DBMS > Amazon DocumentDB vs. GigaSpaces vs. H2GIS vs. Hazelcast vs. MySQL

System Properties Comparison Amazon DocumentDB vs. GigaSpaces vs. H2GIS vs. Hazelcast vs. MySQL

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

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

Spatial extension of H2

A widely adopted in-memory data grid

Widely used open source RDBMS

Primary database model

Document store

Document store
Object oriented DBMS

Values are user defined objects

Spatial DBMS

Key-value store

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Graph DBMS
Search engine

Relational DBMS

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	1.91
Rank	#132	Overall
	#24	Document stores

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

aws.amazon.com/documentdb

Technical documentation

aws.amazon.com/documentdb/resources

docs.gigaspaces.com/latest/landing.html

www.h2gis.org/docs/home

hazelcast.org/imdg/docs

dev.mysql.com/doc

Developer

Gigaspaces Technologies

CNRS

Hazelcast

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2019

2000

2013

2008

1995

Current release

15.5, September 2020

5.3.6, November 2023

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; Commercial licenses available

Open Source

LGPL 3.0

Open Source

Apache Version 2; commercial licenses available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java, C++, .Net

Java

C and C++

Server operating systems

hosted

Linux
macOS
Solaris
Windows

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

schema-free

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.

XML can be used for describing objects metadata

yes

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

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

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

.Net
C++
Java
Python
Scala

Java

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

based on H2

yes

Event Listeners, Executor Services

yes

proprietary syntax

Triggers

yes, event driven architecture

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

based on H2

yes

Replicated Map

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

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

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

not for MyISAM storage engine

Transaction concepts

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

Atomic single-document operations

ACID

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

table locks or row locks depending on storage engine

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 and roles

Role-based access control

yes

based on H2

Role-based access control

Users with fine-grained authorization concept

no user groups or roles

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