DBMS > GridGain vs. Hazelcast vs. Hyprcubd vs. SQLite vs. UniData,UniVerse

System Properties Comparison GridGain vs. Hazelcast vs. Hyprcubd vs. SQLite vs. UniData,UniVerse

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

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

A widely adopted in-memory data grid

Serverless Time Series DBMS

Widely used embeddable, in-process RDBMS

MultiValue database and application server with SQL mapping layer and meta database capabilities

Primary database model

Key-value store
Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

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	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	3.16
Rank	#97	Overall
	#2	Multivalue DBMS

Website

www.gridgain.com

hazelcast.com

hyprcubd.com (offline)

www.sqlite.org

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-unidata

Technical documentation

www.gridgain.com/docs/index.html

hazelcast.org/imdg/docs

www.sqlite.org/docs.html

docs.rocketsoftware.com/bundle?cluster=true&labelkey=unidata&labelkey=prod_unidata

Developer

GridGain Systems, Inc.

Hazelcast

Hyprcubd, Inc.

Dwayne Richard Hipp

Rocket Software

Initial release

2007

2008

2000

1985

Current release

GridGain 8.5.1

5.3.6, November 2023

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

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

Java

Server operating systems

Linux
OS X
Solaris
Windows

All OS with a Java VM

hosted

server-less

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-free

yes

dynamic column types

schema-free

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

yes

not rigid because of 'dynamic typing' concept.

optional

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

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

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

SQL-like query language

yes

SQL-92 is not fully supported

yes

APIs and other access methods

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

JCache
JPA
Memcached protocol
RESTful HTTP API

gRPC (https)

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Java API

JPA
JDBC
ODBC
OLE DB
Proprietary protocol
RESTful HTTP API
SOAP-based API

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

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
Basic

U2 Basic
C
Java

Server-side scripts

Stored procedures

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

yes

Event Listeners, Executor Services

yes

Triggers

yes (cache interceptors and events)

yes

Events

yes

U2 Basic

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)

yes

Replicated Map

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

ACID

configurable

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

Role-based access control

token access

Access rights according to SQL-standard and operating system based

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