DBMS > AllegroGraph vs. Amazon Redshift vs. Amazon SimpleDB vs. Geode

System Properties Comparison AllegroGraph vs. Amazon Redshift vs. Amazon SimpleDB vs. Geode

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Editorial information provided by DB-Engines

Name

Description

High performance, persistent RDF store with additional support for Graph DBMS

Large scale data warehouse service for use with business intelligence tools

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

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

Primary database model

Document store

with version 6.5
Graph DBMS
RDF store
Vector DBMS

Relational DBMS

Key-value store

Secondary database models

Spatial DBMS

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

Score	1.06
Rank	#187	Overall
	#30	Document stores
	#17	Graph DBMS
	#7	RDF stores
	#7	Vector DBMS

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.85
Rank	#138	Overall
	#24	Key-value stores

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

Website

allegrograph.com

aws.amazon.com/redshift

aws.amazon.com/simpledb

geode.apache.org

Technical documentation

franz.com/agraph/support/documentation/current/agraph-introduction.html

docs.aws.amazon.com/redshift

docs.aws.amazon.com/simpledb

geode.apache.org/docs

Developer

Franz Inc.

Amazon (based on PostgreSQL)

Amazon

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

Initial release

2004

2012

2007

2002

Current release

8.0, December 2023

1.1, February 2017

License

Commercial or Open Source

commercial

Limited community edition free

commercial

Open Source

Apache Version 2; commercial licenses available as Gemfire

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

Linux
OS X
Windows

hosted

All OS with a Java VM

the JDK (8 or later) is also required

Data scheme

yes

RDF schemas

yes

schema-free

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.

bulk load of XML files possible

Secondary indexes

yes

restricted

yes

All columns are indexed automatically

SQL

Support of SQL

SPARQL is used as query language

yes

does not fully support an SQL-standard

SQL-like query language (OQL)

APIs and other access methods

RESTful HTTP API
SPARQL

JDBC
ODBC

RESTful HTTP API

Java Client API
Memcached protocol
RESTful HTTP API

Supported programming languages

C#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala

All languages supporting JDBC/ODBC

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

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

Server-side scripts

Stored procedures

yes

JavaScript or Common Lisp

user defined functions

in Python

user defined functions

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

with Federation

Sharding

none

Sharding must be implemented in the application

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Multi-source replication

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

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

Concurrent data updates can be detected by the application

yes, on a single node

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

Users with fine-grained authorization concept, user roles and pluggable authentication

fine grained access rights according to SQL-standard

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Access rights per client and object definable

More information provided by the system vendor

Specific characteristics

Knowledge Graph Platform Leader FedShard - Designed for Entity-Event Knowledge Graph...
» more

Competitive advantages

AllegroGraph is uniquely suited to support adhoc queries through SPARQL, Prolog and...
» more

News

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17 April 2024

100 Companies That Matter in KM – Franz Inc.
3 April 2024

Exploring AllegroGraph v8 – Unleashing the Power of Neuro-Symbolic AI (Recorded Webinar)
9 February 2024

What is Neuro-Symbolic AI?
23 January 2024

Allegro CL v11 – Now Available! – The Neuro-Symbolic AI Programming Platform
8 January 2024

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