anchor.diff:  ...
   ==>ripple
   ==>lop
  +
  +[gremlin-groovy]
  +----
  +graph = GraphFactory.open('conf/hadoop-gryo.properties')
  +g = graph.traversal(computer())
  +g.V().count()
  +g.V().out().out().values('name')
  +----
   
   IMPORTANT: The examples above do not use lambdas (i.e. closures in Gremlin-Groovy). This makes the traversal serializable and thus, able to be distributed to all machines in the Hadoop cluster. If a lambda is required in a traversal, then the traversal must be sent as a `String` and compiled locally at each machine in the cluster. The following example demonstrates the `:remote` command which allows for submitting Gremlin traversals as a `String`.
   
  
positive.code: gremlin.hadoop.graphInputFormat=org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoInputFormat
  gremlin.hadoop.graphOutputFormat=org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoOutputFormat
  gremlin.hadoop.jarsInDistributedCache=true
  gremlin.hadoop.inputLocation=tinkerpop-modern-vertices.kryo
  gremlin.hadoop.outputLocation=output
  #####################################
  # GiraphGraphComputer Configuration #
  
negative.code: gremlin> g.V().out().out().values('name')
  ==>lop
  ==>ripple
  
  [gremlin-groovy]
  ----
  graph = GraphFactory.open('conf/hadoop-gryo.properties')
  g = graph.traversal(computer(SparkGraphComputer))
  g.V().count()
  g.V().out().out().values('name')
  ----
  
  For using lambdas in Gremlin-Groovy, simply provide `:remote connect` a `TraversalSource` which leverages SparkGraphComputer.
  
  