DBMS > Amazon Redshift vs. GraphDB vs. MySQL vs. Quasardb

System Properties Comparison Amazon Redshift vs. GraphDB vs. MySQL vs. Quasardb

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Enterprise-ready RDF and graph database with efficient reasoning, cluster and external index synchronization support. It supports also SQL JDBC access to Knowledge Graph and GraphQL over SPARQL.

Widely used open source RDBMS

Distributed, high-performance timeseries database

Primary database model

Relational DBMS

Graph DBMS
RDF store

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	3.25
Rank	#91	Overall
	#7	Graph DBMS
	#4	RDF stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Website

aws.amazon.com/redshift

www.ontotext.com

www.mysql.com

quasar.ai

Technical documentation

docs.aws.amazon.com/redshift

graphdb.ontotext.com/documentation

dev.mysql.com/doc

doc.quasar.ai/master

Social network pages

Developer

Amazon (based on PostgreSQL)

Ontotext

Oracle

since 2010, originally MySQL AB, then Sun

quasardb

Initial release

2012

2000

1995

2009

Current release

10.4, October 2023

8.4.0, April 2024

3.14.1, January 2024

License

Commercial or Open Source

commercial

Some plugins of GraphDB Workbench are open sourced

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

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

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

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Implementation language

Java

C and C++

C++

Server operating systems

hosted

All OS with a Java VM
Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

BSD
Linux
OS X
Windows

Data scheme

yes

schema-free and OWL/RDFS-schema support; RDF shapes

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

Secondary indexes

restricted

yes, supports real-time synchronization and indexing in SOLR/Elastic search/Lucene and GeoSPARQL geometry data indexes

yes

with tags

SQL

Support of SQL

yes

does not fully support an SQL-standard

stored SPARQL accessed as SQL using Apache Calcite through JDBC/ODBC

yes

with proprietary extensions

SQL-like query language

APIs and other access methods

JDBC
ODBC

GeoSPARQL
GraphQL
GraphQL Federation
Java API
JDBC
RDF4J API
RDFS
RIO
Sail API
Sesame REST HTTP Protocol
SPARQL 1.1

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

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

Server-side scripts

Stored procedures

user defined functions

in Python

well-defined plugin interfaces; JavaScript server-side extensibility

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

Multi-source replication
Source-replica replication

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency, Eventual consistency (configurable in cluster mode per master or individual client request)

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Constraint checking

yes

not for MyISAM storage engine

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

by using LevelDB

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

fine grained access rights according to SQL-standard

Default Basic authentication through RDF4J client, or via Java when run with cURL, default token-based in the Workbench or via Rest API, optional access through OpenID or Kerberos single sign-on.

Users with fine-grained authorization concept

no user groups or roles

Cryptographically strong user authentication and audit trail

More information provided by the system vendor

Specific characteristics

Ontotext GraphDB is a semantic database engine that allows organizations to build...
» more

Competitive advantages

GraphDB allows you to link text and data in big knowledge graphs. It’s easy to experiment...
» more

Typical application scenarios

Metadata enrichment and management, linked data publishing, semantic inferencing...
» more

Key customers

GraphDB provides a platform for building next-generation AI and Knowledge Graph...
» more

Market metrics

GraphDB is the most utilized semantic triplestore for mission-critical enterprise...
» more

Licensing and pricing models

GraphDB Free is a non-commercial version and is free to use. GraphDB Enterprise edition...
» more

News

Riding the Databricks Wave with Hybrid Knowledge Graphs
6 June 2024

Matching Skills and Candidates with Graph RAG
31 May 2024

A Triple Store RAG Retriever
29 May 2024

Integrating GraphDB with Relational Database Systems
23 May 2024

Understanding the Graph Center of Excellence
17 May 2024

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