DBMS > ArcadeDB vs. GridGain vs. H2 vs. Quasardb

System Properties Comparison ArcadeDB vs. GridGain vs. H2 vs. Quasardb

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Fast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewritten

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

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Distributed, high-performance timeseries database

Primary database model

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.02
Rank	#374	Overall
	#52	Document stores
	#38	Graph DBMS
	#56	Key-value stores
	#38	Time Series DBMS

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

Score	7.57
Rank	#47	Overall
	#30	Relational DBMS

Score	0.13
Rank	#332	Overall
	#28	Time Series DBMS

Website

Technical documentation

docs.arcadedb.com

www.gridgain.com/docs/index.html

www.h2database.com/html/main.html

doc.quasar.ai/master

Developer

Arcade Data

GridGain Systems, Inc.

Thomas Mueller

quasardb

Initial release

2021

2007

2005

2009

Current release

September 2021

GridGain 8.5.1

2.2.220, July 2023

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial, open source

Open Source

dual-licence (Mozilla public license, Eclipse public license)

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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

Java, C++, .Net, Python, REST, SQL

Java

C++

Server operating systems

All OS with a Java VM

Linux
OS X
Solaris
Windows
z/OS

All OS with a Java VM

BSD
Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

SQL

Support of SQL

SQL-like query language, no joins

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

yes

SQL-like query language

APIs and other access methods

JDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin

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

JDBC
ODBC

HTTP API

Supported programming languages

Java

C#
C++
Java
PHP
Python
Ruby
Scala

Java

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

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

Java Stored Procedures and User-Defined Functions

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes (replicated cache)

With clustering: 2 database servers on different computers operate on identical copies of a database

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

by using LevelDB

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Role-based access control
Security Hooks for custom implementations

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

GridGain Advances Its Unified Real-Time Data Platform to Help Enterprises Accelerate AI-Driven Data Processing
10 July 2024, insideainews.com

GridGain Sponsoring Strategic AI and Kafka Conferences This Month
4 September 2024, Datanami

GridGain in-memory data and generative AI
10 May 2024, Blocks & Files

Data Management News for the Week of July 12; Updates from Cloudera, HerculesAI, Oracle & More
12 July 2024, Solutions Review

Lalit Ahuja
13 August 2024, Forbes

provided by Google News

NVIDIA to Present Innovations at Hot Chips That Boost Data Center Performance and Energy Efficiency
23 August 2024, NVIDIA Blog

Exploring secular variation of the gravitational constant from high-resolution quasar spectra
6 July 2024, Nature.com

Record quasar is most luminous object in the universe
20 February 2024, EarthSky

When a Quasar Remote Access Tool Falls Into the Wrong Hands
23 February 2024, Darktrace

Ultrabright Quasar Lit Up the Early Universe
9 January 2019, Livescience.com

provided by Google News

Share this page