DBMS > Dgraph vs. HugeGraph vs. Lovefield vs. SQLite vs. VoltDB

System Properties Comparison Dgraph vs. HugeGraph vs. Lovefield vs. SQLite vs. VoltDB

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

A fast-speed and highly-scalable Graph DBMS

Embeddable relational database for web apps written in pure JavaScript

Widely used embeddable, in-process RDBMS

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Graph DBMS

Relational DBMS

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

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

dgraph.io

github.com/hugegraph
hugegraph.apache.org

google.github.io/lovefield

www.sqlite.org

www.voltdb.com

Technical documentation

dgraph.io/docs

hugegraph.apache.org/docs

github.com/google/lovefield/blob/master/docs/spec_index.md

www.sqlite.org/docs.html

docs.voltdb.com

Developer

Dgraph Labs, Inc.

Baidu

Google

Dwayne Richard Hipp

VoltDB Inc.

Initial release

2016

2018

2014

2000

2010

Current release

0.9

2.1.12, February 2017

3.45.3 (15 April 2024), April 2024

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Public Domain

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

Java

JavaScript

Java, C++

Server operating systems

Linux
OS X
Windows

Linux
macOS
Unix

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

server-less

Linux
OS X

for development

Data scheme

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

also supports composite index and range index

yes

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

yes

SQL-92 is not fully supported

yes

only a subset of SQL 99

APIs and other access methods

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

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

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

Groovy
Java
Python

JavaScript

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

Java

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

yes

depending on used storage backend, e.g. Cassandra and HBase

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

Snapshots and command logging

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

Planned for future releases

Users, roles and permissions

Users and roles with access to stored procedures

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