Anatomy of a graph traversal
The anatomy of a graph traversal explores the results of each traversal step.
Structure of a graph traversal
Simple traversals can be complex, but generally do not employ specialized techniques such as recursion or branching.
Break down the chain of a graph traversal into traversal
steps:
g.V().hasLabel('recipe').count()
This graph traversal to find
the number of recipes in the graph has four parts:- The graph traversal
g
g
will return an error if run alone- All vertices are gathered with
V()
- All the vertices will be returned. A sample of the
result:
gremlin> g.V() ==>v[{~label=ingredient, member_id=18, community_id=1989847424}] ==>v[{~label=ingredient, member_id=19, community_id=1989847424}] ==>v[{~label=ingredient, member_id=16, community_id=1989847424}] ==>v[{~label=ingredient, member_id=17, community_id=1989847424}] ==>v[{~label=ingredient, member_id=22, community_id=1989847424}] ==>v[{~label=ingredient, member_id=13, community_id=1989847424}] ==>v[{~label=meal, member_id=25, community_id=1989847424}] ==>v[{~label=ingredient, member_id=24, community_id=1989847424}] ==>v[{~label=recipe, member_id=14, community_id=1878171264}] ==>v[{~label=recipe, member_id=21, community_id=1878171264}] ==>v[{~label=recipe, member_id=19, community_id=1878171264}] ==>v[{~label=meal, member_id=27, community_id=1989847424}] ==>v[{~label=recipe, member_id=20, community_id=1878171264}] ==>v[{~label=meal, member_id=26, community_id=1989847424}] ==>v[{~label=book, member_id=13, community_id=1878171264}] ==>v[{~label=book, member_id=10, community_id=1878171264}] ==>v[{~label=book, member_id=11, community_id=1878171264}] ==>v[{~label=author, member_id=1, community_id=1878171264}] ==>v[{~label=author, member_id=0, community_id=1878171264}] ==>v[{~label=author, member_id=3, community_id=1878171264}] ==>v[{~label=ingredient, member_id=2, community_id=1989847424}] ==>v[{~label=author, member_id=2, community_id=1878171264}]
- Filter out the vertices labeled as a recipe with
hasLabel('recipe')
- Only the vertices that are recipes will be
returned:
gremlin> g.V().hasLabel('recipe') ==>v[{~label=recipe, member_id=14, community_id=1878171264}] ==>v[{~label=recipe, member_id=21, community_id=1878171264}] ==>v[{~label=recipe, member_id=19, community_id=1878171264}] ==>v[{~label=recipe, member_id=20, community_id=1878171264}] ==>v[{~label=recipe, member_id=17, community_id=1878171264}] ==>v[{~label=recipe, member_id=18, community_id=1878171264}] ==>v[{~label=recipe, member_id=15, community_id=1878171264}] ==>v[{~label=recipe, member_id=16, community_id=1878171264}]
- Count the number of vertices with
count()
- The number of vertices returned from the last traversal step is
totalled:
gremlin> g.V().hasLabel('recipe').count() ==>8
The vertex information returned consists of three parts:
- member_id
- vertex ID within a group
- community_id
- community ID within a graph
- label
- The specified vertex label
member_id
and
community_id
for each vertex. The relationship is:- A graph is a collection of disjoint communities
- A community is a collection of disjoint member vertices
member_id
is set to to a value within each community.Graph traversal with edges
Before trying the traversals displayed below, run the following script either in Studio
(copy and paste) or Gremlin console (:load
/tmp/generateReviews.groovy):
// reviewer vertices
johnDoe = graph.addVertex(label, 'reviewer', 'name','John Doe')
johnSmith = graph.addVertex(label, 'reviewer', 'name', 'John Smith')
janeDoe = graph.addVertex(label, 'reviewer', 'name','Jane Doe')
sharonSmith = graph.addVertex(label, 'reviewer', 'name','Sharon Smith')
betsyJones = graph.addVertex(label, 'reviewer', 'name','Betsy Jones')
beefBourguignon = g.V().has('recipe', 'name','Beef Bourguignon').tryNext().orElseGet {graph.addVertex(label, 'recipe', 'name', 'Beef Bourguignon')}
spicyMeatLoaf = g.V().has('recipe', 'name','Spicy Meatloaf').tryNext().orElseGet {graph.addVertex(label, 'recipe', 'name', 'Spicy Meatloaf')}
carrotSoup = g.V().has('recipe', 'name','Carrot Soup').tryNext().orElseGet {graph.addVertex(label, 'recipe', 'name', 'Carrot Soup')}
// reviewer - recipe edges
johnDoe.addEdge('rated', beefBourguignon, 'timestamp', Instant.parse('2014-01-01T00:00:00.00Z'), 'stars', 5, 'comment', 'Pretty tasty!')
