DBMS > Faircom DB vs. HBase vs. TerminusDB vs. VoltDB

System Properties Comparison Faircom DB vs. HBase vs. TerminusDB vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

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

Key-value store
Relational DBMS

Wide column store

Graph DBMS

Relational DBMS

Secondary database models

Document store
RDF store

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

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

www.faircom.com/products/faircom-db

hbase.apache.org

terminusdb.com

www.voltdb.com

Technical documentation

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

hbase.apache.org/book.html

terminusdb.github.io/terminusdb/#

docs.voltdb.com

Developer

FairCom Corporation

Apache Software Foundation

Apache top-level project, originally developed by Powerset

DataChemist Ltd.

VoltDB Inc.

Initial release

1979

2008

2018

2010

Current release

V12, November 2020

2.3.4, January 2021

11.0.0, January 2023

11.3, April 2022

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache version 2

Open Source

GPL V3

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

ANSI C, C++

Java

Prolog, Rust

Java, C++

Server operating systems

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

easily portable to other OSs

Linux
Unix
Windows

using Cygwin

Linux

Linux
OS X

for development

Data scheme

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

schema-free, schema definition possible

yes

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

options to bring your own types, AVRO

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

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

SQL-like query language (WOQL)

yes

only a subset of SQL 99

APIs and other access methods

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

Java API
RESTful HTTP API
Thrift

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

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

C
C#
C++
Groovy
Java
PHP
Python
Scala

JavaScript
Python

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Coprocessors in Java

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

Graph Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication
Source-replica replication

Journaling Streams

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

tunable from ACID to Eventually Consistent

Single row ACID (across millions of columns)

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

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

yes

in-memory journaling

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

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Role-based access control

Users and roles with access to stored procedures

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More resources

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