DBMS > gStore vs. Kinetica vs. Spark SQL vs. SQL.JS vs. STSdb

System Properties Comparison gStore vs. Kinetica vs. Spark SQL vs. SQL.JS vs. STSdb

Editorial information provided by DB-Engines

Name

Description

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

Fully vectorized database across both GPUs and CPUs

Spark SQL is a component on top of 'Spark Core' for structured data processing

Port of SQLite to JavaScript

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Graph DBMS
RDF store

Relational DBMS

Key-value store

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.04
Rank	#359	Overall
	#37	Graph DBMS
	#18	RDF stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.53
Rank	#252	Overall
	#116	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

github.com/STSSoft/STSdb4

Technical documentation

en.gstore.cn/#/enDocs

docs.kinetica.com

spark.apache.org/docs/latest/sql-programming-guide.html

sql.js.org/documentation/index.html

Developer

Kinetica

Apache Software Foundation

Alon Zakai

enhancements implemented by others

STS Soft SC

Initial release

2016

2012

2014

2012

2011

Current release

1.2, November 2023

7.1, August 2021

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

License

Commercial or Open Source

Open Source

BSD

commercial

Open Source

Apache 2.0

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++

C, C++

Scala

JavaScript

Server operating systems

Linux

Linux
OS X
Windows

server-less, requires a JavaScript environment (browser, Node.js)

Windows

Data scheme

schema-free and OWL/RDFS-schema support

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

APIs and other access methods

HTTP API
SPARQL 1.1

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

JavaScript API

.NET Client API

Supported programming languages

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

C++
Java
JavaScript (Node.js)
Python

Java
Python
R
Scala

JavaScript

C#
Java

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

none

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

Durability

Support for making data persistent

yes

except by serializing a db to a file

yes

In-memory capabilities

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

yes

GPU vRAM or System RAM

yes

User concepts

Access control

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

Access rights for users and roles on table level

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