Hi there,
the key here is the independence of the traversal time from the total
size of the dataset. Max just wrote a great piece testing this, see
http://maxdemarzi.com/2014/02/12/online-payment-risk-management-with-neo4j/
for a gatling micro-benchmark. Instead, the traversal time in Neo4j
depends on the number of hops along the relationships as a constant
time operation.

In most SQL solutions query times depend on the number of joins in
your query, and normally increases exponentionally after 4 joins and
higher. Also, the total data size has a negative influence on query
times, as all data in an index needs to be visited for index lookups
(or at least the index B-Trees get deeper with increasing index size).

These are just rough guides, look at graphdatabases.com for a good
book on this, also Marko Rodriguez and I wrote some of this down in
http://arxiv.org/abs/1004.1001

/peter

G:  neubauer.peter
S:  peter.neubauer
P:  +46 704 106975
L:   http://www.linkedin.com/in/neubauer
T:   @peterneubauer

Neo4j 2.0.0                                  - (graphs)-[:FOR]->(everyone)
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On Thu, Feb 6, 2014 at 11:34 AM, Alireza Rezaei Mahdiraji
<[email protected]> wrote:
>
> Hi List,
>
>  I am doing some research with the goal to identify the following question:
> Where do graph databases go beyond relational databases? This could be in
> terms of
> operations for instance or other facilities. The critical question is to see
> what are the example
> graph queries which cannot be expressed in standard SQL clause (I mean
> select, from,
> where, order by, group by , etc not the newly fancy sql with a lot of
> programming structs).
>
> Please if you have some references, papers, books, etc or experiences share
> with me.
>
> Thanks,
> Best,
> Alireza
>
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