DBMS > Faircom DB vs. Sequoiadb vs. Splice Machine vs. TerminusDB vs. VelocityDB

System Properties Comparison Faircom DB vs. Sequoiadb vs. Splice Machine vs. TerminusDB vs. VelocityDB

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.

NewSQL database with distributed OLTP and SQL

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

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

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Key-value store
Relational DBMS

Document store
Relational DBMS

Relational DBMS

Graph DBMS

Graph DBMS
Object oriented 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	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Score	0.05
Rank	#358	Overall
	#36	Graph DBMS
	#16	Object oriented DBMS

Website

www.faircom.com/products/faircom-db

Technical documentation

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

www.sequoiadb.com/en/index.php?m=Files&a=index

splicemachine.com/how-it-works

terminusdb.github.io/terminusdb/#

velocitydb.com/UserGuide

Developer

FairCom Corporation

Sequoiadb Ltd.

Splice Machine

DataChemist Ltd.

VelocityDB Inc

Initial release

1979

2013

2014

2018

2011

Current release

V12, November 2020

3.1, March 2021

11.0.0, January 2023

7.x

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Server: AGPL; Client: Apache V2

Open Source

AGPL 3.0, commercial license available

Open Source

GPL V3

commercial

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++

C++

Java

Prolog, Rust

Server operating systems

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

easily portable to other OSs

Linux

Linux
OS X
Solaris
Windows

Linux

Any that supports .NET

Data scheme

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

schema-free

yes

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

yes

oid, date, timestamp, binary, regex

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

yes

SQL-like query language (WOQL)

APIs and other access methods

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

proprietary protocol using JSON

JDBC
Native Spark Datasource
ODBC

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

.Net

Supported programming languages

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

.Net
C++
Java
PHP
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

JavaScript
Python

.Net

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

JavaScript

yes

Java

yes

Triggers

yes

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

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).

Source-replica replication

Multi-source replication
Source-replica replication

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

Document is locked during a transaction

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

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

yes

in-memory journaling

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

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

simple password-based access control

Access rights for users, groups and roles according to SQL-standard

Role-based access control

Based on Windows Authentication

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More resources

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