DBMS > AgensGraph vs. GigaSpaces vs. Hazelcast

System Properties Comparison AgensGraph vs. GigaSpaces vs. Hazelcast

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Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

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

A widely adopted in-memory data grid

Primary database model

Graph DBMS
Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Key-value store

Secondary database models

Graph DBMS
Search engine

Document store

JSON support with IMDG 3.12

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

Score	0.16
Rank	#333	Overall
	#30	Graph DBMS
	#146	Relational DBMS

Score	1.02
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	6.87
Rank	#55	Overall
	#6	Key-value stores

Website

bitnine.net/agensgraph

www.gigaspaces.com

hazelcast.com

Technical documentation

bitnine.net/documentation

docs.gigaspaces.com/latest/landing.html

hazelcast.org/imdg/docs

Developer

Bitnine Global Inc.

Gigaspaces Technologies

Hazelcast

Initial release

2016

2000

2008

Current release

2.1, December 2018

15.5, September 2020

5.3.6, November 2023

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

Apache Version 2; Commercial licenses available

Open Source

Apache Version 2; commercial licenses 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

Java

Server operating systems

Linux
OS X
Windows

Linux
macOS
Solaris
Windows

All OS with a Java VM

Data scheme

depending on used data model

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

yes

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-99 for query and DML statements

SQL-like query language

APIs and other access methods

Cypher Query Language
JDBC

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

JCache
JPA
Memcached protocol
RESTful HTTP API

Supported programming languages

C
Java
JavaScript
Python

.Net
C++
Java
Python
Scala

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

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

Triggers

yes, event driven architecture

yes

Events

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

Replicated Map

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

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

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

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

fine grained access rights according to SQL-standard

Role-based access control

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