DBMS > Amazon Redshift vs. GigaSpaces vs. Lovefield vs. SQLite vs. Valentina Server

System Properties Comparison Amazon Redshift vs. GigaSpaces vs. Lovefield vs. SQLite vs. Valentina Server

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Embeddable relational database for web apps written in pure JavaScript

Widely used embeddable, in-process RDBMS

Object-relational database and reports server

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Secondary database models

Graph DBMS
Search engine

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Trend Chart

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Website

aws.amazon.com/redshift

www.gigaspaces.com

google.github.io/lovefield

www.sqlite.org

www.valentina-db.net

Technical documentation

docs.aws.amazon.com/redshift

docs.gigaspaces.com/latest/landing.html

github.com/google/lovefield/blob/master/docs/spec_index.md

www.sqlite.org/docs.html

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Amazon (based on PostgreSQL)

Gigaspaces Technologies

Google

Dwayne Richard Hipp

Paradigma Software

Initial release

2012

2000

2014

2000

1999

Current release

15.5, September 2020

2.1.12, February 2017

3.46.0 (23 May 2024), May 2024

5.7.5

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; Commercial licenses available

Open Source

Apache 2.0

Open Source

Public Domain

commercial

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, C++, .Net

JavaScript

Server operating systems

hosted

Linux
macOS
Solaris
Windows

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

server-less

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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.

XML can be used for describing objects metadata

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-99 for query and DML statements

SQL-like query language

via JavaScript builder pattern

yes

SQL-92 is not fully supported

yes

APIs and other access methods

JDBC
ODBC

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

ODBC

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C++
Java
Python
Scala

JavaScript

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Triggers

yes, event driven architecture

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Role-based access control

fine grained access rights according to SQL-standard

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