DBMS > Amazon DynamoDB vs. GridGain vs. Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB vs. mSQL

System Properties Comparison Amazon DynamoDB vs. GridGain vs. Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB vs. mSQL

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

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

Search-as-a-service for web and mobile app development

Globally distributed, horizontally scalable, multi-model database service

mSQL (Mini SQL) is a simple and lightweight RDBMS

Primary database model

Document store
Key-value store

Key-value store
Relational DBMS

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Vector DBMS

Spatial DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

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

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Website

aws.amazon.com/dynamodb

www.gridgain.com

azure.microsoft.com/en-us/services/search

azure.microsoft.com/services/cosmos-db

hughestech.com.au/products/msql

Technical documentation

docs.aws.amazon.com/dynamodb

www.gridgain.com/docs/index.html

learn.microsoft.com/en-us/azure/search

learn.microsoft.com/azure/cosmos-db

Developer

Amazon

GridGain Systems, Inc.

Microsoft

Hughes Technologies

Initial release

2012

2007

2015

2014

1994

Current release

GridGain 8.5.1

4.4, October 2021

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

free licenses can be provided

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

Server operating systems

hosted

Linux
OS X
Solaris
Windows

hosted

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

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

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

SQL-like query language

A subset of ANSI SQL is implemented

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

APIs and other access methods

RESTful HTTP API

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

RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
ODBC

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

C#
Java
JavaScript
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C
C++
Delphi
Java
Perl
PHP
Tcl

Server-side scripts

Stored procedures

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

JavaScript

Triggers

yes

by integration with AWS Lambda

yes (cache interceptors and events)

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes (replicated cache)

yes

Implicit feature of the cloud service

yes

Implicit feature of the cloud service

none

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)

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

none

Foreign keys

Referential integrity

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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 can be defined via the AWS Identity and Access Management (IAM)

Security Hooks for custom implementations

yes

using Azure authentication

Access rights can be defined down to the item level

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