The International Standards Organization is considering a standard for Graph Query Language (GQL.)
The International Standards Organization is considering a standard for Graph Query Language (GQL) which would ultimately allow for interoperability not only between Graph DBMS but eventually with the older, admittedly pervasive, SQL.
A standardized graph query language would not only help vendors but would help customers accelerate digital transformation and re-platforming. The existing lack of this standard means that adjacent technologies, such as BI tools, have difficulty integrating with a graph database. Organizations are unlikely to adopt a new technology, such as a graph data base, when that adoption means they will be unable to leverage their existing investments in things such as BI tools.
Few people have been as articulate on this topic as Alastair Green, Director of Project Management at Neo4J
Alastair says the idea was to get a single property graph query language which would do for graph what SQL does for relational data.There are three existing languages – Cypher, PGQL and a research language called G-CORE, which are very similar. They are like dialects of one language – and we thought it would be a good idea to bring them together. But we didn’t just want to bring them together, we also wanted to try to get a more advanced graph query language. This graph deserves its own language – and it’s not really going to happen as an extension to SQL.
There’s interest both from users and vendors in respect to graph Cloud services, like Amazon Neptune and Azure Cosmos DB. Those have come out initially with Gremlin support for property graphs. Gremlin is an engine with a valid API and a particular style of programming, but it doesn’t do the kind of job that SQL does for relational databases. I think that there is a lot of interest in changing that situation and getting to the point where there is declarative querying for all property graph databases. An interesting point about these Cloud services is that they are not built on preexisting relational technology. They are built on other technologies or they are built in a more native way for the graph problem, much as Neo4j’s database was. So there is interest in a language which is really oriented toward property graph query and isn’t based on how we adapt to or cover over existing SQL capabilities.
People are also looking at complex advanced graph problems and the utilization of graph in AI environments, for example. In some cases, people discussed this as being of interest – being able to really support a graph data model as the underpinning for artificial intelligence and machine learning work. Lastly, property graphs are a kind of super set data model. People are thinking about much more ubiquitous sets of graphs, data sets that can be managed independently and so forth. It’s like a “graphs everywhere” theme. There is a lot of interest in more sophisticated, concise, regular expression-based extensions to the pattern matching principle of Cypher and GQL, to make it easier for people to very concisely express complex explorations of the graph. I think this is a testament to the fact that people are looking at more and more complex problems where the graph model actually makes it easier and makes handling those graph problems more tractable.
The world is just starting to see the potential of Graph Databases. Standardization like GQL will be a key step in wide adoption.
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