I have person vertex, has_vehicle edge and vehicle vertex which models vehicle ownership use case. The graph path is person -> has_vehicle -> vehicle.
I want to implement a Gremlin query which associates a vehicle to a person only if
The person does not have a vehicle
AND
The input vehicle is not associated with a person yet.
I followed the fold-coalesce-unfold pattern and came out with following Gremlin query with nested coalesce
g.V().hasLabel('person').has('name', 'Tom').as('Tom').outE('has_vehicle').fold().coalesce(
__.unfold(), // check if Tom already have a vehicle
g.V().has('vehicle', 123).as('Vehicle').inE('has_vehicle').fold().coalesce(
__.unfold(), // check if vehicle 123 is already associated with a person
__.addE('has_vehicle').from('Tom').to('Vehicle') // associate the vehicle to Tom
)
)
Is there a way to eliminate the nested coalesce? If I have multiple criteria, it would be too complex to write the query.
This might be a case where a couple of where(not(...)) patterns, rather than nesting coalesce steps works well. For example, we might change the query as shown below.
g.V().hasLabel('person').has('name', 'Tom').as('Tom').
where(not(outE('has_vehicle'))).
V().has('vehicle', 123).as('Vehicle').
where(not(inE('has_vehicle'))).
addE('has_vehicle').from('Tom').to('Vehicle')
So long as the V steps do not fan out and yield multiple Tom or Vehicle nodes that should work and is easy to extend by adding more to the where filters as needed.
As as a side note, the not steps used above should work even if not wrapped by where steps, but I tend to find it just reads better as written.
This rewrite does make an assumption that you are able to tolerate the case where Tom already has a car and the query just ends there. In that case no vertex or edge will be returned. If you did a toList to run the query you would get an empty list back in that case however to indicate nothing was done.
Related
I'm trying to build a suggestion engine using Gremlin but I'm having a hard time trying to understand how to create a query when multiple nodes are connected by different intermediate nodes.
Playground:
https://gremlify.com/alxrvpfnlo9/2
Graph:
In this simple example I have two users, both like cheese and bread. But User2 also likes sandwiches, which seems a good suggestion for User1 as he shares some common interests with User2
The question I'm trying to answer is: "What can I suggest to User1 based on what other users like?"
The answer should be: Everything that other users that like the same things as User1 likes, but excluding what User1 already like. In this case it should return a sandwich
So far I have this query:
g.V(2448600).as('user1')
.out().as('user1Likes')
.in().where(neq('user1')) // to get to User2
.out().where(neq('user1Likes')) // to get to what User2 likes but excluding items that User1 likes
Which returns:
Sandwich, bread, Sandwich (again), cheese
I think that it returns that data because it walks through the graph by the Cheese node first, so Bread is not included in the 'user1Likes' list, thus not excluded in the final result. Then it walks through the Bread node, so cheese in this case is a good suggestion.
Any ideas/suggestions on how to write that query? Take into consideration that it should escalate to multiple users-ingredients
I suggest that you model your problem differently. Normally the vertex label is used to determine the type of the entity. Not to identify the entity. In your case, I think you need two vertex labels: "user" and "product".
Here is the code that creates the graph.
g.addV('user').property('name', 'User1').as('user1').
addV('user').property('name', 'User2').as('user2').
addV('product').property('name', 'Cheese').as('cheese').
addV('product').property('name', 'Bread').as('bread').
addV('product').property('name', 'Sandwiches').as('sandwiches').
addE('likes').from('user1').to('cheese').
addE('likes').from('user1').to('bread').
addE('likes').from('user2').to('cheese').
addE('likes').from('user2').to('bread').
addE('likes').from('user2').to('sandwiches')
And here is the traversal that gets the recommended products for "User1".
g.V().has('user', 'name', 'User1').as('user1').
out('likes').aggregate('user1Likes').
in('likes').
where(neq('user1')).
dedup().
out('likes').
where(without('user1Likes')).
dedup()
The aggregate step aggregates all the products liked by "User1" into a collection named "user1Likes".
The without predicate passes only the vertices that are not within the collection "user1Likes".
I'm working on a recommendation system that recommends other users. The first results should be the most "similar" users to the "searcher" user. Users respond to questions and the amount of questions responded in the same way is the amount of similarity.
The problem is that I don't know how to write the query
So in technical words I need to sort the users by the amount of edges that has specific property values, I tried with this query, I thought it should work but it doesn't work:
let query = g.V().hasLabel('user');
let search = __;
for (const question of searcher.questions) {
search = search.outE('response')
.has('questionId', question.questionId)
.has('answerId', question.answerId)
.aggregate('x')
.cap('x')
}
query = query.order().by(search.unfold().count(), order.asc);
Throws this gremlin internal error:
org.apache.tinkerpop.gremlin.process.traversal.step.util.BulkSet cannot be cast to org.apache.tinkerpop.gremlin.structure.Vertex
I also tried with multiple .by() for each question, but the result was not ordered by the amount of coincidence.
How can I write this query?
