DBMS > Faircom DB vs. gStore vs. Splice Machine vs. SwayDB

System Properties Comparison Faircom DB vs. gStore vs. Splice Machine vs. SwayDB

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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.

A native Graph DBMS to store and maintain very large RDF datasets.

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

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Key-value store
Relational DBMS

Graph DBMS
RDF store

Relational DBMS

Key-value store

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

Score	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	0.14
Rank	#342	Overall
	#34	Graph DBMS
	#16	RDF stores

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Website

www.faircom.com/products/faircom-db

en.gstore.cn

splicemachine.com

swaydb.simer.au

Technical documentation

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

en.gstore.cn/#/enDocs

splicemachine.com/how-it-works

Developer

FairCom Corporation

Splice Machine

Simer Plaha

Initial release

1979

2016

2014

2018

Current release

V12, November 2020

1.2, November 2023

3.1, March 2021

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

BSD

Open Source

AGPL 3.0, commercial license available

Open Source

GNU Affero GPL V3.0

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

Scala

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

Data scheme

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

schema-free and OWL/RDFS-schema support

yes

schema-free

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

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

yes

APIs and other access methods

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

HTTP API
SPARQL 1.1

JDBC
Native Spark Datasource
ODBC

Supported programming languages

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

C++
Java
JavaScript (Node.js)
PHP
Python

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

Java
Kotlin
Scala

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

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

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

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

none

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

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

yes

ACID

Atomic execution of operations

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 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

Users, roles and permissions, Role-Based Access Control (RBAC) supported

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

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More resources

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