DBMS > EXASOL vs. GridGain vs. gStore vs. WakandaDB

System Properties Comparison EXASOL vs. GridGain vs. gStore vs. WakandaDB

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Editorial information provided by DB-Engines

Name

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

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

A native Graph DBMS to store and maintain very large RDF datasets.

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Relational DBMS

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Graph DBMS
RDF store

Object oriented DBMS

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

Score	1.66
Rank	#138	Overall
	#64	Relational DBMS

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	0.03
Rank	#367	Overall
	#37	Graph DBMS
	#18	RDF stores

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

Technical documentation

www.exasol.com/resources

www.gridgain.com/docs/index.html

en.gstore.cn/#/enDocs

wakanda.github.io/doc

Developer

Exasol

GridGain Systems, Inc.

Wakanda SAS

Initial release

2000

2007

2016

2012

Current release

GridGain 8.5.1

1.2, November 2023

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

commercial, open source

Open Source

BSD

Open Source

AGPLv3, extended 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, Python, REST, SQL

C++

C++, JavaScript

Server operating systems

Linux
OS X
Solaris
Windows
z/OS

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free and OWL/RDFS-schema support

yes

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

yes

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

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

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

HTTP API
SPARQL 1.1

RESTful HTTP API

Supported programming languages

Java
Lua
Python
R

C#
C++
Java
PHP
Python
Ruby
Scala

C++
Java
JavaScript (Node.js)
PHP
Python

JavaScript

Server-side scripts

Stored procedures

user defined functions

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

yes

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID

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

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

Role-based access control
Security Hooks for custom implementations

Users, roles and permissions, Role-Based Access Control (RBAC) supported

yes

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