Concept
A graph database is a database that stores data as nodes (entities), edges
(relationships between entities), and properties (values on both), and answers queries
by following those relationships. Because connections are stored as data rather than
rebuilt from matching keys at query time, questions about how things relate, such as
who knows whom, what depends on what, or which accounts share a device, are direct to
express and typically efficient to run. Graph databases are used for recommendations,
fraud detection, knowledge graphs, access control, network topology, and retrieving
context for AI applications.
Learning objectives
After reading this article you will be able to:
- Define a graph database and its building blocks: nodes, edges, properties, and labels
- Explain how a graph database follows stored relationships instead of joining tables
- Compare the main graph query languages: GQL, openCypher, Gremlin, and SPARQL
- Recognize when a graph database fits a workload and when another database fits better
How does a graph database work?
A graph database stores entities as nodes and relationships as edges, and answers a query by starting at matching nodes and following edges to related nodes. Its data is organized around three building blocks.
Many graph databases also give every node and edge a label, or type, such as
User or
PURCHASED. Labels group similar elements and scope queries and indexes.
The starting nodes are often found through an index on a property, and the query then
walks edges to reach related nodes. “Products bought by people who bought this lamp” is
a walk from the lamp to its buyers, then from each buyer to their other purchases.
Following relationships instead of joining tables
In a relational database, a relationship is typically stored as a foreign key and rebuilt at query time with a join, which looks up matching keys in an index or scans a table. A graph database stores the adjacency itself. Some systems keep direct references from each node to its neighboring edges and nodes, an approach called index-free adjacency. Others keep each node’s edges in an adjacency list keyed by node ID. Either way, moving from a node to its neighbors reads stored adjacency instead of matching keys in a join. The cost of a traversal depends mainly on the number of relationships the query touches, and typically grows at most logarithmically with total data size, so multi-hop questions such as friends of friends stay practical at query time.What query languages do graph databases use?
Graph databases typically use GQL (the ISO standard), openCypher, or Gremlin for property graphs, and SPARQL for RDF data. These languages describe patterns of connected nodes and edges rather than tables and joins.
In openCypher, a query reads like a sketch of the graph; GQL uses very similar pattern
syntax. This openCypher query finds other products bought by buyers of the desk lamp.
The
WHERE clause excludes the lamp itself, which the pattern could otherwise match
again when a buyer has more than one PURCHASED edge to it:
When should you use a graph database?
A graph database fits when relationships are the point of the question: queries follow connections across several hops, the shape of those connections varies, or the relationships carry their own data. Common graph database use cases:How are graph databases used for AI and RAG?
In AI applications, a graph adds what isolated passages lack: relationships between entities, provenance back to source documents, newer versions, and the permissions that decide what a user may see. In retrieval-augmented generation (RAG), which grounds a language model’s answer in data retrieved at query time, vector or keyword search finds the entry points and the graph supplies this surrounding context; see What is RAG?. The same structure underpins agent memory, where facts, their sources, and their changes over time are stored as connected records. See What is GraphRAG? and What is AI agent memory?.When is a graph database not the right tool?
A graph database is not a general replacement for other databases. Consider another option when:- The workload is mostly aggregation and reporting. Sums, group-bys, and scans over every row of a large table are the strength of relational and columnar analytics systems.
- Relationships are shallow and fixed. If queries go one join deep and the schema rarely changes, a relational database handles them well with mature tooling.
- Access is simple key-value lookup. Fetching a record by its ID does not need traversal.
- Your tooling depends on SQL. Existing BI tools and SQL skills can outweigh the modeling benefits.
How do you choose a graph database?
Choose a graph database by comparing its data model, query interface, transaction guarantees, built-in search, storage architecture, and license and deployment options:- Data model: a labeled property graph or RDF triples. See What is a property graph? and Property graph vs RDF.
- Query interface: a text language such as GQL, openCypher, Gremlin, or SPARQL, or typed APIs in your application language.
- Transactions: whether reads and writes are ACID and at which isolation level.
- Search: whether vector and full-text search live in the same system and transaction, or in separate stores you keep in sync.
- Storage architecture: whether data must fit on one machine, or storage scales separately from compute.
- License and deployment: open source or proprietary, managed or self-hosted, server or embedded.
How does HelixDB work as a graph database?
HelixDB is an open-source (Apache 2.0) graph database with native vector search and BM25 full-text search, built on object storage. Its data model is one labeled property graph: nodes are entities, directed edges are relationships, and each has exactly one label and typed properties.- Typed SDKs and raw JSON. Queries are built with typed SDKs in Rust, TypeScript, Go,
and Python, or written as raw JSON. Each produces a JSON operation tree sent to
POST /v2/query, with no query deployment step. - Graph operations. Traversals over outgoing, incoming, or both directions, bounded repeats, branches, shortest path, projections, and aggregates.
- One transaction per request. Each request is one ACID transaction, and graph traversals, vector search, text search, and index lookups run inside it.
Frequently asked questions
Are graph databases ACID?
Many are, but guarantees vary by system. Transactional graph databases typically make each transaction atomic and durable, while the isolation level ranges from read committed to snapshot or serializable isolation, and some distributed systems relax guarantees across partitions. HelixDB runs each request as one ACID transaction with serializable snapshot isolation.Is a knowledge graph the same as a graph database?
No. A knowledge graph is a body of data: entities and facts organized by a shared schema. A graph database is software for storing and querying graph data. Knowledge graphs are often stored in graph databases; see What is a knowledge graph?.Do graph databases use SQL?
Usually not as their primary language. SQL does have a standard extension for property graph queries, SQL/PGQ, added in SQL:2023. It lets a relational database define a property graph over existing tables and query it with graph pattern matching inside a SQL statement, and some relational systems implement it.Is there an open-source graph database?
Yes. Many graph databases are available under open-source licenses, with different data models, query languages, and storage designs. HelixDB is open source under the Apache 2.0 license and includes vector and full-text search in the same database.Does a graph database replace vector search in AI applications?
No. A graph adds structure alongside semantic search, such as entity relationships, provenance, versions, and permissions, but it does not find passages by meaning. Many AI systems combine a graph with vector and full-text search to retrieve both similar content and the facts connected to it. See Hybrid search.Related topics
Graph vs relational databases
How multi-hop questions differ from joins, and when each model fits.
What is a property graph?
Labels and properties on nodes and edges, and how to model them.
Property graph vs RDF
How labeled property graphs and RDF triples model the same facts.
What is a knowledge graph?
Entities, relationships, and provenance for search and LLMs.
What is GraphRAG?
Retrieval that follows relationships, compared with vector RAG.
What is RAG?
Grounding a model’s answer in data retrieved at query time.
HelixDB data model
How HelixDB represents nodes, edges, properties, and indexes.
Traversals
Follow outgoing and incoming relationships in a HelixDB query.