Is there a way that I can ensure that any creation of a vertex in janusgraph with a given set of properties only results in one such vertex being created?
Right now, what I do is I traverse the graph and ensure that the number of vertices I find with particular properties is only one. For example:
val g = graph.traversal
val vertices = g.V().has("type", givenType).has("name", givenName).toList
if (vertices.size > 1) {
// the vertex is not unique, cannot add vertex
}
This can be done with the so called get or create traversal which is described in TinkerPop's Element Existence recipe and in the section Using coalesce to only add a vertex if it does not exist of the Practical Gremlin book.
For your example, this traversal would look like this:
g.V().has("type", givenType).has("name", givenName).
fold().
coalesce(unfold(),
addV("yourVertexLabel").
property("type", givenType).
property("name", givenName))
Note however, that it depends on the graph provider whether this is an atomic operation or not. In your case of JanusGraph, the existence check and the conditional vertex addition are executed with two different operations which can lead to a race condition when two threads execute this traversal at the same time in which case you can still end up with two vertices with these properties. So, you currently need to ensure that two threads can't execute this traversal for the same properties in parallel, e.g., with locks in your application.
I just published a blog post about exactly this topic: How to Avoid Doppelgängers in a Graph Database if you want to get more information about this topic in general. It also describes distributed locking as a way to implement locks for distributed systems and discusses possible improvements to better support upserts in JanusGraph in the future.
Related
I'm having a hard time understanding the shape of the state that's derived applying that entity's events vs a projection of that entity's data.
Is an Aggregate's state ONLY used for determining whether or not a command can successfully be applied? Or should that state be usable in other ways?
An example - I have a Post entity for a standard blog post. I might have events like postCreated, postPublished, postUnpublished, etc. For my projections that I'll be persisting in my read tables, I need a projection for the base posts (which will include all posts, regardless of status, with lots of detail) as well as published_posts projection (which will only represent posts that are currently published with only the information necessary for rendering.
In the situation above, is my aggregate state ONLY supposed to be used to determine, for example, if a post can be published or unpublished, etc? If this is the case, is the shape of my state within the aggregate purely defined by what's required for these validations? For example, in my base post projection, I want to have a list of all users that have made a change to the post. In terms of validation for the aggregate/commands, I couldn't care less about the list of users that have made changes. Does that mean that this list should not be a part of my state within my aggregate?
TL;DR: yes - limit the "state" in the aggregate to that data that you choose to cache in support of data change.
In my aggregates, I distinguish two different ideas:
the history , aka the sequence of events that describes the changes in the lifetime of the aggregate
the cache, aka the data values we tuck away because querying the event history every time kind of sucks.
There's not a lot of value in caching results that we are never going to use.
One of the underlying lessons of CQRS is that we don't need aggregates everywhere
An AGGREGATE is a cluster of associated objects that we treat as a unit for the purpose of data changes. -- Evans, 2003
If we aren't changing the data, then we can safely work directly with immutable copies of the data.
The only essential purpose of the aggregate is to determine what events, if any, need to be applied to bring the aggregate's state in line with a command (if the aggregate can be brought so in line). All state that's not needed for that purpose can be offloaded to a read-side, which can be thought of as a remix of the event stream (with each read-side only maintaining the state it needs).
That said, there are in practice, reasons to use the aggregate state directly, with the primary one being a desire for a stronger consistency for the aggregate: CQRS is inherently eventually consistent. As with all questions of consistent updates, it's important to recognize that consistency isn't free and very often isn't even cheap; I tend to think of a project as having a consistency budget and I'm pretty miserly about spending it.
In your case, there's probably no reason to include the list of users changing a post in the aggregate state, unless e.g. there's something like "no single user can modify a given post more than n times".
I am trying to figure out efficient algorithm for processing Documents in distributed (FaaS to be more precise) environment.
Bruteforce approach would be O(D * F * R) where:
D is amount of Documents to process
F is amount of filters
R is highest amount of Rules in single Filter
I can assume, that:
single Filter has no more than 10 Rules
some Filters may share Rules (so it's N-to-N relation)
Rules are boolean functions (predicates) so I can try to take advantage of early cutting, meaning that if I have f() && g() && h() with f() evaluating to false then I do not have to process g() and h() at all and can return false immediately.
in single Document amount of Fields is always same (and about 5-10)
Filters, Rules and Documents are already in database
every Filter has at least one Rule
Using sharing (second assumption) I had an idea to first process Document against every Rule and then (after finishing) for every Filter using already computed Rules compute result. This way if Rule is shared then I am computing it only once. However, it doesn't take advantage of early cutting (third assumption).
