Database Reference
In-Depth Information
//in vertex
Vertex in = bgraph.addVertex(Math.random() +
"");
in.setProperty("twitter_tag",
in_twitter_tag);
in.setProperty("fname", in_fname);
in.setProperty("lname", in_lname);
//out vertex
Vertex out = bgraph.addVertex(Math.random() +
"");
out.setProperty("twitter_tag",
out_twitter_tag);
out.setProperty("fname", out_fname);
out.setProperty("lname", out_lname);
//assign edge
bgraph.addEdge(null, in, out,
edgeName);
7.
Finally we can call
commit
after successfully reading all records
from the
.csv
file and populating BatchGraph:
bgraph.commit();
Here batch size is the number of vertices and edges to be loaded before we invoke
the commit on the graph. One thing we should take care of is setting a moderate value
as the batch size to avoid heap size issues while processing a big graph having millions
or billions of edges.
The Supernode Problem
In the real world, big data-based graphs can be very large, and there can be a group of
vertices having a very high number of incident edges. In graph theory, such vertices are
called
supernodes
. With so many complex paths, a random traversal in a graph can lead
us to such supernodes, which would badly affect the system's performance.
Figure 7-21
shows my LinkedIn social graph, where the marked vertices can be
termed supernodes.
