问题
I have an application that creates many thousands of graphs in memory per second. I wish to find a way to persist these for subsequent querying. They aren't particularly large (perhaps max ~1k nodes).
I need to be able to store the entire graph object including node attributes and edge attributes. I then need to be able to search for graphs within specific time windows based on a time attribute in a node.
Is there a simple way to coerce this data into neo4j ? I've yet to find any examples of this. Though I have found several python libs including an embedded neo4j and a rest client.
Is the common approach to manually traverse the graph and store it in that manner?
Are there any better persistence alternatives?
回答1:
Networkx has several serialization method.
In your case, I would choose graphml serialization :
http://networkx.github.io/documentation/latest/reference/readwrite.graphml.html
It's quite simple to use :
import networkx as nx
nx.write_graphml('/path/to/file')
To load it in Neo4j, provided you have Neo4j<2.0, you could use Tinkerpop Gremlin to load your graphml dump in Neo4J
g.loadGraphML('/path/to/file')
The Tinkerpop are quite useful - not only for serialization/deserialization.
It will allow you to use different graph database with a commion "dialect" (provided they have a "Blueprint" driver which most of them do)
来源：https://stackoverflow.com/questions/16639770/python-networkx-and-persistence-perhaps-in-neo4j