DBMS > JaguarDB vs. Transwarp StellarDB vs. Valentina Server

System Properties Comparison JaguarDB vs. Transwarp StellarDB vs. Valentina Server

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Editorial information provided by DB-Engines

Name

Description

Performant, highly scalable DBMS for AI and IoT applications

A distributed graph DBMS built for enterprise-level graph applications

Object-relational database and reports server

Primary database model

Key-value store
Vector DBMS

Graph DBMS

Relational DBMS

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

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#13	Vector DBMS

Score	0.07
Rank	#371	Overall
	#39	Graph DBMS

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Website

www.jaguardb.com

www.transwarp.cn/en/product/stellardb

www.valentina-db.net

Technical documentation

www.jaguardb.com/support.html

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

DataJaguar, Inc.

Transwarp

Paradigma Software

Initial release

2015

1999

Current release

3.3 July 2023

5.7.5

License

Commercial or Open Source

Open Source

GPL V3.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

the server part. Clients available in other languages

Server operating systems

Linux

Linux
OS X
Windows

Data scheme

yes

Typing

predefined data types such as float or date

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

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC

OpenCypher

ODBC

Supported programming languages

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

rights management via user accounts

yes

fine grained access rights according to SQL-standard

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More resources

Recent citations in the news

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