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Gremlin Tutorial

This example is a gremlin tutorial that shows how to explore the graph with sample queries. It also shows how to make recommendations using collaborative filtering.

Prerequisite

This tutorial assumes you already have your environment setup. To setup a new environment, create an Amazon Neptune Cluster.

See the following links on how to create an Amazon Neptune Cluster for Gremlin and setup IAM authentication:

You will also need to load the data files into an S3 bucket.

Use Case

In this tutorial, we'll traverse console game preferences among a small set of gamers and games. We'll explore commonality, preferences and make potential game recommendations. These queries are for the purposes of learning gremlin and Amazon Neptune.

cloudformation

Step 1 (Load Data Sample data)

Game & Player Vertices (~id,~label,GamerAlias:String,ReleaseDate:Date,GameGenre:String,ESRBRating:String,Developer:String,Platform:String,GameTitle:String)

curl -X POST \
    -H 'Content-Type: application/json' \
    http://your-neptune-endpoint:8182/loader -d '
    { 
      "source" : "s3://your-s3-bucket/vertex.txt", 
      "iamRoleArn" : "arn:aws:iam::account-id:role/role-name",
      "format" : "csv", 
      "region" : "us-east-1", 
      "failOnError" : "FALSE"
    }'

Edges (~id, ~from, ~to, ~label, weight:Double)

curl -X POST \
    -H 'Content-Type: application/json' \
    http://your-neptune-endpoint:8182/loader -d '
    { 
      "source" : "s3://your-s3-bucket/recommendation/edges.txt", 
      "iamRoleArn" : "arn:aws:iam::account-id:role/role-name",
      "format" : "csv", 
      "region" : "us-east-1", 
      "failOnError" : "FALSE"
    }'

Tip. Alternatively, you could load all of the files by loading the entire directory

curl -X POST \
    -H 'Content-Type: application/json' \
    http://your-neptune-endpoint:8182/loader -d '
    { 
      "source" : "s3://your-s3-bucket/", 
      "iamRoleArn" : "arn:aws:iam::account-id:role/role-name",
      "format" : "csv", 
      "region" : "us-east-1", 
      "failOnError" : "FALSE"
    }'

Tip. Upon executing each curl command, Neptune will return the loadId associated with each request. You can check the status of your load with the following command:

curl http://your-neptune-endpoint:8182/loader?loadId=[loadId value]

For more information about loading data into Amazon Neptune visit: https://docs.aws.amazon.com/neptune/latest/userguide/bulk-load.html

Sample Queries

Query for a particular vertex (gamer)

gremlin> g.V().hasId('Luke').valueMap()
==>{GamerAlias=[skywalker123]}

gremlin> g.V().has("GamerAlias","skywalker123").valueMap()
==>{GamerAlias=[skywalker123]}

gremlin> g.V().has('GamerAlias','skywalker123')
==>v[Luke]

Sample some of the edges (limit 5)

gremlin> g.E().limit(5)
==>e[e25][Luke-likes->SuperMarioOdyssey]
==>e[e26][Mike-likes->SuperMarioOdyssey]
==>e[e8][Mike-likes->CallOfDutyBO4]
==>e[e1][Luke-likes->HorizonZeroDawn]
==>e[e9][Mike-likes->GranTurismoSport]

Sample some of the vertices (limit 4)

gremlin> g.V().limit(4)
==>v[SuperMarioOdyssey]
==>v[Luke]
==>v[Emma]
==>v[MarioKart8]

Count the in-degree centrality of incoming edges to each vertex

gremlin> g.V().group().by().by(inE().count())
==>{v[HorizonZeroDawn]=2, v[Luke]=0, v[ARMS]=2, v[Ratchet&Clank]=3, v[SuperMarioOdyssey]=3, v[GravityRush]=2, v[CallOfDutyBO4]=1, v[MarioKart8]=3, v[Fifa18]=1, v[Nioh]=1, v[Mike]=0, v[Knack]=2, v[Lina]=0, v[TombRaider]=2, v[GranTurismoSport]=2, v[Emma]=0}

Count the out-degree centrality of outgoing edges from each vertex

gremlin> g.V().group().by().by(outE().count())
==>{v[HorizonZeroDawn]=0, v[Luke]=8, v[ARMS]=0, v[Ratchet&Clank]=0, v[SuperMarioOdyssey]=0, v[GravityRush]=0, v[CallOfDutyBO4]=0, v[MarioKart8]=0, v[Fifa18]=0, v[Nioh]=0, v[Mike]=8, v[Knack]=0, v[Lina]=6, v[TombRaider]=0, v[GranTurismoSport]=0, v[Emma]=2}

