Scalable, Distributed, Low Latency, High Throughput Graph Database.

DGraph's goal is to provide Google production level scale and throughput, with low enough latency to be serving real time user queries, over terabytes of structured data. DGraph supports GraphQL as query language, and responds in JSON.

Check out the demo at dgraph.io.

MVP launch - Dec 2015 - Branch v0.1 This is a minimum viable product, alpha release of DGraph. It's not meant for production use. This version is not distributed and support for GraphQL is partial. See the Roadmap for list of working and planned features.

Your feedback is welcome. Feel free to file an issue when you encounter bugs and to direct the development of DGraph.

There's an instance of DGraph running at http://dgraph.xyz, that you can query without installing DGraph. This instance contains 21M facts from Freebase Film Data. See the query section below for a sample query. curl dgraph.xyz/query -XPOST -d '{}'

There's a docker image that you can readily use.

$ docker pull dgraph/dgraph:latest
$ docker run -t -i -v /somedir:/dgraph -v $HOME/go/src/github.com/dgraph-io/benchmarks/data:/data -p 80:8080 dgraph/dgraph:latest

Once into the dgraph container, you can now load your data. See Data Loading below. Also, you can skip this step, if you just want to play with DGraph. See Use Freebase Film data.

$ loader --postings /dgraph/p --rdfgzips /data/rdf-data.gzip --stw_ram_mb 3000

Once done, you can start the server

$ mkdir /dgraph/m  # Ensure mutations directory exists.
$ server --postings /dgraph/p --mutations /dgraph/m  --stw_ram_mb 3000

Now you can query the server, like so:

$ curl localhost:8080/query -XPOST -d '{root(_xid_: g.11b7nwjrxk) {type.object.name.en}}'

Best way to do this is to refer to Dockerfile, which has the most complete instructions on getting the right setup. All the instructions below are based on a Debian/Ubuntu system.

Go 1.5 has a regression bug in cgo, due to which DGraph is dependent on Go1.4. So download and install Go 1.4.3.

DGraph depends on RocksDB for storing posting lists.

# First install dependencies.
# For Ubuntu, follow the ones below. For others, refer to INSTALL file in rocksdb.
$ sudo apt-get install libgflags-dev libsnappy-dev zlib1g-dev libbz2-dev
$ git clone https://github.com/facebook/rocksdb.git
$ cd rocksdb
$ git checkout v4.1
$ make shared_lib
$ sudo make install

This would install RocksDB library in /usr/local/lib. Make sure that your LD_LIBRARY_PATH is correctly pointing to it.

# In ~/.bashrc
export LD_LIBRARY_PATH="/usr/local/lib"

Now get DGraph code. DGraph uses glock to fix dependency versions.

go get -v github.com/robfig/glock
go get -v github.com/dgraph-io/dgraph/...
glock sync github.com/dgraph-io/dgraph

# Optional
go test github.com/dgraph-io/dgraph/...

Let's load up data first. If you have RDF data, you can use that. Or, there's Freebase film rdf data here.

To use the above mentioned Film RDF data, install Git LFS first. I've found the Linux download to be the easiest way to install. Once installed, clone the repository:

$ git clone https://github.com/dgraph-io/benchmarks.git

To load the data in bulk, use the data loader binary in dgraph/server/loader. Loader needs a postings directory, where posting lists are stored.

$ cd $GOPATH/src/github.com/dgraph-io/dgraph/server/loader
$ go build . && ./loader --rdfgzips=path_of_benchmarks_dir/data/rdf-films.gz,path_of_benchmarks_dir/data/names.gz --postings DIRPATH/p

Loader is memory bound. Every mutation loads a posting list in memory, where mutations are applied in layers above posting lists. While loader doesn't write to disk every time a mutation happens, it does periodically merge all the mutations to posting lists, and writes them to rocksdb which persists them. How often this merging happens can be fine tuned by specifying stw_ram_mb. Periodically loader checks it's memory usage and if determines it exceeds this threshold, it would stop the world, and start the merge process. The more memory is available for loader to work with, the less frequently merging needs to be done, the faster the loading.

In other words, loader performance is highly dependent on merging performance, which depends on how fast the underlying persistent storage is. So, Ramfs/Tmpfs > SSD > Hard disk, when it comes to loading performance.

As a reference point, it took 2028 seconds (33.8 minutes) to load 21M RDFs from rdf-films.gz and names.gz (from benchmarks repository) on n1-standard-4 GCE instance using a 2G tmpfs as the dgraph directory for output, with stw_ram_mb=8196 flag set. The final output was 1.3GB. Note that stw_ram_mb is based on the memory usage perceived by Golang, the actual usage is higher.

Once data is loaded, point the dgraph server to the postings and mutations directory.

$ cd $GOPATH/src/github.com/dgraph-io/dgraph/server
$ go build .
$ ./server --mutations DIRPATH/m --postings DIRPATH/p

This would now run dgraph server at port 8080. If you want to run it at some other port, you can change that with the --port flag.

Now you can run GraphQL queries over freebase film data like so:

curl localhost:8080/query -XPOST -d '{
	me(_xid_: m.06pj8) {
		type.object.name.en
		film.director.film {
			type.object.name.en
			film.film.starring {
				film.performance.character {
					type.object.name.en
				}
				film.performance.actor {
					type.object.name.en
					film.director.film {
						type.object.name.en
					}
				}
			}
			film.film.initial_release_date
			film.film.country
			film.film.genre {
				type.object.name.en
			}
		}
	}
}' > output.json

This query would find all movies directed by Steven Spielberg, their names, initial release dates, countries, genres, and the cast of these movies, i.e. characteres and actors playing those characters; and all the movies directed by these actors, if any.

The support for GraphQL is very limited right now. In particular, mutations, fragments etc. via GraphQL aren't supported. You can conveniently browse Freebase film schema here. There're also some schema pointers in README.

With the data loaded above on the same hardware, it took 270ms to run the pretty complicated query above the first time after server run. Note that the json conversion step has a bit more overhead than captured here.

{
  "server_latency": {
    "json": "57.937316ms",
    "parsing": "1.329821ms",
    "processing": "187.590137ms",
    "total": "246.859704ms"
  }
}

Consecutive runs of the same query took much lesser time (100ms), due to posting lists being available in memory.

{
  "server_latency": {
    "json": "60.419897ms",
    "parsing": "143.126µs",
    "processing": "32.235855ms",
    "total": "92.820966ms"
  }
}

• Please see this wiki page for guidelines on contributions.

• Check out the wiki pages for documentation.
• Please use Github issue tracker to file bugs, or request features.
• You can direct your questions to dgraph@googlegroups.com.
• Or, just join Gitter chat.

• Lightening Talk on 29th Oct, 2015 at Go meetup, Sydney.

I, Manish R Jain, the author of DGraph, used to work on Google Knowledge Graph. My experience building large scale, distributed (Web Search and) Graph systems at Google is what inspired me to build this.