When you cap() an aggregate() it returns a BulkSet which is a Set that has counts for how many times each object exists in that Set. It behaves like a List when you iterate through it by unrolling each object the associated size of the count. So you get your error because the output of cap('x') is a BulkSet but because you are building search in a loop you are basically just calling outE('response') on that BulkSet and that's not valid syntax as has() expects a graph Element such as a Vertex as indicated by the error.
I think you would prefer something more like:
let query = g.V().hasLabel('user').
outE('response');
let search = [];
for (const question of searcher.questions) {
search.push(has('questionId', question.questionId).
has('answerId', question.answerId));
}
query = query.or(...search).
groupCount().
by(outV())
order(local).by(values, asc)
I may not have the javascript syntax exactly right (and I used spread syntax in my or() to just convey the idea quickly of what needs to happen) but basically the idea here is to filter edges that match your question criteria and then use groupCount() to count up those edges.
If you need to count users who have no connection then perhaps you could switch to project() - maybe like:
let query = g.V().hasLabel('user').
project('user','count').
by();
let search = [];
for (const question of searcher.questions) {
search.push(has('questionId', question.questionId).
has('answerId', question.answerId));
}
query = query.by(outE('response').or(...search).count()).
order().by('count', asc);
fwiw, I think you might consider a different schema for your data that might make this recommendation algorithm a bit more graph-like. A thought might be to make the question/answer a vertex (a "qa" label perhaps) and have edges go from the user vertex to the "qa" vertex. Then users directly link to the question/answers they gave. You can easily see by way of edges, a direct relationship, which users gave the same question/answer combination. That change allows the query to flow much more naturally when asking the question, "What users answered questions in the same way user 'A' did?"
g.V().has('person','name','A').
out('responds').
in('responds').
groupCount().
order(local).by(values)
With that change you can see that we can rid ourselves of all those has() filters because they are implicitly implied by the "responds" edges which encode them into the graph data itself.
I have designed a model in Neo4j in order to get paths from one station to another including platforms/legs involved. The model is depicted down here. Basically, I need a query to take me from NBW to RD. also shows the platforms and legs involved. I am struggling with the query. I get no result. Appreciate if someone helps.
Here is my cypher statement:
MATCH p = (a:Station)-[r:Goto|can_board|can_alight|has_platfrom*0..]->(c:Station)
WHERE (a.name='NBW')
AND c.name='RD'
RETURN p
Model:
As mentioned in the comments, in Cypher you can't use a directed variable-length relationship that uses differing directions for some of the relationships.
However, APOC Procedures just added the ability to expand based on sequences of relationships. You can give this a try:
MATCH (start:station), (end:station)
WHERE start.name='NBW' AND end.name='THT'
CALL apoc.path.expandConfig(start, {terminatorNodes:[end], limit:1,
relationshipFilter:'has_platform>, can_board>, goto>, can_alight>, <has_platform'}) YIELD path
RETURN path
I added a limit so that only the first (and shortest) path to your end station will be returned. Removing the limit isn't advisable, since this will continue to repeat the relationships in the expansion, going from station to station, until it finds all possible ways to get to your end station, which could hang your query.
EDIT
Regarding the new model changes, the reason the above will not work is because relationship sequences can't contain a variable-length sequence within them. You have 2 goto> relationships to traverse, but only one is specified in the sequence.
Here's an alternative that doesn't use sequences, just a whitelisting of allowed relationships. The spanningTree() procedure uses NODE_GLOBAL uniqueness so there will only be a single unique path to each node found (paths will not backtrack or revisit previously-visited nodes).
MATCH (start:station), (end:station)
WHERE start.name='NBW' AND end.name='RD'
CALL apoc.path.spanningTree(start, {terminatorNodes:[end], limit:1,
relationshipFilter:'has_platform>|can_board>|goto>|can_alight>|<has_platform'}) YIELD path
RETURN path
Your query is directed --> and not all of the relationships between your two stations run in the same direction. If you remove the relationship direction you will get a result.
Then once you have a result I think something like this could get you pointed in the right direction on extracting the particular details from the resulting path once you get that working.
Essentially I am assuming that everything you are interested in is in your path that is returned you just need to filter out the different pieces that are returned.
As #InverseFalcon points out this query should be limited in a larger graph or it could easily run away.
MATCH p = (a:Station)-[r:Goto|can_board|can_alight|has_platfrom*0..]-(c:Station)
WHERE (a.name='NBW')
AND c.name='THT'
RETURN filter( n in nodes(p) WHERE 'Platform' in labels(n)) AS Platforms
I have build a small graph where all the screens are connected and the flow of the screen varies based on the system/user. So the system/user is the relationship type.
I am looking to fetch all nodes that are linked with a certain relation ship from a starting screen. I don't care about the depth since i don't know the depth of the graph.
Something like this, but the below query takes ever to get the result and its returning incorrect connections not matching the attribute {path:'CC'}
match (n:screen {isStart:true})-[r:NEXT*0..{path:'CC'}]-()
return r,n
A few suggestions:
Make sure you have created an index for :screen(isStart):
CREATE INDEX ON :screen(isStart);
Are you sure you want to include 0-length paths? If not, take out 0.. from your query.