Second idea is to use early cutting as slightly optimized bruteforce, but it won't use Rules sharing then.
Rules sharing looks like subproblem sharing, so probably memoization and dynamic programming will be helpful.
I have noticed, that Filter-Rule relation is bipartite graph. Not quite sure if it can help me though. I also have noticed, that I could use reverse sets and in every Rule store corresponding Set. This would however create circular dependency and may cause desynchronization problems in database.
Default idea is that Documents are streamed, and every single of them is event that will create FaaS instance to process it. However, this would probably force every FaaS instance to query for all Filters, which leaves me at O(F * D) queries because of Shared-Nothing architecture.
Sample Filter:
{
'normalForm': 'CONJUNCTIVE',
'rules':
[
{
'isNegated': true,
'field': 'X',
'relation': 'STARTS_WITH',
'value': 'G',
},
{
'isNegated': false,
'field': 'Y',
'relation': 'CONTAINS',
'value': 'KEY',
},
}
or in more condense form:
document -> !document.x.startsWith("G") && document.y.contains("KEY")
for Document:
{
'x': 'CAR',
'y': 'KEYBOARD',
'z': 'PAPER',
}
evaluates to true.
I can slightly change data model, stream something else instead of Document (ex. Filters) and use any nosql database and tools to help it. Apache Flink (event processing) and MongoDB (single query to retrieve Filter with it's Rules) or maybe Neo4j (as model looks like bipartite graph) looks like could help me, but not sure about it.
Can it be processed efficiently (with regard to - probably - database queries)? What tools would be appropriate?
I have been also wondering, if maybe I am trying to solve special case of some more general (math) problem that may have useful theorems and algorithms.
EDIT: My newest idea: Gather all Documents in cache like Redis. Then single event starts up and publishes N functions (as in Function as a Service), and every function selects F/N (amount of Filters divided by number of instances - so just evenly distributing Filters across instances) this way every Filter is fetched from database only once.
Now, every instance streams all Documents from cache (one document should be less than 1MB and at the same time I should have 1-10k of them so should fit in cache). This way every Document is selected from database only once (to cache).
I have reduced database read operations (still some Rules are selected multiple times), but still I am not taking advantage of Rule sharing across Filters. I could intentionally ignore it by using document database. This way by selecting Filter I will also get it's Rules. Still - I have to recalculate it's value.
I guess that's what I get for using Shared Nothing scalable architecture?
I realized that although my graph is indeed (in theory) bipartite but (in practice) it's going to be set of disjoint bipartite graphs (as not all Rules are going to be shared). This means, that I can process those disjoint parts independently on different FaaS instances without recalculating same Rules.
This reduces my problem to processing single bipartite connected graph. Now, I can use benefits of dynamic programming and share result of Rule computation only if memory i shared, so I cannot divide (and distribute) this problem further without sacrificing this benefit. So I thought this way: if I have already decided, that I will have to recompute some Rules, then let it be low compared to disjoint parts that I will get.
This is actually minimum cut problem, that has (fortunately) polynomial complexity known algorithm.
However, this may be not ideal in my case, because I don't want to cut any part of graph - I would like to cut graph ideally in half (divide and conquer strategy, that could be reapplied recursively till graph would be so small that could be processed in seconds in FaaS instance, that has time bound).
This means, that I am looking for cut, that would create two disjoint bipartite graphs, with possibly same amount of vertexes each (or at least similar).
This is sparsest cut problem, that is NP-hard, but has O(sqrt(logN)) approximated algorithm, that also favors less cut edges.
Currently, this does look like solution for my problem, however I would love to hear any suggestions, improvements and other answers.
Maybe it can be done better with other data model or algorithm? Maybe I can reduce it further with some theorem? Maybe I could transform it to other (simpler) problem, or at least that is easier to divide and distribute across nodes?
This idea and analysis strongly suggests using graph database.
I am creating a system in orientDB I have some cases where i need to make the system where multiple threads adding edges to certain vertex and updating a property on that vertex.
My Question is that is there any method where i can block operations on this vertex until other threads finish a block of code this block for both read and write?
My case i have vertex for hotel and number of available rooms as property, any reservation, my code will work the following order
1- Make sure the remaining rooms size is more than 1
2- Create an edge to the customer vertex
3- Decrease the number of available rooms by one
OrientDB has an optimistic concurrency control system, but on very high concurrent updates on the few records it could be more efficient locking records to avoid retries. You could synchronize the access by yourself or by using the storage API. Note that this works only with non-remote databases.
