Dgraph is a horizontally scalable and distributed GraphQL database with a graph backend. It provides
ACID transactions, consistent replication, and linearizable reads. It's built from the ground up to
perform a rich set of queries. Being a native GraphQL database, it tightly controls how the data is
arranged on disk to optimize for query performance and throughput, reducing disk seeks and network
calls in a cluster.
Dgraph's goal is to provide Google production-level scale and throughput, with low enough latency to
serve real-time user queries over terabytes of structured data. Dgraph supports
GraphQL query syntax, and responds in JSON
and Protocol Buffers over
GRPC and HTTP. Dgraph is written using the Go Programming Language.
Status
Dgraph is at version v25 and is production-ready. Apart from the vast open source community,
it is being used in production at multiple Fortune 500 companies.
Supported Platforms
Dgraph officially supports the Linux/amd64 and Linux/arm64 architectures. In order to take advantage
of memory performance gains and other architecture-specific advancements in Linux, we dropped
official support for Mac and Windows in 2021, see
this blog post for more
information. You can still build and use Dgraph on other platforms (for live or bulk loading for
instance), but support for platforms other than Linux/amd64 and Linux/arm64 is not available.
Running Dgraph in a Docker environment is the recommended testing and deployment method.
Then clone the Dgraph repository and use make install to install the Dgraph binary in the
directory named by the GOBIN environment variable, which defaults to $GOPATH/bin or $HOME/go/bin if
the GOPATH environment variable is not set.
git clone https://github.com/dgraph-io/dgraph.git
cd dgraph
make setup
make install
Do you have more than 10 SQL tables connected via foreign keys?
Do you have sparse data, which doesn't elegantly fit into SQL tables?
Do you want a simple and flexible schema, which is readable and maintainable over time?
Do you care about speed and performance at scale?
If the answers to the above are YES, then Dgraph would be a great fit for your application. Dgraph
provides NoSQL like scalability while providing SQL like transactions and the ability to select,
filter, and aggregate data points. It combines that with distributed joins, traversals, and graph
operations, which makes it easy to build applications with it.
Dgraph compared to other graph DBs
Features
Dgraph
Neo4j
Janus Graph
Architecture
Sharded and Distributed
Single server (+ replicas in enterprise)
Layer on top of other distributed DBs
Replication
Consistent
None in community edition (only available in enterprise)
Via underlying DB
Data movement for shard rebalancing
Automatic
Not applicable (all data lies on each server)
Via underlying DB
Language
GraphQL inspired
Cypher
Gremlin
Protocols
Grpc / HTTP + JSON / RDF
Bolt + Cypher
Websocket / HTTP
Transactions
Distributed ACID transactions
Single server ACID transactions
Not typically ACID
Full-Text Search
Native support
Native support
Via External Indexing System
Regular Expressions
Native support
Native support
Via External Indexing System
Geo Search
Native support
External support only
Via External Indexing System
License
Apache 2.0
GPL v3
Apache 2.0
Users
Dgraph official documentation is present at docs.dgraph.io.
Dgraph 是一个开源的、可横向扩展的分布式图数据库，带有 GraphQL 接口，专为对 TB 级数据进行实时查询而构建。它提供 ACID 事务、一致性复制和线性一致性读取。