You did not specify the directionality of the :NEXT relationships, so the DB has to look at both incoming and outgoing :NEXT relationships. If appropriate, specify the directionality.
To minimize the number of result rows, add a WHERE clause that ensures that the current path cannot be extended further.
Here is a proposed query that combines the last 3 suggestions (fix it up to suit your needs):
MATCH (n:screen {isStart:true})-[r:NEXT* {path:'CC'}]->(x)
WHERE NOT (x)-[:NEXT {path:'CC'}]->()
return r,n;
A totally neo4j noob is talking here,
I like to create a graph to store a set of users, a typical user is as follows:
CREATE
(node_1 {FullName:"Peter Parker",FirstName:"peter",FamilyName:"parker"}),
(node_2 {Address:"Newyork",CountryCode:"US"}),
(node_3 {Location:"Hidden"}),
(node_4 {phoneNumber:11111}),
(node_5 {InternetEmailAddress:"peter#peterland.com")
now the problem is,
Every time I execute this I add 5 more nodes.
I know I need to use a unique key, but all example I saw can use a unique key for a specific node. So how can I make sure a user doesn't get added if it already exists(I can use email address as unique key).
how do I update the nodes if some changes occur. for example, after a week I want to update the graph to contain the following instead of the previous one.(no duplicates)
CREATE(node_1 {FullName:"Peter Parker",FirstName:"peter",FamilyName:"parker"}),(node_2 {Address:"Newyork",CountryCode:"US"}),(node_3 {Location:"public"}),(node_4 {phoneNumber:11111}),(node_5 {InternetEmailAddress:"peter#peterland.com"),(node_6 {status:"Jailed"})
(NOTE the new update changed location to "public" and added a new node for peter
Seeing as you had a load of nodes anyway.
Some of the data you have modelled as Nodes are probably properties as the other answer suggests, some are possibly correctly modelled as Nodes and one could probably form the or a part of the relationship.
Location public/hidden can be modelled in one of three ways, as a property on the Person, as a property between the Person and the Location or as the relationship type. To understand that first you need to have a relationship.
Your address at the moment is another Node, I think this is correct, but possibly you would want two nodes, related something like this:
(s:State)-[:IN_COUNTRY]-(c:Country)
YMMV and clearly that a US centric model, but you can extend it easilly enough.
Now you could create Peter with a LIVES_IN relationship:
CREATE (p:Person{fullName:"Peter Parker"}), (s:State{name:"New York"}), (c:Country{code:"US"}),
(p)-[:LIVES_IN]->(s), (s)-[:IN_COUNTRY]->(c)
For speed you are better off modelling two relationships which could be LIVES_IN_PUBLIC and LIVES_IN_HIDDEN which means to perform that update that you want above then you have to delete the one and create the other. However, if speed is not of the essence, it is common also to use properties on the relationship.
CREATE (p:Person{fullName:"Peter Parker"}), (s:State{name:"New York"}), (c:Country{code:"US"}),
(p)-[:LIVES_IN{public:false}]->(s), (s)-[:IN_COUNTRY]->(c)
So your complete Q&A:
CREATE (p:Person {fullName:"Peter Parker",firstName:"peter",familyName:"parker", phoneNumber:1111, internetEmailAddress:"peter#peterland.com"}),
(s:State {name:"New York"}), (c:Country {code:"US"}),
(p)-[:LIVES_IN{public:false}]->(s), (s)-[:IN_COUNTRY]-(c)
MATCH (p:Person {internetEmailAddress:"peter#peterland.com"})-[li:LIVES_IN]->()
SET li.public = true, p.status = "jailed"
When adding other People you probably do not want to recreate States and Countries, rather you want to match them, and possibly Merge them, but we'll stick to Create.
MATCH (s:State{name:"New York"})
CREATE (p:Person{name:"John Smith", internetEmailAddress:"john#google.com"})-[:LIVES_IN{public:false}]->(s)
John Smith now implicitly lives in the US too as you can follow the relationship through the State Node.
Treatise complete.
I think you're modeling your data incorrectly here - you're setting up each property of the person as a separate node, which is not a good idea. You don't have any linkages between those nodes, so with this data pattern, later on you won't be able to tell what Peter Parker's address is. You're also not using node labels, which I think could really help here.
The quick question to your answer about updating nodes is that you have to MATCH them, then use SET to modify a property. So if you had a person, you might do this:
MATCH (p:Person { FullName: "Peter Parker" })
SET p.Address = "123 Fake Street"
RETURN p;
But notice I'm making assumptions about the way your data is structured. I'll take that same data you provided, this might be a better way of creating it:
CREATE (node_1:Person {FullName:"Peter Parker",
FirstName:"peter",
FamilyName:"parker",
Address:"Newyork",CountryCode:"US",
Location:"Hidden",
phoneNumber:11111,
InternetEmailAddress:"peter#peterland.com"});
The difference with this suggestion is that I'm putting all the properties into a single node (instead of one property per node) and I'm applying the Person label to the node.
If you structured the data like this, then the update query I provided would work. Structuring the data like you have it, it's not possible to update Peter Parker's address, because there's no relationship between your node_1 and node_2