DBMS > Dgraph vs. Faircom DB vs. JanusGraph vs. Riak KV

System Properties Comparison Dgraph vs. Faircom DB vs. JanusGraph vs. Riak KV

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Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Distributed, fault tolerant key-value store

Primary database model

Graph DBMS

Key-value store
Relational DBMS

Graph DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

dgraph.io

www.faircom.com/products/faircom-db

janusgraph.org

Technical documentation

dgraph.io/docs

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

docs.janusgraph.org

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Dgraph Labs, Inc.

FairCom Corporation

Linux Foundation; originally developed as Titan by Aurelius

OpenSource, formerly Basho Technologies

Initial release

2016

1979

2017

2009

Current release

V12, November 2020

0.6.3, February 2023

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Restricted, free version available

Open Source

Apache 2.0

Open Source

Apache version 2, commercial enterprise edition

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

ANSI C, C++

Java

Erlang

Server operating systems

Linux
OS X
Windows

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux
OS X
Unix
Windows

Linux
OS X

Data scheme

schema-free

schema free, schema optional, schema required, partial schema,

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

restricted

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

APIs and other access methods

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

HTTP API
Native Erlang Interface

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

Clojure
Java
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

yes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

yes

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

links between data sets can be stored

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

tunable from ACID to Eventually Consistent

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Planned for future releases

Fine grained access rights according to SQL-standard with additional protections for files

User authentification and security via Rexster Graph Server

yes, using Riak Security

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