((OStorageEmbedded)db.getStorage()).acquireWriteLock(final ORID iRid)
((OStorageEmbedded)db.getStorage()).acquireSharedLock(final ORID iRid)
((OStorageEmbedded)db.getStorage()).releaseWriteLock(final ORID iRid)
((OStorageEmbedded)db.getStorage()).releaseSharedLock(final ORID iRid)
Source and examples of usage can be found on the official documentation.
I'm using titan graph db with tinkerpop plugin. What is the best way to retrieve a vertex using has step?
Assuming employeeId is a unique attribute which has a unique vertex centric index defined.
Is it through label
i.e g.V().has(label,'employee').has('employeeId','emp123')
g.V().has('employee','employeeId','emp123')
(or)
is it better to retrieve a vertex based on Unique properties directly?
i.e g.V().has('employeeId','emp123')
Which one of the two is the quickest and better way?
First you have 2 options to create the index:
mgmt.buildIndex('byEmployeeId', Vertex.class).addKey(employeeId).buildCompositeIndex()
mgmt.buildIndex('byEmployeeId', Vertex.class).addKey(employeeId).indexOnly(employee).buildCompositeIndex()
For option 1 it doesn't really matter which query you're going to use. For option 2 it's mandatory to use g.V().has('employee','employeeId','emp123').
Note that g.V().hasLabel('employee').has('employeeId','emp123') will NOT select all employees first. Titan is smart enough to apply those filter conditions, that can leverage an index, first.
One more thing I want to point out is this: The whole point of indexOnly() is to allow to share properties between different types of vertices. So instead of calling the property employeeId, you could call it uuid and also use it for employers, companies, etc:
mgmt.buildIndex('employeeById', Vertex.class).addKey(uuid).indexOnly(employee).buildCompositeIndex()
mgmt.buildIndex('employerById', Vertex.class).addKey(uuid).indexOnly(employer).buildCompositeIndex()
mgmt.buildIndex('companyById', Vertex.class).addKey(uuid).indexOnly(company).buildCompositeIndex()
Your queries will then always have this pattern: g.V().has('<label>','<prop-key>','<prop-value>'). This is in fact the only way to go in DSE Graph, since we got completely rid of global indexes that span across all types of vertices. At first I really didn't like this decision, but meanwhile I have to agree that this is so much cleaner.
The second option g.V().has('employeeId','emp123') is better as long as the property employeeId has been indexed for better performance.
This is because each step in a gremlin traversal acts a filter. So when you say:
g.V().has(label,'employee').has('employeeId','emp123')
You first go to all the vertices with the label employee and then from the employee vertices you find emp123.
With g.V().has('employeeId','emp123') a composite index allows you to go directly to the correct vertex.
Edit:
As Daniel has pointed out in his answer, Titan is actually smart enough to not visit all employees and leverages the index immediately. So in this case it appears there is little difference between the traversals. I personally favour using direct global indices without labels (i.e. the first traversal) but that is just a preference when using Titan, I like to keep steps and filters to a minimum.
Following this post from Neo4j's google group I have to say that I don't see any benefits when using this multiple-label-thing but rather, on the contrary, IMHO it just adds complexity for what a uniqueness constraint is. It could also tempt the user to introduce inheritance into the data model which would cause frustration since that's not possible at all...
Labels have not the notion of just representing a type, they are rather roles which are viable in different contexts.
So in one role, certain attributes and relationships of a node might matter and in another role (label) a different set (that might intersect with the first one).
We stayed away from inheritance as it opens a new can of worms, and we favor composition. So you'd rather compose a node whole as the sum of its parts. You can also mimic an inheritance by also attaching the "super"-types as labels to the child elements in your hierarchy.
Node labels can also be used to separate subgraphs in a larger graph, e.g. label the proteins that are active in human pathways and phylo pathways with those labels. So you can quickly select a part of the graph that you're interested in.
Those separate subgraphs can also come from different domains, like geo,social,catalogue,supplier that are combined in a single graph.
And multiple labels also make sense to separate "technical" namespaces of your graph that are used to represent "in-graph-indexes" from your "domain"-labels.
Regarding uniqueness - all uniqueness constraints for the existing labels and properties on your nodes are enforced at the same time. If they cannot be resolved on insert or update the operation will fail.