Modeling Data in MongoDB vs ArangoDB

05Architecture, Documentation, Graphs, Query LanguageTags: , , ,

MongoDB is a document DB whereas ArangoDB is a multi-model DB supporting documents, graphs and key/values within a single database. When it comes to data modeling and data querying, they pursue somewhat different approaches.


In a Nutshell: In MongoDB, data modeling is “aggregate-oriented”, avoiding relations and joins. On the other side, everybody has probably used relational databases which organize the data in tables with relations and try to avoid as much redundancy as possible. Both approaches have their pros and cons. ArangoDB is somewhat in-between: You can both model and query your data in a “relational way” but also in an “aggregate-oriented way”, depending on your use case. ArangoDB offers joins, nesting of sub-documents and multi-collection graphs. More info

Is UNQL Dead?

04Architecture, Query LanguageTags: ,
Note: We changed the name of the database in May 2012. AvocadoDB is now called ArangoDB.

UNQL started with quite some hype last year. However, after some burst of activity the project came to a hold. So it seems, that – at least as a project – UNQL has been a failure. IMHO one of the major issues with the current UNQL is, that it tries to cover everything in NoSQL, from key-value stores to document-stores to graph-database. Basically you end up with greatest common divisor – namely key-value access. But with graph structures and also document-structures you really want to supports joins, paths or some sort of sub-structures.

Apart from all the technical and theoretical benefits of SQL and what advantages the underlying theory has to offer, the major plus from an users point of view is that it is readable. You simple can see an SQL statement – be it in C, Java, Ruby – and understand what is going on. It is declarative, not imperative. With other imperative solution, like a fluent interface or a map-reduce, you need to understand the underlying syntax or language. With SQL you only need to understand English – at least most of the time.

And here I think is where UNQL is totally right. We need something similar for the NoSQL world. But it should not try to be a “fits all situation”. It should be a fit for 80% of the problems. For simple key-values stores a fluent-interface is indeed enough. For very complex graph traversals a traversal program must be written. For very complex map-reduces you might need to write a program – but check out Google’s talk (www.nosql-matters.org/program) about NoNoSQL. There they describe why they are developing a SQL-like interface for Map/Reduce.

In my experience most of the time you have a set of collections holding different “types” of documents with some relations between them. One of the biggest advantages of document stores or graph databases is that you can have lists and sub-objects. The problem with SQL is, that it has no good way to deal with these structures. So I believe UNQL would be quite successful if it would concentrate on these strong advantages of NoSQL, instead of trying to unify everything – especially after hear Jan’s talk about a document query language at the NoSQL Cologne UG (an English version is also available).

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