DBMS > JaguarDB vs. MySQL vs. Stardog vs. SWC-DB vs. Trafodion

System Properties Comparison JaguarDB vs. MySQL vs. Stardog vs. SWC-DB vs. Trafodion

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Performant, highly scalable DBMS for AI and IoT applications

Widely used open source RDBMS

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

A high performance, scalable Wide Column DBMS

Transactional SQL-on-Hadoop DBMS

Primary database model

Key-value store
Vector DBMS

Relational DBMS

Key/Value like access via memcached API

Graph DBMS
RDF store

Wide column store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Time Series DBMS

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

www.jaguardb.com

www.mysql.com

www.stardog.com

github.com/kashirin-alex/swc-db
www.swcdb.org

trafodion.apache.org

Technical documentation

www.jaguardb.com/support.html

dev.mysql.com/doc

docs.stardog.com

trafodion.apache.org/documentation.html

Developer

DataJaguar, Inc.

Oracle

since 2010, originally MySQL AB, then Sun

Stardog-Union

Alex Kashirin

Apache Software Foundation, originally developed by HP

Initial release

2015

1995

2010

2020

2014

Current release

3.3 July 2023

8.4.0, April 2024

7.3.0, May 2020

0.5, April 2021

2.3.0, February 2019

License

Commercial or Open Source

Open Source

GPL V3.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Open Source

GPL V3

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

C++

the server part. Clients available in other languages

C and C++

Java

C++

C++, Java

Server operating systems

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS
Windows

Linux

Data scheme

yes

schema-free and OWL/RDFS-schema support

schema-free

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.

yes

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

yes

SQL

Support of SQL

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

with proprietary extensions

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Proprietary protocol
Thrift

ADO.NET
JDBC
ODBC

Supported programming languages

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

C++

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

proprietary syntax

user defined functions and aggregates, HTTP Server extensions in Java

Java Stored Procedures

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication
Source-replica replication

Multi-source replication in HA-Cluster

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Immediate Consistency in HA-Cluster

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

relationships in graphs

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Users with fine-grained authorization concept

no user groups or roles

Access rights for users and roles

fine grained access rights according to SQL-standard

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