Hi, folks,

This week we released v0.1.0-alpha3

* Remove constrains on vectors, Simbase support arbitrary vectors now
* Fix various bugs on memory structure to keep scale ratio linearly
* Almost 7 times improvement on performance, right now it can handle 100k
dimensional dense vectors in under 0.14 sec on a i7-cup mac laptop.

>From now on, it enter the beta phase. If it is relevant to your work,  we
encourage you to have a try, and help us to find more bugs.

Regards,
Mingli


On Mon, Jan 13, 2014 at 5:55 PM, Mingli Yuan <mingli.y...@gmail.com> wrote:

> Hi, folks,
>
> We just release an alpha version of Simbase, a vector similarity database
> that talks redis protocol. Since it is the first version of all its
> releases, we decided to keep it in alpha right now, for we want to hear
> from the community for any comments and improvements.
>
> Github page
> ------------------
>
> https://github.com/guokr/simbase
>
> We introduce the basic idea, limitations, build process and commands there.
>
> Background
> ------------------
>
> Simbase is a tool we developed during the process we revise our content
> recommendation engine.
>
> Our document set have 300k docs, and we use LDA to change them into
> vectors. But how to compare the 300k vectors was a problem for us then. We
> had tried different method, but the performance is not very good.
>
> Since the comparison logic is quit simple, we decided to write a new data
> store to do the tricks.
>
> So far, we are satisfied by its performance. Under the setting of an i7
> MacBook and 120k 1k-dimensional vector set:
>
>    - write: about 1 ops per second
>    - read: up to 1k ops per second
>
> The real read performance may be higher than the current result, because
> our testing method is limited.
>
> Regards,
>
> Mingli
>
>
>
>
>

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