Suppose I've the following simple graph.
I see two ways of traversing this graph
Option 1
I can use the following API provided by the Graph class
Graph factory = ...
factory.getVertices("type", "Response");
Option 2
I can also use GremlinPipeline API as documented here
Graph g = ... // a reference to a Blueprints graph
GremlinPipeline pipe = new GremlinPipeline();
pipe.start(g.getVertex(1))
My question are
Why two API's?
When to use which one?
Does GremlinPipeline take advantage of the indeces created using index related methods of TnkerGraph?
There are two APIs for getting data because one represents a Blueprints-level which is a lower level of abstraction having utility-level functions for accessing graphs and Gremlin-level which is a higher level of abstraction having a much higher degree of expressivity when traversing graphs. The design principle is built around the fact that Blueprints is an abstraction layer over different graph databases (i.e. Neo4j, OrientDB, etc) and that it needs to be simple enough for the implementations to be developed quickly. Gremlin however is a graph traversal language which works over the Blueprints API making it possible for Gremlin to operate over multiple graph databases.
Your examples don't allow Gremlin to shine at all. Sure, in those cases, there really isn't a reason to choose one over the other. Here's a similar example which I think is better:
// blueprints
g.getVertices()
// gremlin
g.V
Other than saving a few characters, Gremlin really isn't getting me anything. However, consider this Gremlin:
g.V.out('knows').outE('bought').has('weight',T.gt,100).inV.groupCount().cap()
I won't supply the Blueprints equivalent of that because it's a lot of code to type. You'll have to trust that this single line of Gremlin is worth many lines of code with tons of ugly looping.
Most of the time, usage of the raw Blueprints API isn't really necessary for traversals. You'll find yourself using it for loading data, but other than that, use Gremlin.
Related
I am trying to understand if I should use a GraphDB for my project. I am mapping a computer network and I use NetworkX. The relationships are physical or logical adjacency (L2 and L3) . In the current incarnation my program scans the network and dumps the adjacency info in a Postgress RDB. From there I use Python to build my graphs using NetworkX.
I am trying to understand if I should change my approach and if there is any benefit in storing the info in a GaphDB. Postgress has AgensGraph which seems to be built on top of Postgress as a GraphDB overlay or addon. I don not know yet if installing this on top will make my life easier. I barely survived the migration from SQLite to Postgress :-) and to SQLAlchemy so now in not even 3 months I am reconsidering things while I can (the migration is not complete)
I could chose to use a mix but I am not sure if it makes sense to use a GraphDB. From what I understand these has advantages as not needing a schema (which helps a lot for a DB newbie like me)
I am also wondering if NetworkX (Python librayr) and GraphDB overlap in any way. As far as I understand these things NetworkX could be instrumental in analyzing the topology of the graph while GraphDB is mainly used to query the data stored in the DB. Do they overlap in anyway? Can they be used together?
TLDR: Use Neo4j or OrientDB to store data and networkx for processing it (if you need complicated algorithms). It will be the best solution.
I strongly don't recommend you to use GraphDB for your purposes. GraphDB is based on RDF that is used for semantic web and common knowledge storage. It is not supposed to be used with problems like yours. There are many graph databases that will fit to you much better. I can recommend Neo4j (the most popular graph database, as you can see; free, but non-open-source) or OrientDB (the most popular open-source graph database).
I used graph database when I had a similar problem (but I used HP UCMDB, that is corporate software and is not free). It was really MUCH better than average relational DBs. So the idea of graph database usage is good and it fits to this kind of problems naturally.
I am not sure you really need networkx to analyze the graph (you can use graph query languages to it), but if you want, you can load the data from your DB to networkx with GraphML or some another methods (OrientDB is similar) to process it using networkx.
And the little question-answer quiz in the end:
As far as I understand these things NetworkX could be instrumental in analyzing the topology of the graph
Absolutely right.
while GraphDB is mainly used to query the data stored in the DB.
It is a database. And, yes, it is mainly used to query the data.
Do they overlap in anyway?
They are both about graphs. Of course they overlap :)
Can they be used together?
Yes, they can. No, they should not be used together for your problem.
I use Neo4j to do calculations on complex graphs from data stored on a relational database, these calculations must be done frequently so the natural solution has been to use Neo4j to create impermanent neo4j graphs on the fly.
I continue to find references like the one below on the internet (Neo4j: is it a in-memory graph database?):
Neo4j features a stripped down variant called
ImpermanentGraphDatabase. This one is intended to be used for testing
only. E.g. when you develop a graph enabled application your unit
tests might use it. It is not recommended to use
ImpermanentGraphDatabase for real life scenarios with large amounts of
data.
I'm doing exactly the above, using ImpermanentGraphDatabase for a real life scenario with thousands of nodes on which I do on the fly calculations.
Creating an embedded database each time I need to do a calculation on the fly is not feasible so what solution does Neo4j offer for this scenario? what exactly happens if you use Neo4j ImpermanentGraphDatabase for real life scenarios with large amounts of data?
There is the little word 'not' in the text.
Also in the (free) book "Graph Databases" (download) impermanent server mode is only recommended for testing, NOT for productive environments. If you want to use your graph locally (no cluster), you might use the embedded mode. Keep in mind, that there are always two parts for graph dbs: storage and traversal engine. But I think, you're looking for a (cypher) queryable datastructure? It's worth to have a look at TinkerPop/Gremlin (link).
For your purpose it depends on the part that'll change. The structure of your data? The formula for calculation? Or just the values? If your graph is static, you just need to update your data (not the graph database). If your graph is dynamic you might optimize your algorithm and use different data-structures. A graph is not always the best choice, neither are trees, lists or dictionaries.