johnSmith.addEdge('rated', beefBourguignon, 'timestamp', Instant.parse('2014-01-23T00:00:00.00Z'), 'stars', 4)
janeDoe.addEdge('rated', beefBourguignon, 'timestamp', Instant.parse('2014-02-01T00:00:00.00Z'), 'stars', 5, 'comment', 'Yummy!')
sharonSmith.addEdge('rated', beefBourguignon, 'timestamp', Instant.parse('2015-01-01T00:00:00.00Z'), 'stars', 3, 'comment', 'It was okay.')
johnDoe.addEdge('rated', spicyMeatLoaf, 'timestamp', Instant.parse('2015-12-31T00:00:00.00Z'), 'stars', 4, 'comment', 'Really spicy - be careful!')
sharonSmith.addEdge('rated', spicyMeatLoaf, 'timestamp',Instant.parse('2014-07-23T00:00:00.00Z'), 'stars', 3, 'comment', 'Too spicy for me. Use less garlic.')
janeDoe.addEdge('rated', carrotSoup, 'timestamp', Instant.parse('2015-12-30T00:00:00.00Z'), 'stars', 5, 'comment', 'Loved this soup! Yummy vegetarian!')
Any number of traversal steps can be chained into a traversal, filtering and transforming
the graph data as required. In some cases, edges will be the result, and perhaps unexpected.
Consider the following
traversal:
g.V().hasLabel('recipe').has('name', 'Beef Bourguignon').inE().values('comment')
This
graph traversal begins as the last traversal did with
g.V().hasLabel('recipe')
. It is then followed by:- A traversal step to pick only the vertices with the recipe title specified
- The filter should capture one recipe if recipe titles are
unique.
gremlin> g.V().hasLabel('recipe').has('name', 'Beef Bourguignon') ==>v[{~label=recipe, member_id=14, community_id=1878171264}]
- A traveral step that retrieves incoming edges
- Notice from the two edges sampled from the complete result that edges with any label
are filtered with this step. Using
inE('rated')
would be more precise if the target result are only ratings.gremlin> g.V().hasLabel('recipe').has('name', 'Beef Bourguignon').inE() ==>e[{out_vertex={~label=reviewer, member_id=4, community_id=857859584}, local_id=ca461ec0-0e7e-11e6-b5e4-0febe4822aa4, in_vertex={~label=recipe, member_id=14, community_id=1878171264}, ~type=rated}] [{~label=reviewer, member_id=4, community_id=857859584}-rated->{~label=recipe, member_id=14, community_id=1878171264}] ==>e[{out_vertex={~label=reviewer, member_id=5, community_id=857859584}, local_id=ca5bf0b0-0e7e-11e6-b5e4-0febe4822aa4, in_vertex={~label=recipe, member_id=14, community_id=1878171264}, ~type=rated}] [{~label=reviewer, member_id=5, community_id=857859584}-rated->{~label=recipe, member_id=14, community_id=1878171264}] ==>e[{out_vertex={~label=reviewer, member_id=6, community_id=857859584}, local_id=ca72d410-0e7e-11e6-b5e4-0febe4822aa4, in_vertex={~label=recipe, member_id=14, community_id=1878171264}, ~type=rated}] [{~label=reviewer, member_id=6, community_id=857859584}-rated->{~label=recipe, member_id=14, community_id=1878171264}] ==>e[{out_vertex={~label=reviewer, member_id=7, community_id=857859584}, local_id=ca8a0590-0e7e-11e6-b5e4-0febe4822aa4, in_vertex={~label=recipe, member_id=14, community_id=1878171264}, ~type=rated}] [{~label=reviewer, member_id=7, community_id=857859584}-rated->{~label=recipe, member_id=14, community_id=1878171264}] ==>e[{out_vertex={~label=author, member_id=0, community_id=1878171264}, local_id=524504c0-0e7b-11e6-b5e4-0febe4822aa4, in_vertex={~label=recipe, member_id=14, community_id=1878171264}, ~type=created}] [{~label=author, member_id=0, community_id=1878171264}-created->{~label=recipe, member_id=14, community_id=1878171264}]
- Parsing out the comment property from the rated edges
- Here, the
inE()
is specified with the edge labelrated
. The property values are retrieved for the property keycomment
:gremlin> g.V().hasLabel('recipe').has('name', 'Beef Bourguignon').inE('rated').values('comment') ==>Yummy! ==>Pretty tasty! ==>It was okay.
