Agree and I understand what you concerned about that, however we hope
those underlying distributed object graph could be transparent for upper
layer data structures or better with some hints :), but anyway, we could
firstly focus on code moving and stabilization for Mnemonic as you
suggested, Thanks.

On 3/24/2016 2:03 AM, Zheng, Kai wrote:
> Hi Gary,
>
> Thanks for this work and the nice proposal
>
> Overall it makes sense. I do have a concern over this item:
>>>    - Arrow could take advantages of coming Mnemonic features of memory 
>>> clustering/DOG (distributed object graph) and massive native computing
> I would think this will go too far away from the initiatives it should focus 
> on, at least in medium future. There are bundles of projects that provide 
> distributed data structures, and Mnemonic may be better to be utilized in 
> them as a memory centric library project.
>
> By the way, I thought we should try to boost this project up in the ASF space 
> and have the first release out as soon as possible. Is there any plan or 
> update for that? 
>
> Note Apache Arrow itself is still in the very early stage. The integration 
> would be good to happen when the both have their first releases out.
>
> Regards,
> Kai
>
> -----Original Message-----
> From: Gary [mailto:[email protected]] 
> Sent: Thursday, March 24, 2016 10:19 AM
> To: [email protected]
> Subject: A Proposal Apache Incubator Mnemonic as an alternative infra. for 
> Apache Arrow
>
> Hello,
>
>    We have created a patch for Apache Arrow to leverage Apache incubator 
> Mnemonic as an alternative infra. for underlying memory resources allocation, 
> you can find it as below forked repo.
>
> https://github.com/NonVolatileComputing/arrow
>
>     By this way, Apache Arrow could take some structural benefits from 
> Mnemonic project they are
>
>     - Arrow is able to leverage larger capacity of high performance hybrid 
> storage devices. e.g. high-end SSD, NVMe
>
>     - Mnemonic provide a potential opportunity for Arrow to optimize/tuning 
> its allocation algorithms as a native Arrow-oriented allocation services
>
>     - The non-volatile features of  Mnemonic make it possible that Arrow 
> could make its columnar in-memory data shared between different applications 
> or across life-cycle of single application
>
>     - Arrow could take advantages of coming Mnemonic features of memory 
> clustering/DOG (distributed object graph) and massive native computing
>
>     - Mnemonic helps to reduce the pressure of main memory utilization and 
> its related system wide overheads.
>
>    Our this patch is designed to minimize the changes for user to use Arrow, 
> please check out the test cases provided by this patch for your reference.
>
>    Note that, we need to put allocator services to a specified position 
> (indicated by pom.xml) for Mnemonic backed Arrow related test cases to run 
> because those services are required for external memory-like device 
> management.
>
>    Please give your comments and review feedback for better collaboration of 
> Apache Arrow and Mnemonic, Thanks.
>
> Best Regards.
> Gary.
>
>
>
>


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