DBMS > Amazon Redshift vs. Geode vs. KeyDB vs. mSQL vs. OrigoDB

System Properties Comparison Amazon Redshift vs. Geode vs. KeyDB vs. mSQL vs. OrigoDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

mSQL (Mini SQL) is a simple and lightweight RDBMS

A fully ACID in-memory object graph database

Primary database model

Relational DBMS

Key-value store

Relational DBMS

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	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	0.71
Rank	#226	Overall
	#33	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

geode.apache.org

github.com/Snapchat/KeyDB
keydb.dev

hughestech.com.au/products/msql

origodb.com

Technical documentation

docs.aws.amazon.com/redshift

geode.apache.org/docs

docs.keydb.dev

origodb.com/docs

Developer

Amazon (based on PostgreSQL)

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

EQ Alpha Technology Ltd.

Hughes Technologies

Robert Friberg et al

Initial release

2012

2002

2019

1994

2009

under the name LiveDB

Current release

1.1, February 2017

4.4, October 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available as Gemfire

Open Source

BSD-3

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

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Implementation language

Java

C++

Server operating systems

hosted

All OS with a Java VM

the JDK (8 or later) is also required

Linux

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

Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

by using the Redis Search module

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

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

Java Client API
Memcached protocol
RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protoco

JDBC
ODBC

.NET Client API
HTTP API
LINQ

Supported programming languages

All languages supporting JDBC/ODBC

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

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

Lua

yes

Triggers

yes

Cache Event Listeners

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

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

Multi-source replication
Source-replica replication

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

none

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

depending on model

Transaction concepts

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

ACID

yes, on a single node

Optimistic locking, atomic execution of commands blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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 per client and object definable

simple password-based access control and ACL

Role based authorization

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