DBMS > GridGain vs. Hazelcast vs. Postgres-XL

System Properties Comparison GridGain vs. Hazelcast vs. Postgres-XL

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Editorial information provided by DB-Engines

Name

Description

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

A widely adopted in-memory data grid

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Primary database model

Key-value store
Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Spatial 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	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Website

www.gridgain.com

hazelcast.com

www.postgres-xl.org

Technical documentation

www.gridgain.com/docs/index.html

hazelcast.org/imdg/docs

www.postgres-xl.org/documentation

Developer

GridGain Systems, Inc.

Hazelcast

Initial release

2007

2008

2014

since 2012, originally named StormDB

Current release

GridGain 8.5.1

5.3.6, November 2023

10 R1, October 2018

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

Mozilla public license

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
Solaris
Windows

All OS with a Java VM

Linux
macOS

Data scheme

yes

schema-free

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

the object must implement a serialization strategy

yes

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

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

SQL-like query language

yes

distributed, parallel query execution

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

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

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

yes

Event Listeners, Executor Services

user defined functions

Triggers

yes (cache interceptors and events)

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

Replicated Map

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

Immediate 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

MVCC

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

Role-based access control

fine grained access rights according to SQL-standard

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More resources

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