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Dgraph Database Overview

Dgraph

Designed from day one to be distributed for scale and speed, Dgraph is the native Graph database with native GraphQL support. It is open-source, scalable, distributed, highly-available, and lightning fast.

Dgraph is different from other graph databases in a number of ways, including:

  • Distributed Scale:   Built from day 1 to be distributed, to handle larger data sets.

  • 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:   Dgraph 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:   Dgraph supports points and shapes data and queries can use near, within, contains, or intersects geo functions

  • True Free Open Source Software (FOSS):   Dgraph is free to use, and available on github.

Dgraph and GraphQL

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 powerful 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.

In addition, 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.

Application developers and data engineers work together seamlessly

Dgraph allows a particularly smooth interaction among data teams or experts and data consumers. GraphQL’s flexibility empowers data consumers to get exactly the data they want, in the format they want it, at the speed they need, without writing custom REST APIs or understanding a new graph query language.

Database experts can focus on the data, schema and indexes, without maintaining a sprawling set of REST APIs, views, or optimized queries tailored to each data consumer or app.

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 get started 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.

    Try the Introduction to GraphQL tutorial to define a GraphQL schema, insert and query data in just a few minutes.

  • Dedicated instances run on their own dedicated hardware to ensure consistent performance. This option extends the capabilities of the lower-cost shared instances to support enterprise, production workloads, and includes a high availability option.

Dgraph Architecture

Dgraph scales to larger 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 which communicate to form a single logical data store. There are two main types of processes (nodes) 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” which represent the relations among data elements. 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.

    Architecture of Dgraph

    Dgraph Internal Architecture

In addition, people use common tools to define schemas, load data, and query the database:

  • GraphQL IDEs: A number of GraphQL IDEs are available to update GraphQL schemas and run GraphQL updates and queries. One of these IDEs is GraphiQL

  • Ratel Ratel is a GUI app from Dgraph that runs DQL queries and mutations, and allows schema viewing and editing (as well as some cluster management operations).

  • Dgraph lambdas: A Dgraph lambda is a data function written in JavaScript that can augment results of a query. Lambdas implement database triggers and custom GraphQL resolvers, and run in an optional node.js server (included in any cloud deployment).

Scale, replication and sharding

Every cluster will have at least one Dgraph Zero node and one Dgraph Alpha node. Then databases are expanded in two ways.

  • High Availability Replication: For high-availability, Dgraph runs with three zeros and three alphas instead of one of each. This configuration is recommended for the scale and reliability required by most production applications. Having three servers both triples the capacity of the overall cluster, and also provides redundancy.

  • Sharding: When data sizes approach or exceed 1 TB, Dgraph databases are typically sharded so that full data replicas are not kept on any single alpha node. With sharding, data is distributed across many nodes (or node groups) to achieve higher scale. Sharding and high-availability combine when desired to provide massive scale and ideal reliability.

  • Self-healing: In Dgraph’s cloud offering, Kubernetes is used to automatically detect, restart and heal any cluster (HA, sharded, both or neither) to keep things running smoothly and at full capacity.

What’s Next

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