On Fri, Mar 20, 2009 at 5:19 AM, Jos van Dongen <
jvdongen@tholis.com> wrote:
> There is one other open source column based DB that might be worth looking
> at: Infobright Community Edition (
http://www.infobright.org/)
> But if you want the moon on a stick, have a look at Esper, a realtime
> streaming database that supports the kinds of queries you mentioned in the
> UDF examples
>
> Good luck,
> Jos van Dongen
>
>
> Tom H wrote:
>
> On Thu, 2009-03-19 at 18:04 -0700, Yue Sheng wrote:
>
>
> I was wondering if anyone know of other column-based DBs suited to
> vase amount of time series data? Tried Googling. It'd be nice to hear
> from someone who has any insight on this.
>
>
> Hi,
>
> We are currently demonstrating prototypes of our system, which is a web
> based interface similar to igmarkets and CMC markets, that relies on
> running time series queries against 3 different backends that we are
> currently testing. (monet, lucid and mysql)
>
> The other column based system we are looking at is lucidDB.
>
> The current idea is to go for performance from monet or lucid -
>
http://www.luciddb.org/
> or alternatively write some time series custom functions for MySQL.
>
> These guys apparently have the financial time series database market
> nailed;
http://kx.com/ but they are super expensive to deploy.
>
> I guess I should point out that I don't have a db or CS background, I
> work as a tech consultant from a startup incubations perspective, so
> forgive me (or indeed correct me where I am wrong...;-)
>
> My understanding is that performance wise, if you are going to run your
> analysis of your time series on the app/client side of the database
> system, then for technical analysis types of problems, performance is
> going to be achieved by getting a time series into a client side data
> structure as efficiently/quickly as possible for the app to use. And
> monetDB seems to perform very well at that for columns of data.
>
> But that means that a strategy that wants to compare 200day moving
> averages across every time series is going to have to get all that data
> every time it runs the query, or cache it and summarise back to a store
> of aggregate data which has to be written back to a table.
>
> Obviously you can use relevant aggregate SQL queries to improve the
> performance, ie SUM(), COUNT(), MAX() etc.
>
> However ideally, we just want to plug ta-lib or some other technical
> analysis lib straight into the aggregate functions of the database. For
> example at the moment to do a query like; show me all the stocks that
> have declined 40% in 6 months, it would be a join like "select symbol
> from stocks s1 join stocks s2 on s1.symbol = s2.symbol where s1.date =
> NOW() and s2.date = date_sub(Now(), interval 6 months) and (s1.price <
> (.6 * s2.price))
> (I just run that one off my head from what i remember about mysql, its
> probably wrong)
>
> Anyway, but to do something like compare all stocks against bollinger
> bands, or Doji you are going to have to pull down all the data for the
> time series you want to compare, or indeed re-implement bollinger or
> some technical indicator from ta-lib in SQL.
>
> So my point is, that it would be cool to have a extendible UDF system
> that would allow us to plugin a ta-lib library into user defined
> functions for example;
>
> "Select symbol from stocks group by symbol having Today(price) < (.6
> (DateAgo( interval 6 months, price ) ))"
>
> or even better,
>
> "Select symbol from stocks group by symbol having
> Today(MovingAverage(price)) < (.6 (DateAgo( interval 6 months,
> MovingAverage(price,50 days) ) ))"
>
> or even better better;
>
> "Select symbol from stocks group by symbol having
> Today(CallTaLib("Chande Momentum Oscillator",price)) < (.6
> (DateAgo( interval 6 months, CallTAlib("Chande Momentum
> Oscillator",price,50 days) ) ))"
>
> so just have an aggregate function that wraps some time series libraries
> into aggregate functions.
>
> Oh, and having the moon on a stick would be nice as well!!!!
>
> Cheers,
>
> Tom
>
>
>
>
>
>
>
>
>
>
>
>
>
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