ArangoDB supports multiple data models within a single database engine, allowing developers to choose the most suitable model for their specific use case. The three main data models supported by ArangoDB are:
Document Model :
* In the document model, data is organized into collections of JSON-like documents.
* Each document is a self-contained unit of data that can contain nested fields, arrays, and key-value pairs.
* Documents within a collection do not need to have a fixed schema, allowing for flexible and dynamic data structures.
* This model is well-suited for applications with semi-structured or schema-less data, such as content management systems, e-commerce platforms, and user profiles.
Graph Model :
* In the graph model, data is represented as vertices (nodes) and edges (relationships) between vertices.
* Vertices represent entities, such as people, places, or things, while edges represent relationships between entities.
* Graphs can be directed or undirected, and edges can have properties that describe the relationship.
* This model is ideal for applications with highly connected data, such as social networks, recommendation engines, network analysis, and fraud detection.
Key-Value Model :
* In the key-value model, data is stored as simple key-value pairs.
* Each key is unique and associated with a single value, which can be any type of data, such as strings, numbers, or binary blobs.
* Key-value stores are optimized for high-performance read and write operations, making them suitable for caching, session management, and distributed systems.
* While the key-value model is the simplest, it provides limited querying capabilities compared to the document and graph models.
ArangoDB's multi-model architecture allows developers to combine and query data from different models using a single query language (AQL), making it easier to work with diverse and interconnected data types within the same database. This flexibility enables developers to build complex applications that require multiple data models without the need for separate databases or complex data integration processes.
