I may be doing this wrong so I'll share my approach - I'm finding it
unusably slow though.
I'm working on a stock market analysis tool. There are about 1100
securities on the AMEX, NASDAQ, and NYSE. So I create a Dictionary
in the root dict called securities. This is keyed by stock ticker
symbol and the value is an object of type security which contains
fields tickerSymbol, exchange, issueName, and historicalData.
The historicalData is an OrderedCollection of quotes going back as
far as the 80's. A quote is a timestamp, hi, low, close, and trade
volume. There are around 300 of these per year - going back as much
as 25 years. Believe it or not, I can fetch these from yahoo as a
csv and process them into objects in about 5-10 seconds per
security. Saving this data structure into magma seems to take many
times that. Something like 3 hours in Magma I think. If this were
just the initial load, it would be tolerable. However fetching the
last 5 days quotes and splicing them onto the tail of the
historicalData collection takes as long as the initial load. So this
approach isn't working for me.
I'm open to ideas on better ways to structure this. I'll also be
adding some charts to keep in the database - the idea being the
charts are mostly up to date and I only have to replot the last day's
worth of data everytime it is fetched. When I say chart - I mean a
data structure containing a 2D array of values - not a visual
representation. I will always draw the visual form on the fly.
This chart will also potentially be a big block of data that will
reference the historicalData points.
Ideas? I'm close to just going to image segments - one per security.
-Todd