DBMS > gStore vs. Quasardb vs. Splice Machine vs. STSdb

System Properties Comparison gStore vs. Quasardb vs. Splice Machine vs. STSdb

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Editorial information provided by DB-Engines

Name

Description

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

Distributed, high-performance timeseries database

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

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

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

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

en.gstore.cn

quasar.ai

splicemachine.com

github.com/STSSoft/STSdb4

Technical documentation

en.gstore.cn/#/enDocs

doc.quasar.ai/master

splicemachine.com/how-it-works

Developer

quasardb

Splice Machine

STS Soft SC

Initial release

2016

2009

2014

2011

Current release

1.2, November 2023

3.14.1, January 2024

3.1, March 2021

4.0.8, September 2015

License

Commercial or Open Source

Open Source

BSD

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

AGPL 3.0, commercial license available

Open Source

GPLv2, commercial license available

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++

Java

Server operating systems

Linux

BSD
Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Windows

Data scheme

schema-free and OWL/RDFS-schema support

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

yes

primitive types and user defined types (classes)

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

with tags

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

HTTP API
SPARQL 1.1

HTTP API

JDBC
Native Spark Datasource
ODBC

.NET Client API

Supported programming languages

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

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

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

C#
Java

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

consistent hashing

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication with selectable replication factor

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

yes

User concepts

Access control

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

Cryptographically strong user authentication and audit trail

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

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