DBMS > Amazon Redshift vs. AnzoGraph DB vs. Geode vs. mSQL vs. OrigoDB

System Properties Comparison Amazon Redshift vs. AnzoGraph DB vs. Geode vs. mSQL vs. OrigoDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

mSQL (Mini SQL) is a simple and lightweight RDBMS

A fully ACID in-memory object graph database

Primary database model

Document store
Object oriented DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Website

aws.amazon.com/redshift

cambridgesemantics.com/anzograph

geode.apache.org

hughestech.com.au/products/msql

origodb.com

Technical documentation

docs.aws.amazon.com/redshift

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

geode.apache.org/docs

origodb.com/docs

Developer

Amazon (based on PostgreSQL)

Cambridge Semantics

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Hughes Technologies

Robert Friberg et al

Initial release

2012

2018

2002

1994

2009

under the name LiveDB

Current release

2.3, January 2021

1.1, February 2017

4.4, October 2021

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

free licenses can be provided

Open Source

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.

Implementation language

Java

Server operating systems

hosted

Linux

All OS with a Java VM

the JDK (8 or later) is also required

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
Windows

Data scheme

yes

Schema-free and OWL/RDFS-schema support

schema-free

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SPARQL and SPARQL* as primary query language. Cypher preview.

SQL-like query language (OQL)

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

APIs and other access methods

JDBC
ODBC

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

Java Client API
Memcached protocol
RESTful HTTP API

JDBC
ODBC

.NET Client API
HTTP API
LINQ

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
Python

.Net
All JVM based languages
C++
Groovy
Java
Scala

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions and aggregates

user defined functions

yes

Triggers

yes

Cache Event Listeners

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

Sharding

none

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication in MPP-Cluster

Multi-source replication

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in MPP-Cluster

Eventual Consistency

none

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

not needed in graphs

depending on model

Transaction concepts

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

ACID

yes, on a single node

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles

Access rights per client and object definable

Role based authorization

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