A graph database stores things as nodes and the connections between them as relationships. Queries follow those relationships to find matching entities or paths, making the model useful when an answer depends on how several things are connected. The details vary by graph model and product: property graphs and RDF graphs represent information differently, and their query languages are not interchangeable.
What a graph database stores
In a property graph such as the model documented by Neo4j, a node represents an entity or distinct object. A node can have labels that describe its role and key-value properties that store details. A person node, for example, might have a name property and a Person label.
A relationship connects a source node to a target node. It has a type and a direction, and it can also carry properties. A person connected to a movie by an ACTED_IN relationship expresses who acted in the movie; a property on that relationship could record the person’s role. The connection is represented directly in the graph, rather than being only a fact reconstructed by joining records. Neo4j describes its own graph database as storing “nodes, relationships, and properties instead of in tables or documents” in its product documentation.
Nodes, relationships, and properties
- Nodes: the entities or objects being represented, such as people and movies.
- Relationships: typed, directed connections between nodes, such as
ACTED_IN. - Properties: key-value details attached to nodes or, in a property graph, to relationships as well.
How graph queries work
A graph query starts from one or more nodes and follows relationships that satisfy specified conditions. It can return matching nodes, paths, or patterns. For example, a query can start with Tom Hanks, follow ACTED_IN relationships, and return connected movie nodes such as Forrest Gump. A traversal typically follows the relevant connections rather than examining every node in the graph.
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Neo4j’s Graph database concepts documentation describes the idea this way: “A traversal is how you query a graph in order to find answers to questions, for example: ‘What music do my friends like that I don’t yet own?’, or ‘What web services are affected if this power supply goes down?’” These examples show why following chains of relationships can be central to a query.
Example: finding a connection
- Choose a starting node, such as a person.
- Follow a relationship type, such as
ACTED_IN, in its specified direction. - Apply conditions to the connected nodes or relationships, if needed.
- Return the matching nodes, paths, or pattern.
Property graphs and RDF graphs are different models
“Graph database” does not describe one universal data model. Property graphs commonly attach properties to nodes and relationships. RDF represents information as triples: a subject, predicate, and object. The W3C describes an RDF triple visually as a node-arc-node link in an RDF graph; see its RDF 1.1 Concepts and Abstract Syntax.
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Those models organize information differently, so a feature or query described for one should not automatically be assumed to apply to the other. Choose based on how the information needs to be represented, queried, and exchanged.
Graph query languages depend on the system
Query languages are tied to models and product ecosystems; there is no single language that works with every graph database. Examples include:
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- Cypher: Neo4j documents Cypher as a declarative, GQL-conformant language for describing graph patterns. See the Cypher Manual.
- Gremlin: Apache TinkerPop describes Gremlin as a functional, data-flow language for graph traversals. See the Gremlin documentation.
- SPARQL: RDF systems use SPARQL specifications to query RDF data. See the W3C SPARQL 1.1 Query Language.
These are examples from different systems and model ecosystems, not interchangeable spellings for the same query language. When evaluating a product, check which model and language it supports and whether the tools your application needs are compatible.
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A graph representation is especially legible when recurring questions follow chains of connections: who is connected to whom, which services depend on a component, or which items are related to something a person already likes. The value is not simply that data has relationships; relational databases can represent connections too. The relevant question is how naturally the workload’s recurring queries map to each system.
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Relational databases may suit workloads centered on tabular operations, while a graph model may make relationship-heavy pattern matching more direct. That is a design comparison, not a guarantee that graph queries will outperform joins in every case. The right choice also depends on transactions, constraints, the existing shape of the data, and operational requirements.
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Questions to ask before choosing
- Data model: Does the application need a property graph, RDF, or another model?
- Query language and ecosystem: Are Cypher, Gremlin, SPARQL, or the product’s other supported tools compatible with the application?
- Workload shape: Do important queries repeatedly match relationship patterns, or are they primarily tabular aggregations and other operations?
- Operations: Does the selected product and version meet the application’s transaction, scaling, security, backup, and hosting needs? Confirm these capabilities in that product’s current official documentation.
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