DBMS > GigaSpaces vs. JaguarDB vs. openGauss vs. RavenDB

System Properties Comparison GigaSpaces vs. JaguarDB vs. openGauss vs. RavenDB

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Editorial information provided by DB-Engines

Name

Description

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

Performant, highly scalable DBMS for AI and IoT applications

An enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by Huawei

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Key-value store
Vector DBMS

Relational DBMS

Document store

Secondary database models

Graph DBMS
Search engine

Document store
Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	1.08
Rank	#183	Overall
	#84	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Website

www.gigaspaces.com

www.jaguardb.com

gitee.com/opengauss
opengauss.org

ravendb.net

Technical documentation

docs.gigaspaces.com/latest/landing.html

www.jaguardb.com/support.html

docs.opengauss.org/en
gitee.com/opengauss/docs

ravendb.net/docs

Developer

Gigaspaces Technologies

DataJaguar, Inc.

Huawei and openGauss community

Hibernating Rhinos

Initial release

2000

2015

2019

2010

Current release

15.5, September 2020

3.3 July 2023

3.0, March 2022

5.4, July 2022

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

Open Source

GPL V3.0

Open Source

AGPL version 3, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java, C++, .Net

C++

the server part. Clients available in other languages

C, C++, Java

Server operating systems

Linux
macOS
Solaris
Windows

Linux

Linux
macOS
Raspberry Pi
Windows

Data scheme

schema-free

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.

XML can be used for describing objects metadata

Secondary indexes

yes

SQL

Support of SQL

SQL-99 for query and DML statements

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

ANSI SQL 2011

SQL-like query language (RQL)

APIs and other access methods

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

JDBC
ODBC

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Supported programming languages

.Net
C++
Java
Python
Scala

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C
C++
Java

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

yes, event driven architecture

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash)

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Multi-source replication

Source-replica replication

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Eventual Consistency

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID, Cluster-wide transaction available

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

Role-based access control

rights management via user accounts

Access rights for users, groups and roles according to SQL-standard

Authorization levels configured per client per database

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More resources

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