In this chapter, we will focus on the following topics −

• Database Interaction
• Data Model
• Data Retrieval

ArangoDB supports document based data model as well as graph based data model. Let us first describe the document based data model.

ArangoDB”s documents closely resemble the JSON format. Zero or more attributes are contained in a document, and a value attached with each attribute. A value is either of an atomic type, such as a number, Boolean or null, literal string, or of a compound data type, such as embedded document/object or an array. Arrays or sub-objects may consist of these data types, which implies that a single document can represent non-trivial data structures.

Further in hierarchy, documents are arranged into collections, which may contain no documents (in theory) or more than one document. One can compare documents to rows and collections to tables (Here tables and rows refer to those of relational database management systems – RDBMS).

But, in RDBMS, defining columns is a prerequisite to store records into a table, calling these definitions schemas. However, as a novel feature, ArangoDB is schema-less – there is no a priori reason to specify what attributes the document will have.

And unlike RDBMS, each document can be structured in a completely different way from another document. These documents can be saved together in one single collection. Practically, common characteristics may exist among documents in the collection, however the database system, i.e., ArangoDB itself, does not bind you to a particular data structure.

Now we will try to understand ArangoDB”s [graph data model], which requires two kinds of collections — the first is the document collections (known as vertices collections in group-theoretic language), the second is the edge collections. There is a subtle difference between these two types. Edge collections also store documents, but they are characterized by including two unique attributes, _from and _to for creating relations between documents. In practice, a document (read edge) links two documents (read vertices), both stored in their respective collections. This architecture is derived from the graph-theoretic concept of a labeled, directed graph, excluding edges that can have not only labels, but can be a complete JSON like document in itself.

To compute fresh data, delete documents or to manipulate them, queries are used, which select or filter documents as per the given criteria. Either being simple as an “example query” or being as complex as “joins”, queries are coded in AQL – ArangoDB Query Language.