technologist | Architect | Data Engineer  | Generative AI | FinCrime
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *Disclaimer:* The information provided is correct to the best of my
>> knowledge but of course cannot be guaranteed . It is essential to note
>> that, as with any advice, quote "one test result is worth one-thousand
>> expert opinions (Werner
>> Von Braun
>> )".
>>
>>
>> On Thu, 2 May 2024 at 21:25, Karthick Nk  wrote:
>>
>>> Hi All,
>>>
>>> Requirements:
>>> I am working on the data flow, which will use the view definition(view
>>> definition already defined in schema), there are multiple tables used in
>>> the view definition. Here we want to stream the view data into elastic
>>> index based on if any of the table(used in the view definition) data got
>>> changed.
>>>
>>>
>>> Current flow:
>>> 1. we are inserting id's from the table(which used in the view
>>> definition) into the common table.
>>> 2. From the common table by using the id, we will be streaming the view
>>> data (by using if any of the incomming id is present in the collective id
>>> of all tables used from view definition) by using spark structured
>>> streaming.
>>>
>>>
>>> Issue:
>>> 1. Here we are facing issue - For each incomming id here we running view
>>> definition(so it will read all the data from all the data) and check if any
>>> of the incomming id is present in the collective id's of view result, Due
>>> to which it is taking more memory in the cluster driver and taking more
>>> time to process.
>>>
>>>
>>> I am epxpecting an alternate solution, if we can avoid full scan of view
>>> definition every time, If you have any alternate deisgn flow how we can
>>> achieve the result, please suggest for the same.
>>>
>>>
>>> Note: Also, it will be helpfull, if you can share the details like
>>> community forum or platform to discuss this kind of deisgn related topics,
>>> it will be more helpfull.
>>>
>>

Re: ********Spark streaming issue to Elastic data**********

[Karthick Nk]

Thanks Mich,

can you please confirm me is my understanding correct?

First, we have to create the materialized view based on the mapping details
we have by using multiple tables as source(since we have multiple
join condition from different tables). From the materialised view we can
stream the view data into elastic index by using cdc?

Thanks in advance.

On Fri, May 3, 2024 at 3:39 PM Mich Talebzadeh 
wrote:

> My recommendation! is using materialized views (MVs) created in Hive with
> Spark Structured Streaming and Change Data Capture (CDC) is a good
> combination for efficiently streaming view data updates in your scenario.
>
> HTH
>
> Mich Talebzadeh,
> Technologist | Architect | Data Engineer  | Generative AI | FinCrime
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner  Von
> Braun )".
>
>
> On Thu, 2 May 2024 at 21:25, Karthick Nk  wrote:
>
>> Hi All,
>>
>> Requirements:
>> I am working on the data flow, which will use the view definition(view
>> definition already defined in schema), there are multiple tables used in
>> the view definition. Here we want to stream the view data into elastic
>> index based on if any of the table(used in the view definition) data got
>> changed.
>>
>>
>> Current flow:
>> 1. we are inserting id's from the table(which used in the view
>> definition) into the common table.
>> 2. From the common table by using the id, we will be streaming the view
>> data (by using if any of the incomming id is present in the collective id
>> of all tables used from view definition) by using spark structured
>> streaming.
>>
>>
>> Issue:
>> 1. Here we are facing issue - For each incomming id here we running view
>> definition(so it will read all the data from all the data) and check if any
>> of the incomming id is present in the collective id's of view result, Due
>> to which it is taking more memory in the cluster driver and taking more
>> time to process.
>>
>>
>> I am epxpecting an alternate solution, if we can avoid full scan of view
>> definition every time, If you have any alternate deisgn flow how we can
>> achieve the result, please suggest for the same.
>>
>>
>> Note: Also, it will be helpfull, if you can share the details like
>> community forum or platform to discuss this kind of deisgn related topics,
>> it will be more helpfull.
>>
>