Sorry, this strategy doesn't work well with long tail and personalized search load. The indexing cost (I'd consider cluster selection an indexing phase) is much higher as well. For aggregate performance, a much simpler caching strategy (multiple (for different types/languages etc.) doc.part + (pre-computed/trained) distributed query cache) can be built that match or outperform this complicated solution.
Good idea, however I find only one other approach to make it speedier.
AskASearchEngineGuru 1 year ago
Sorry, this strategy doesn't work well with long tail and personalized search load. The indexing cost (I'd consider cluster selection an indexing phase) is much higher as well. For aggregate performance, a much simpler caching strategy (multiple (for different types/languages etc.) doc.part + (pre-computed/trained) distributed query cache) can be built that match or outperform this complicated solution.
vicaya 4 years ago 2
The crusing capabilities of ac tive data clouds you mean?
One day it'll know the kind of stuff i want and i won't even have to make entries all the time. (Standard unified ratings data).
I'll also be able to talk to a bot wich wil adapt it's data personality as to know me better.
wildchildplasma 4 years ago