Hi,

in your case I'd use a mixed approach, like the one used in time series
(see also
http://www.slideshare.net/LuigiDellAquila/orientdb-time-representation I
hope it will answer question B). In particular I'd take into consideration
the idea of storing two different time trees:
- one having year as root node:

year -> month -> query

- one having single query or query groups as root nodes:

query -> year -> month
queryCategory -> query -> year -> month

this will give you a lot of flexibility in the analysis. On top of this you
can add other classifications.
These structures are very handy to store aggregate information and avoid
continuous reads, especially if you are interested in having a finer
grained structures (eg. day -> hour -> minute -> second)

In my opinion using a hash index to quickly find root nodes is not a bad
idea in general, write performance will not be affected too much. The real
disadvantage of hash indexes is that they do not allow range queries (but I
don't think it will be a problem for you)
If you could share some additional details about insert/update/select rate
you expect, I would give you some more advices on how to dimension it.

About question c (internals), every vertex and edge in OrientDB is
represented as a document at the storage level. A document has a unique ID
that is a physical pointer to the position of the record. A document can
contain nested information (eg. hierarchical embedded documents). Vertexes
and edges are linked together using physical pointers, so traversal
operations are very cheap ( formally O(1) ).

If you need any additional details about particular points, please let me
know

Thanks

Luigi



2015-05-17 23:03 GMT+02:00 MV-dev1 <[email protected]>:

>
> <https://lh3.googleusercontent.com/-kWTPh3ZCwTI/VVkCFyBkRWI/AAAAAAAAAEk/PC8AqnYVncs/s1600/ODBQuestionEx2b.jpg>
> Actually, this is what the pure graph would really look like.
>
>
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