In my application, I've a domain model which is essentially a graph. I need to essentially perform the following operations and the send the resulting graph to the client over network
Operations to be performed
Filter certain nodes based on business policy
Augment with more nodes and relationships (potentially from other data providers
After filtering, I need a serialization mechanism as well. After working with Neo4j and Tinkerpop, I feel Tinkerpop fits well for my usecase as it has
In-memory graph support (TinkerGraph)
Serialization mechanisms: GraphML, GML and GrapjSON
I am wondering if my understanding is accurate and approach is correct. Please suggest.
Sounds right. I often extract subgraphs and store them in a TinkerGraph for follow-on processing. I also use GraphSON for serialization. Seems like you're on the right track.
Here are 2 good sources for additional information:
gremlindocs.com
https://groups.google.com/forum/#!forum/gremlin-users
This is a fairly advanced topic related to directed graphs. I am looking into NOSQL technology for a project. In particular, using graph databases. It's a perfect fit in supporting the rich model I want to save relationships for and the problem domain is a graph itself (vertices and edges). Now obviously this made me look at Neo4j and other vendors in this space. Now I believe they def have solved or closed the gap with persisting data in a graph data structure way...which is perfect.
However my requirement goes further where there is a need to understand a specification of a directed graph to create an actual instance of a 'directed graph', such that particular rules and constraints are adhered to when actually creating the graph. The graph database doesn't concern itself with this which is correct and wouldnt want it too (happy that it's agnostic of this). The problem is this leaves it a little open ended as what ensures that the graph complies with your graph rules (ie that certain nodes can have certain relationships or even have relationships to other nodes). What should I be using that will allow me to specify the specification/metadata of the directed graph such that when creating an instance of it at runtime it adheres correctly to it's
specification.
Any help or suggestions on what is available or what is the standard way to approach this would be appreciated
I think you should take a look at Spring Data Graph, http://www.springsource.org/spring-data/neo4j that is as close as you get in having a powerful mapping layer that can project rules etc, much like JPA or Hibernate.
WDYT?
/peter
Suppose I have a set of directed graphs. I need to query those graphs. I would like to get a feeling for my best choice for the graph modeling task. So far I have these options, but please don't hesitate to suggest others:
Proprietary implementation (matrix)
and graph traversal algorithms.
RDBM and SQL option (too space consuming)
RDF and SPARQL option (too slow)
What would you guys suggest? Regards.
EDIT: Just to answer Mad's questions:
Each one is relatively small, no more than 200 vertices, 400 edges. However, there are hundreds of them.
Frequency of querying: hard to say, it's an experimental system.
Speed: not real time, but practical, say 4-5 seconds tops.
You didn't give us enough information to respond with a well thought out answer. For example: what size are these graphs? With what frequencies do you expect to query these graphs? Do you need real-time response to these queries? More information on what your application is for, what is your purpose, will be helpful.
Anyway, to counter the usual responses that suppose SQL-based DBMSes are unable to handle graphs structures effectively, I will give some references:
Graph Transformation in Relational Databases (.pdf), by G. Varro, K. Friedl, D. Varro, presented at International Workshop on Graph-Based Tools (GraBaTs) 2004;
5 Conclusion and Future Work
In the paper, we proposed a new graph transformation engine based on off-the-shelf
relational databases. After sketching the main concepts of our approach, we carried
out several test cases to evaluate our prototype implementation by comparing it to
the transformation engines of the AGG [5] and PROGRES [18] tools.
The main conclusion that can be drawn from our experiments is that relational
databases provide a promising candidate as an implementation framework for graph
transformation engines. We call attention to the fact that our promising experimental
results were obtained using a worst-case assessment method i.e. by recalculating
the views of the next rule to be applied from scratch which is still highly inefficient,
especially, for model transformations with a large number of independent matches
of the same rule. ...
They used PostgreSQL as DBMS, which is probably not particularly good at this kind of applications. You can try LucidDB and see if it is better, as I suspect.
Incremental SQL Queries (more than one paper here, you should concentrate on " Maintaining Transitive Closure of Graphs in SQL "): "
.. we showed that transitive closure, alternating paths, same generation, and other recursive queries, can be maintained in SQL if some auxiliary relations are allowed. In fact, they can all be maintained using at most auxiliary relations of arity 2. ..
Incremental Maintenance of Shortest Distance and Transitive Closure in First Order Logic and SQL.
Edit: you give more details so... I think the best way is to experiment a little with both a main-memory dedicated graph library and with a DBMS-based solution, then evaluate carefully pros and cons of both solutions.
For example: a DBMS need to be installed (if you don't use an "embeddable" DBMS like SQLite), only you know if/where your application needs to be deployed and what your users are. On the other hand, a DBMS gives you immediate benefits, like persistence (I don't know what support graph libraries gives for persisting their graphs), transactions management and countless other. Are these relevant for your application? Again, only you know.
The first option you mentioned seems best. If your graph won't have many edges (|E|=O(|V|)) then you might earn better complexity of time and space using Dictionary:
var graph = new Dictionary<Vertex, HashSet<Vertex>>();
An interesting graph library is QuickGraph. Never used it but it seems promising :)
I wrote and designed quite a few graph algorithms for various programming contests and in production code. And I noticed that every time I need one, I have to develop it from scratch, assembling together concepts from graph theory (BFS, DFS, topological sorting etc).
Perhaps a lack of experience is a reason, but it seems to me that there's still no reasonable general-purpose query language to solve graph problems. Pick a couple of general-purpose graph libraries and solve your particular task in a programming (not query!) language. That will give you best performance and space consumption, but will also require understanding of graph theory basic concepts and of their limitations.
And the last one: do not use SQL for graphs.