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From Nicolas Paris <nicolas.pa...@riseup.net>
Subject Re: Materialized views in Hbase/Phoenix
Date Fri, 11 Oct 2019 16:49:22 GMT
> If one of the tables fails to write, we need some kind of a rollback mechanism,
> which is why I was considering a transaction. We cannot be in a partial state
> where some of the ‘views’ are written and some aren’t.

why not writing to tables to phoenix and if both succeed, 
then rename the original tables and the new tables.
if succeed, then drop the old tables.


On Fri, Sep 27, 2019 at 04:22:54PM +0000, Gautham Acharya wrote:
> We will be reaching 100million rows early next year, and then billions shortly
> after that. So, Hbase will be needed to scale to that degree.
> 
>  
> 
> If one of the tables fails to write, we need some kind of a rollback mechanism,
> which is why I was considering a transaction. We cannot be in a partial state
> where some of the ‘views’ are written and some aren’t.
> 
>  
> 
>  
> 
> From: Pedro Boado [mailto:pedro.boado@gmail.com]
> Sent: Friday, September 27, 2019 7:22 AM
> To: user@phoenix.apache.org
> Subject: Re: Materialized views in Hbase/Phoenix
> 
>  
> 
> CAUTION: This email originated from outside the Allen Institute. Please do not
> click links or open attachments unless you've validated the sender and know the
> content is safe.
> 
> ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
> 
> For just a few million rows I would go for a RDBMS and not Phoenix / HBase.
> 
>  
> 
> You don't really need transactions to control completion, just write a flag (a
> COMPLETED empty file, for instance) as a final step in your job.
> 
>  
> 
>  
> 
>  
> 
> On Fri, 27 Sep 2019, 15:03 Gautham Acharya, <gauthama@alleninstitute.org>
> wrote:
> 
>     Thanks Anil.
> 
>      
> 
>     So, what you’re essentially advocating for is to use some kind of Spark/
>     compute framework (I was going to use AWS Glue) job to write the
>     ‘materialized views’ as separate tables (maybe tied together with some kind
>     of a naming convention?)
> 
>      
> 
>     In this case, we’d end up with some sticky data consistency issues if the
>     write job failed halfway through (some ‘materialized view’ tables would be
>     updated, and some wouldn’t). Can I use Phoenix transactions to wrap the
>     write jobs together, to make sure either all the data is updated, or none?
> 
>      
> 
>     --gautham
> 
>      
> 
>      
> 
>     From: anil gupta [mailto:anilgupta84@gmail.com]
>     Sent: Friday, September 27, 2019 6:58 AM
>     To: user@phoenix.apache.org
>     Subject: Re: Materialized views in Hbase/Phoenix
> 
>      
> 
>     CAUTION: This email originated from outside the Allen Institute. Please do
>     not click links or open attachments unless you've validated the sender and
>     know the content is safe.
> 
>     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
>    
>     For your use case, i would suggest to create another table that stores the
>     matrix. Since this data doesnt change that often, maybe you can write a
>     nightly spark/MR job to update/rebuild the matrix table.(If you want near
>     real time that is also possible with any streaming system) Have you looked
>     into bloom filters? It might help if you have sparse dataset and you are
>     using Phoenix dynamic columns.
>     We use dynamic columns for a table that has columns upto 40k. Here is the
>     presentation and optimizations we made for that use case: https://
>     www.slideshare.net/anilgupta84/phoenix-con2017-truecarfinal
> 
>     IMO, Hive integration with HBase is not fully baked and it has a lot of
>     rough edges. So, it better to stick with native Phoenix/HBase if you care
>     about performance and ease of operations.
> 
>      
> 
>     HTH,
> 
>     Anil Gupta
> 
>      
> 
>      
> 
>     On Wed, Sep 25, 2019 at 10:01 AM Gautham Acharya <
>     gauthama@alleninstitute.org> wrote:
> 
>         Hi,
> 
>          
> 
>         Currently I'm using Hbase to store large, sparse matrices of 50,000
>         columns 10+ million rows of integers.
> 
>          
> 
>         This matrix is used for fast, random access - we need to be able to
>         fetch random row/column subsets, as well as entire columns. We also
>         want to very quickly fetch aggregates (Mean, median, etc) on this
>         matrix.
> 
>          
> 
>         The data does not change very often for these matrices (a few times a
>         week at most), so pre-computing is very feasible here. What I would
>         like to do is maintain a column store (store the column names as row
>         keys, and a compressed list of all the row values) for the use case
>         where we select an entire column. Additionally, I would like to
>         maintain a separate table for each precomputed aggregate (median table,
>         mean table, etc).
> 
>          
> 
>         The query time for all these use cases needs to be low latency - under
>         100ms.
> 
>          
> 
>         When the data does change for a certain matrix, it would be nice to
>         easily update the optimized table. Ideally, I would like the column
>         store/aggregation tables to just be materialized views of the original
>         matrix. It doesn't look like Apache Phoenix supports materialized
>         views. It looks like Hive does, but unfortunately Hive doesn't normally
>         offer low latency queries.
> 
>          
> 
>         Maybe Hive can create the materialized view, and we can just query the
>         underlying Hbase store for lower latency responses?
> 
>          
> 
>         What would be a good solution for this?
> 
>          
> 
>         --gautham
> 
>          
> 
>          
> 
>          
> 
>         --gautham
> 
>          
> 
> 
> 
>     --
> 
>     Thanks & Regards,
>     Anil Gupta
> 

-- 
nicolas

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