What would be the best approach using drools to handle event driven
decisions based on streaming data? In my case I am looking for a typical
stock market scenario. Ticker quotes usually come in at second
increments and depending on how many ticker symbols you subscribe to at
any one time, there can be a lot of data coming and and changing every
second. However, there probably isn't a need to keep large quantities of
historic data in memory...maybe the last 200-500 ticks. The outputs are
if a stock should be sold or bought, at what quantity and what price.
Once the decision has been made, we need to make sure it does not
persist past the point of being valid given the state of that data.
If I have rules that are meant to derive/calculate info and decisions
from the streaming data, what is the best integration architecture for
the rule engine with the rest of the application?
Stateful session which constantly updates the ticks in working memory
and queries the memory for results on an ongoing basis? In this case,
the session would be kept alive for as long as the data stream is going
(i.e. hours)? This approach would require very rigorous working memory
management and all the objects in it.
Or...loop constantly over a stateless session for each tick? This would
reduce the need to manage the number of objects in working memory since
only the amount needed would be inserted in the first place and read
back the results? Sounds inefficient and with lots of overhead.
Also, what about multi-threaded environments? Any potential for
conflicts between concurrent session instances?
Thanks
HC
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