Count the out-degree centrality of outgoing edges from each vertex by order of degree

gremlin> g.V().project("v","degree").by().by(bothE().count()).order().by(select("degree"), decr)
==>{v=v[Luke], degree=8}
==>{v=v[Mike], degree=8}
==>{v=v[Lina], degree=6}
==>{v=v[SuperMarioOdyssey], degree=3}
==>{v=v[MarioKart8], degree=3}
==>{v=v[Ratchet&Clank], degree=3}
==>{v=v[Emma], degree=2}
==>{v=v[HorizonZeroDawn], degree=2}
==>{v=v[GranTurismoSport], degree=2}
==>{v=v[ARMS], degree=2}
==>{v=v[GravityRush], degree=2}
==>{v=v[TombRaider], degree=2}
==>{v=v[Knack], degree=2}
==>{v=v[Fifa18], degree=1}
==>{v=v[Nioh], degree=1}
==>{v=v[CallOfDutyBO4], degree=1}

Return only the vertices that are games

gremlin> g.V().hasLabel('game')
==>v[Mario+Rabbids]
==>v[ARMS]
==>v[HorizonZeroDawn]
==>v[GranTurismoSport]
==>v[Ratchet&Clank]
==>v[Fifa18]
==>v[GravityRush]
==>v[Nioh]
==>v[TombRaider]
==>v[CallOfDutyBO4]
==>v[Knack]
==>v[SuperMarioOdyssey]
==>v[MarioKart8]

Return only the vertices that are gamers

gremlin> g.V().hasLabel('person')
==>v[Luke]
==>v[Emma]
==>v[Lina]
==>v[Mike]

Return counts of games grouped by game genre

gremlin> g.V().hasLabel('game').groupCount().by("GameGenre")
==>{Shooter=2, Action=3, Adventure=5, Racing=2, Sports=1}

Return counts of games grouped by developer

gremlin> g.V().hasLabel('game').groupCount().by("Developer")
==>{Activision=1, Nintendo=3, Square Enix=1, Guerrilla Games=1, Sony Interactive Entertainment=2, Insomniac Games=1, Electronic Arts=1, Project Siren=1, Ubisoft=1, Team Ninja=1}

Return counts of games grouped by Platform

gremlin> g.V().hasLabel('game').groupCount().by("Platform")
==>{PS4=9, Switch=4}

What is the average weighted rating of MarioKart8?

gremlin> g.V().hasLabel('game').has('GameTitle','MarioKart8').inE('likes').values('weight').mean()
==>0.6333333333333334

What games does skywalker123 like?

gremlin> g.V().has('GamerAlias','skywalker123').as('gamer').out('likes')
==>v[ARMS]
==>v[HorizonZeroDawn]
==>v[GranTurismoSport]
==>v[Ratchet&Clank]
==>v[Fifa18]
==>v[GravityRush]
==>v[SuperMarioOdyssey]
==>v[MarioKart8]

What games does skywalker123 like using weight (greater than)?

gremlin> g.V().has('GamerAlias','skywalker123').outE("likes").has('weight', P.gt(0.7f))
==>e[e17][Luke-likes->Mario+Rabbids]
==>e[e3][Luke-likes->Ratchet&Clank]

What games does skywalker123 like using weight (less than)?

gremlin> g.V().has('GamerAlias','skywalker123').outE("likes").has('weight', P.lt(0.5f))
==>e[e1][Luke-likes->HorizonZeroDawn]
==>e[e2][Luke-likes->GranTurismoSport]
==>e[e4][Luke-likes->Fifa18]
==>e[e5][Luke-likes->GravityRush]
==>e[e21][Luke-likes->MarioKart8]

Who else likes the same games?

gremlin> g.V().has('GamerAlias','skywalker123').out('likes').in('likes').dedup().values('GamerAlias')
==>forchinet
==>skywalker123
==>bringit32
==>smiles007

Who else likes these games (exclude yourself)?

gremlin> g.V().has('GamerAlias','skywalker123').as('TargetGamer').out('likes').in('likes').where(neq('TargetGamer')).dedup().values('GamerAlias')
==>forchinet
==>bringit32
==>smiles007

What are other game titles do other gamers like, who have commonality?

gremlin> g.V().has('GamerAlias','skywalker123').as('TargetGamer').out('likes').in('likes').where(neq('TargetGamer')).out('likes').dedup().values('GameTitle')
==>ARMs
==>HorizonZeroDawn
==>GranTurismoSport
==>Nioh
==>TombRaider
==>CallOfDutyBO4
==>SuperMarioOdyssey
==>MarioKart8
==>Ratchet&Clank
==>GravityRush
==>Knack

Which games might make sense to recommend to a specific gamer that they don't already like?

gremlin> g.V().has('GamerAlias','skywalker123').as('TargetGamer').out('likes').aggregate('self').in('likes').where(neq('TargetGamer')).out('likes').where(without('self')).dedup().values('GameTitle')
==>Nioh
==>TombRaider
==>CallOfDutyBO4
==>Knack

For more recommendation example queries or other gremlin recipes you can also visit: http://tinkerpop.apache.org/docs/current/recipes/#recommendation

Drop data

gremlin> g.V().drop().iterate()

License Summary

This sample code is made available under a modified MIT license. See the LICENSE file.