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    <p>if you apply a strict Property Graph Model, you will find that you have great "data local" operations, like exploring the surrounding data of a node say, 5 hops deep along the relationships. However, global operations like "give me all nodes that have a name attribute of value 'Tom*'" require in a graph model a full scan of the data. This is in theory a limit. In practice (like in <a href="http://neo4j.org" rel="nofollow">http://neo4j.org</a>) the graph engine is combined with global indecies like Lucene, BerkelyDB or Cassandra, that can take care of this kind of data-global aspects that are often used in certain analytics scenarios.</p> <p>So, there is no real limitation, just a different way and different patterns to deal with global and local operations of your data. See <a href="http://wiki.neo4j.org/content/Domain_Modeling_Gallery" rel="nofollow">http://wiki.neo4j.org/content/Domain_Modeling_Gallery</a> for some examples of modeling domains in graphs, or even GIS examples like <a href="https://github.com/neo4j/neo4j-spatial/raw/master/src/site/pics/one-street.png" rel="nofollow">https://github.com/neo4j/neo4j-spatial/raw/master/src/site/pics/one-street.png</a> on the Open Street Map graph.</p> <p>For archiving vast amounts of data like Facebook, I would only store say the last month or so of status updates for fast retrieval and recommendations in a graph. The rest I would archive in solutions like Cassandra and just have a reference and key metrics of the archive in the graph on how to retrieve this "archived subgraph" if needed.</p>
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    1. USPeter Neubauer
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    1. COthnx for the reply :)
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      1. UScodecool
    2. COHave a look at this podcast about trinity: http://hanselminutes.com/280/microsoft-research-trinity-is-a-graph-database-and-a-distributed-parallel-platform-for-gra Facebook is mentioned explicitly as example there.
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