Building graph traversals one step at a time can yield interesting results and insight into how to create traversals.
The path of a graph traversal
A traversal step exists that will show the path taken by a graph traversal. First, find the
results for a traversal that answers the question about what recipes that list beef and
carrots as ingredients are included in the cookbooks, given the cookbook and recipe
title?
gremlin> g.V().hasLabel('ingredient').has('name',within('beef','carrots')).in().as('Recipe').
out().hasLabel('book').as('Book').
select('Book','Recipe').by('name').
by('name')
==>[Book:The Art of French Cooking, Vol. 1, Recipe:Beef Bourguignon]
==>[Book:The Art of Simple Food: Notes, Lessons, and Recipes from a Delicious Revolution, Recipe:Carrot Soup]
One expects that the traversal path from
ingredient
to
recipe
to book
. To check if this assumption is correct,
add path()
to the end of the
traversal.gremlin> g.V().hasLabel('ingredient').has('name',within('beef','carrots')).
in().as('Recipe').
out().hasLabel('book').as('Book').
select('Book','Recipe').
by('name').by('name').path()
==>[v[{~label=ingredient, member_id=22, community_id=1878171264}],
v[{~label=recipe, member_id=14, community_id=1878171264}],
v[{~label=book, member_id=10, community_id=1878171264}],
{Book=The Art of French Cooking, Vol. 1, Recipe=Beef Bourguignon}]
==>[v[{~label=ingredient, member_id=21, community_id=1989847424}],
v[{~label=recipe, member_id=20, community_id=1878171264}],
v[{~label=book, member_id=13, community_id=1878171264}],
{Book=The Art of Simple Food: Notes, Lessons, and Recipes from a Delicious Revolution, Recipe=Carrot Soup}]
For
each case, notice that the traversal does follow the expected path.Traversal metrics
In addition to tracing the output of each graph traversal step, metrics can produce
interesting insights as well. To add metrics to the last traversal shown, use some
additional chained
steps:
gremlin> g.V().hasLabel('recipe').has('name', 'Beef Bourguignon').inE('rated').values('comment').profile()
==>Traversal Metrics
Step Count Traversers Time (ms) % Dur
=============================================================================================================
DsegGraphStep([~label.eq(recipe), name.eq(Beef ... 1 1 0.979 73.00
query-optimizer 0.184
retrieve-new 0.115
iterator-setup 0.390
DsegVertexStep(IN,[rated],edge) 4 4 0.286 21.37
query-optimizer 0.080
retrieve-new 0.014
iterator-setup 0.062
DsegPropertiesStep([comment],value) 3 3 0.075 5.63
>TOTAL - - 1.342 -
The
type of traversal step is listed, along with the number of traversers and the time to
complete the traversal step. If a traversal step can be processed in parallel, multiple
traversers will be employed to retrieve data. Some traversal steps are graph-global
requiring retrieval from the entire graph; DsegGraphStep is a graph-global retrieval that
finds vertices that match certain conditions. Other traversal steps are graph-local walks
and can be processed in parallel; DsegVertexStep is a graph-local walk that walks through
the graph along constrained paths. DSE Graph uses automatic query optimization to determine
the traversal strategies to efficiently use any index structures that exist.Looking at the metrics, the question of performance comes to mind. For instance, is there
any way to optimize the traversal shown above? In fact, a simple modification results in a
time
savings:
==>Traversal Metrics Step Count Traversers Time (ms) % Dur ============================================================================================================= DsegGraphStep([~label.eq(recipe), name.eq(Beef ... 1 1 0.733 70.62 query-optimizer 0.143 retrieve-new 0.059 iterator-setup 0.289 DsegVertexStep(IN,[rated],edge) 4 4 0.241 23.29 query-optimizer 0.083 retrieve-new 0.006 iterator-setup 0.049 DsegPropertiesStep([comment],value) 3 3 0.063 6.10 >TOTAL - - 1.038 -The change made is subtle. Two traversal steps,
hasLabel('recipe').has('name', 'Beef
Bourguignon')
have been replaced by one traversal step, has('recipe',
'name', 'Beef Bourguignon')
. Although each measurement can vary, generally the
second traversal will outperform the first traversal.