DBMS > GridGain vs. JaguarDB vs. LeanXcale vs. Oracle Rdb

System Properties Comparison GridGain vs. JaguarDB vs. LeanXcale vs. Oracle Rdb

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Editorial information provided by DB-Engines

Name

Description

GridGain is an in-memory computing platform, built on Apache Ignite

Performant, highly scalable DBMS for AI and IoT applications

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Primary database model

Key-value store
Relational DBMS

Key-value store
Vector DBMS

Key-value store
Relational DBMS

Relational DBMS

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

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

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

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Website

www.gridgain.com

www.jaguardb.com

www.leanxcale.com

www.oracle.com/database/technologies/related/rdb.html

Technical documentation

www.gridgain.com/docs/index.html

www.jaguardb.com/support.html

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

GridGain Systems, Inc.

DataJaguar, Inc.

LeanXcale

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2007

2015

1984

Current release

GridGain 8.5.1

3.3 July 2023

7.4.1.1, 2021

License

Commercial or Open Source

commercial

Open Source

GPL V3.0

commercial

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

Server operating systems

Linux
OS X
Solaris
Windows

Linux

HP Open VMS

Data scheme

yes

Flexible Schema (defined schema, partial schema, 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.

yes

Secondary indexes

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

through Apache Derby

yes

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

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

C
Java
Scala

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

Triggers

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

Security Hooks for custom implementations

rights management via user accounts

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More resources

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