DBMS > eXtremeDB vs. HugeGraph vs. Sphinx vs. VoltDB

System Properties Comparison eXtremeDB vs. HugeGraph vs. Sphinx vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

Natively in-memory DBMS with options for persistency, high-availability and clustering

A fast-speed and highly-scalable Graph DBMS

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

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

Relational DBMS
Time Series DBMS

Graph DBMS

Search engine

Relational DBMS

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

Score	0.74
Rank	#223	Overall
	#103	Relational DBMS
	#18	Time Series DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

www.mcobject.com

github.com/hugegraph
hugegraph.apache.org

sphinxsearch.com

www.voltdb.com

Technical documentation

www.mcobject.com/docs/extremedb.htm

hugegraph.apache.org/docs

sphinxsearch.com/docs

docs.voltdb.com

Developer

McObject

Baidu

Sphinx Technologies Inc.

VoltDB Inc.

Initial release

2001

2018

2001

2010

Current release

8.2, 2021

0.9

3.5.1, February 2023

11.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GPL version 2, commercial licence available

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

C and C++

Java

C++

Java, C++

Server operating systems

AIX
HP-UX
Linux
macOS
Solaris
Windows

Linux
macOS
Unix

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
OS X

for development

Data scheme

yes

Typing

predefined data types such as float or date

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.

support of XML interfaces available

Secondary indexes

yes

also supports composite index and range index

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

with the option: eXtremeSQL

SQL-like query language (SphinxQL)

yes

only a subset of SQL 99

APIs and other access methods

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

Proprietary protocol

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

.Net
C
C#
C++
Java
Lua
Python
Scala

Groovy
Java
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

asynchronous Gremlin script jobs

Java

Triggers

yes

by defining events

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning / sharding

yes

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

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

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

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

Optimistic (MVCC) and pessimistic (locking) strategies available

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

Users and roles with access to stored procedures

More information provided by the system vendor

Specific characteristics

eXtremeDB is an in-memory and/or persistent database system that offers an ultra-small...
» more

Competitive advantages

eXtremeDB databases can be modeled relationally or as objects and can utilize SQL...
» more

Typical application scenarios

IoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
» more

Key customers

Schneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
» more

Market metrics

With hundreds of customers and over 30 million devices/applications using the product...
» more

Licensing and pricing models

For server use cases, there is a simple per-server license irrespective of the number...
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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