DBMS > HugeGraph vs. Sphinx vs. SQLite vs. Stardog vs. VoltDB

System Properties Comparison HugeGraph vs. Sphinx vs. SQLite vs. Stardog vs. VoltDB

Editorial information provided by DB-Engines

Name

Description

A fast-speed and highly-scalable Graph DBMS

Open source search engine for searching in data from different sources, e.g. relational databases

Widely used embeddable, in-process RDBMS

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

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

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

github.com/hugegraph
hugegraph.apache.org

Technical documentation

hugegraph.apache.org/docs

sphinxsearch.com/docs

www.sqlite.org/docs.html

docs.stardog.com

docs.voltdb.com

Developer

Baidu

Sphinx Technologies Inc.

Dwayne Richard Hipp

Stardog-Union

VoltDB Inc.

Initial release

2018

2001

2000

2010

Current release

0.9

3.5.1, February 2023

3.45.3 (15 April 2024), April 2024

7.3.0, May 2020

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPL version 2, commercial licence available

Open Source

Public Domain

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Open Source

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

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

Java

C++

Java

Java, C++

Server operating systems

Linux
macOS
Unix

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

server-less

Linux
macOS
Windows

Linux
OS X

for development

Data scheme

yes

dynamic column types

schema-free and OWL/RDFS-schema support

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.

Import/export of XML data possible

Secondary indexes

yes

also supports composite index and range index

yes

full-text index on all search fields

yes

supports real-time indexing in full-text and geospatial

yes

SQL

Support of SQL

SQL-like query language (SphinxQL)

yes

SQL-92 is not fully supported

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

yes

only a subset of SQL 99

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

Proprietary protocol

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

Groovy
Java
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

user defined functions and aggregates, HTTP Server extensions in Java

Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

yes

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

Sharding

Partitioning is done manually, search queries against distributed index is supported

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

none

Multi-source replication in HA-Cluster

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

Eventual Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

edges in graph

yes

relationships in graphs

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

The original contents of fields are not stored in the Sphinx index.

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

User concepts

Access control

Users, roles and permissions

Access rights for users and roles

Users and roles with access to stored procedures

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