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Path Pattern

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pierredewilde edited this page May 16, 2012 · 7 revisions

Determining the path that was traversed is useful in many applications. For example:

  • What are the paths between X and Y according to a particular path description?
gremlin> g.v(1)
==>v[1]
gremlin> g.v(1).out.name
==>vadas
==>lop
==>josh
gremlin> g.v(1).out.name.path
==>[v[1], v[2], vadas]
==>[v[1], v[3], lop]
==>[v[1], v[4], josh]
gremlin> g.v(1).outE.inV.name.path               
==>[v[1], v[2], vadas]
==>[v[1], v[4], josh]
==>[v[1], v[3], lop]
gremlin> g.v(1).optimize(false).outE.inV.name.path
==>[v[1], e[7][1-knows->2], v[2], vadas]
==>[v[1], e[9][1-created->3], v[3], lop]
==>[v[1], e[8][1-knows->4], v[4], josh]

NOTE: In Gremlin, path optimization is automatically turned on. Path optimization will try to rewrite components of the pipeline to make them more concise/efficient. For instance, outE.inV can be compressed to out. As such, when the explicit steps are needed to be evaluated, optimize(false) should be called. Please see Traversal Optimization for more details.

If the path step is provided closures then, in a round robin fashion, the closures are evaluated over each object of the path and that post-processed path is returned.

gremlin> g.v(1).optimize(false).outE.inV.path{it.name}{it.weight}{it.name}
==>[marko, 0.5, vadas]
==>[marko, 0.4, lop]
==>[marko, 1.0, josh]
gremlin> g.v(1).optimize(false).as('x').outE.inV.loop('x'){it.loops < 3}.path{it.name}{it.weight}
==>[marko, 1.0, josh, 1.0, ripple]
==>[marko, 1.0, josh, 0.4, lop]
gremlin> g.v(1).optimize(false).as('x').outE.inV.loop('x'){it.loops < 3}.path{it.id}
==>[1, 8, 4, 10, 5]
==>[1, 8, 4, 11, 3]

g = new TinkerGraph()
g.loadGraphML('data/graph-example-2.xml')

Using the Grateful Dead graph diagrammed in Defining a More Complex Property Graph, lets determine the outE.inV paths that exist between Dark Star and Jerry Garcia. Be sure to restrict this query to some max number of loops (less than 4 for this example) to prevent an infinite loop. The query below states:

  • get vertex 89 (Dark Star)
  • get the out going edges of the current vertex
  • get the incoming (head) vertices of the current edge
  • if the object hasn’t been looped 4 times, loop back to outE (emit, the object if its Jerry Garcia)
  • get the paths taken from Dark Star to Jerry Garcia
gremlin> g.v(89).optimize(false).as('x').outE.inV.loop('x'){it.loops < 4}{it.object.getProperty('name')== 'Garcia'}.path
==>[v[89], e[7122][89-sung_by->340], v[340]]
==>[v[89], e[7021][89-followed_by->83], v[83], e[7158][83-sung_by->340], v[340]]
==>[v[89], e[7023][89-followed_by->206], v[206], e[7912][206-sung_by->340], v[340]]
==>[v[89], e[7006][89-followed_by->127], v[127], e[7786][127-sung_by->340], v[340]]
==>[v[89], e[7024][89-followed_by->49], v[49], e[7182][49-sung_by->340], v[340]]
==>[v[89], e[7025][89-followed_by->129], v[129], e[7102][129-sung_by->340], v[340]]
==>[v[89], e[7028][89-followed_by->130], v[130], e[7406][130-sung_by->340], v[340]]
==>[v[89], e[7018][89-followed_by->160], v[160], e[7082][160-sung_by->340], v[340]]
==>[v[89], e[7034][89-followed_by->91], v[91], e[7360][91-sung_by->340], v[340]]
==>[v[89], e[7017][89-followed_by->57], v[57], e[7126][57-sung_by->340], v[340]]
==>[v[89], e[7032][89-followed_by->140], v[140], e[7058][140-sung_by->340], v[340]]
==>[v[89], e[7015][89-followed_by->141], v[141], e[7108][141-sung_by->340], v[340]]
==>[v[89], e[7010][89-followed_by->94], v[94], e[7340][94-sung_by->340], v[340]]
==>[v[89], e[7029][89-followed_by->4], v[4], e[7064][4-sung_by->340], v[340]]
==>[v[89], e[7021][89-followed_by->83], v[83], e[1429][83-followed_by->5], v[5], e[7582][5-sung_by->340], v[340]]
==>[v[89], e[7021][89-followed_by->83], v[83], e[1434][83-followed_by->89], v[89], e[7122][89-sung_by->340], v[340]]
...
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