The ultimate guide to graph databases | Dgraph

Dgraph is the native Graph database with native GraphQL support.
It is open-source, scalable, distributed, highly available, and fast.
It is different from other graph databases:
- Distributed Scale
- GraphQL Support
  - GraphQL is built-in to make data access simple and standards-compliant.
  - Unlike most GraphQL solutions, no resolvers are needed - DGraph resolves queries automatically through graph navigation.
- Fully Transactional and ACID Compliant
  - It satisfies demanding OLTP workloads that require frequent inserts and updates.
- Language support & Text Search
  - Full-text searching is included, and strings can be expressed in multiple languages.
- Geo data and geo queries
  - It supports points and shapes data.
  - Queries can use near, within, contains, or intersects geo functions.
In Dgraph, GraphQL is not an afterthought or an add-on; it is core to the product.
GraphQL developers can get started in minutes and need not concern themselves with the robust graph database running in the background.
The difference with Dgraph is that no resolvers or custom queries are needed.
- Simply update a GraphQL schema, and all APIs are ready to go.
- The “resolvers” are transparently implemented by simply following graph relationships from node to node and node to field and with native graph performance.
For complex queries that the GraphQL specification does not support, Dgraph provides a query language called “DQL” which is inspired by GraphQL, but includes more features.
- With GraphQL, simple use cases remain simple, and with DQL, more complex cases become possible.

The Graph Model - Nodes, Relationships, and Values

Dgraph is fundamentally a property-graph database because it stores nodes, relations among those nodes, and associated properties for any relation.
Dgraph supports JSON data as both a return structure and an insert/update format.
- In Dgraph JSON nesting represents relations among nodes, so { "name":"Bob", "homeAddress": { "Street":"123 Main st" } } efficiently and intuitively represents a Person node, an Address node, and a relation (called “homeAddress”) between them.
Dgraph supports RDF triples as an input and output format.
Dgraph relationships are directed links between nodes, allowing optimized traversal from node to node.
- Dgraph allows a bidirectional relation via directed relationships in both directions if desired.

Dgraph Cloud Cluster Types

Shared Instance
- Dgraph Cloud with shared instances is a fully-managed graph database service that lets you focus on building apps, not managing infrastructure.
- This is a fast and easy way to start with GraphQL and does not require any graph database knowledge to start and run.
- Shared instances run in a common database using Dgraph multi-tenancy.
- Your data is protected, but you share resources and will have limited scale.
Dedicated instances
- Run on their dedicated hardware to ensure consistent performance.
- This option extends the capabilities of the lower-cost shared instances to support enterprise and production workloads and includes a high availability option.

Dgraph Architecture

Dgraph scales to more extensive data sizes than other graph databases because it is designed from the ground up to be distributed.
- Therefore, Dgraph runs as a cluster of server nodes communicating to form a single logical data store.
Two main types of processes (nodes) are running: Zeros and Alphas.
- Dgraph Zero server nodes hold metadata for the Dgraph cluster, coordinate distributed transactions, and re-balance data among server groups.
- Dgraph Alpha server nodes store the graph data and indices.
  - Unlike non-distributed graph databases, Dgraph alphas store and index predicates, representing the data elements' relations.
  - This unique indexing approach allows Dgraph to perform a database query with depth N in only N network hops, making it faster and more scalable for distributed (sharded) data sets.