[daemonk]

The flexibility of a graphdb translates to flexibility in writing your queries. And that's my main problem with graphdb. Query optimizations can be difficult. It is very easy to write a query that logically does what you want, but takes hours to run. And if you take a bit of time thinking about how the query runs, you can optimize to run in milliseconds.

[mrjn]

[Author of Dgraph]

> Query optimizations can be difficult.

I don't think they're any more difficult than SQL really. In fact, with Dgraph we can avoid scans, where SQL has to scan for most of the queries.

In fact, we're aiming to work on query optimization in depth starting mid-May. So, perhaps in a few months, this would be a topic worth writing a blog post about.

[daemonk]

Thanks for the response. I look forward to it. I've used both Dgraph and RedisGraph for some toy projects previously. I really think that graph dbs are the way to go for storing biological/health data which I am involved in. They naturally model this type of data very well.

The query optimization issue seems more to do with how the data is structured than perhaps the query engine? For example, if I wanted to query for all node type X, starting from node type A, it can potentially take a very very long time. But if I add a few known constraints like query all node type X, starting from node type A that paths through node B, C, D. It quickly gets much faster. I guess it's really more up the user to optimize the queries in these cases.

And the flexibility that graphdbs afford to the user makes it hard to realize that perhaps there are these natural constraints that can be used for this query. So it seems like there is definitely a graph analysis component to this that gives you the insight to write the queries faster.

I guess this problem also only applies to natural world problems that you are trying to force into a graph structure rather than a pre-designed database.

[mrjn]

The thing about Dgraph (and potentially other graph DBs) is that it knows the "cardinality" of the relationship, because of the way it stores data. So, it can make judgement calls on the best path to execute a query.

I might have a better write-up about this in a quarter or so.

[TechBro8615]

What do you think of GraphQL? I think GQL + multiple backend relational databases can be a nice middle ground. It means that you can optimize each relational DB separately, and still benefit from querying the graph at a higher level. (If needed, some of the backends can even be graph databases.)

It’s worth noting that GQL comes from Facebook, where they spend quite a bit of time operating with graph data.

[fizx]

GQL isn't really much like a graphdb (e.g. SPARQL).

The goal of GQL is to gather a useful subset of your microservice data into a single tree for your frontend to render. In a facebook context, a typical query is "give me all the info i need to be able to show my home feed".

The goal of graph dbs is to allow arbitrary, complexm, often cyclic and aggregating queries to be run against a data source expressed as a graph. A typical query is something like "how many of my friend-of-friends like basketball, displayed by location."