DBMS > AlaSQL vs. Amazon DocumentDB vs. GridGain vs. Hazelcast vs. MySQL

System Properties Comparison AlaSQL vs. Amazon DocumentDB vs. GridGain vs. Hazelcast vs. MySQL

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

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

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

A widely adopted in-memory data grid

Widely used open source RDBMS

Primary database model

Document store
Relational DBMS

Document store

Key-value store
Relational DBMS

Key-value store

Relational DBMS

Key/Value like access via memcached API

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	0.46
Rank	#260	Overall
	#40	Document stores
	#121	Relational DBMS

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

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

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

alasql.org

aws.amazon.com/documentdb

www.gridgain.com

hazelcast.com

www.mysql.com

Technical documentation

github.com/AlaSQL/alasql

aws.amazon.com/documentdb/resources

www.gridgain.com/docs/index.html

hazelcast.org/imdg/docs

dev.mysql.com/doc

Developer

Andrey Gershun & Mathias R. Wulff

GridGain Systems, Inc.

Hazelcast

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2014

2019

2007

2008

1995

Current release

GridGain 8.5.1

5.3.6, November 2023

8.4.0, April 2024

License

Commercial or Open Source

Open Source

MIT-License

commercial

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

JavaScript

Java, C++, .Net

Java

C and C++

Server operating systems

server-less, requires a JavaScript environment (browser, Node.js)

hosted

Linux
OS X
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.

yes

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

Close to SQL99, but no user access control, stored procedures and host language bindings.

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

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

JavaScript API

proprietary protocol using JSON (MongoDB compatible)

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

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

JavaScript

Go
Java
JavaScript (Node.js)
PHP
Python

C#
C++
Java
PHP
Python
Ruby
Scala

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

yes

Event Listeners, Executor Services

yes

proprietary syntax

Triggers

yes

yes (cache interceptors and events)

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

yes (replicated cache)

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 (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

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

yes

only for local storage and DOM-storage

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

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

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

Security Hooks for custom implementations

Role-based access control

Users with fine-grained authorization concept

no user groups or roles

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