DBMS > Amazon DocumentDB vs. GridGain vs. mSQL vs. Yaacomo

System Properties Comparison Amazon DocumentDB vs. GridGain vs. mSQL vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

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

mSQL (Mini SQL) is a simple and lightweight RDBMS

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Document store

Key-value store
Relational DBMS

Relational DBMS

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

Score	1.89
Rank	#137	Overall
	#24	Document stores

Score	1.53
Rank	#155	Overall
	#26	Key-value stores
	#73	Relational DBMS

Score	1.67
Rank	#151	Overall
	#70	Relational DBMS

Website

aws.amazon.com/documentdb

www.gridgain.com

hughestech.com.au/products/msql

yaacomo.com

Technical documentation

aws.amazon.com/documentdb/resources

www.gridgain.com/docs/index.html

Developer

GridGain Systems, Inc.

Hughes Technologies

Q2WEB GmbH

Initial release

2019

2007

1994

2009

Current release

GridGain 8.5.1

4.4, October 2021

License

Commercial or Open Source

commercial

free licenses can be provided

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

Server operating systems

hosted

Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Android
Linux
Windows

Data scheme

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

Secondary indexes

yes

SQL

Support of SQL

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

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

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

JDBC
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C#
C++
Java
PHP
Python
Ruby
Scala

C
C++
Delphi
Java
Perl
PHP
Tcl

Server-side scripts

Stored procedures

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

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes (replicated cache)

none

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)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

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

Atomic single-document operations

ACID

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

Access rights for users and roles

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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