anchor.diff:  link:http://giraph.apache.org/[Giraph]. The scope of this tutorial does not permit it to delve into
   "graph processors", but the short story is that the same Gremlin statement we wrote in the examples above can be
   executed to run in distributed fashion over Spark or Hadoop. The changes required to the code to do this are not
  -in the traversal itself, but in the definition of the `TraversalSource`. You can again see why we encourage, graph
  +in the traversal itself, but in the definition of the `TraversalSource`. You can again see why we encourage graph
   operations to be executed through that class as opposed to just using `Graph`. You can read more about these
   features in this section on link:http://tinkerpop.apache.org/docs/x.y.z/reference/#hadoop-gremlin[hadoop-gremlin].
   
  
positive.code: ----
  
  The data is contained in a tab-delimited structure where vertices are Wikipedia users and edges from one user to
  another implies a "vote" relationship. Here is the script to parse the file and generate the `Graph` instance using
  TinkerGraph:
  
  [source,groovy]
  
negative.code: ----
  
  There are a number of important things to consider in the above code. First, recall that `id` is
  "reserved" for special usage in TinkerPop and is a member of the enum, `T`. The "keys" supplied to the creation
  method are link:https://docs.oracle.com/javase/8/docs/technotes/guides/language/static-import.html[statically imported]
  to the console, which allows you to access them without having to specify their owning enum. Think of `id` as a
  shorthand form that enables a more fluid code style. You would normally refer to it as `T.id`, so without
  that static importing you would instead have to write:
  
  [gremlin-groovy]
  