DBMS > AntDB vs. GridGain vs. JaguarDB vs. Microsoft Azure Cosmos DB vs. Microsoft SQL Server

System Properties Comparison AntDB vs. GridGain vs. JaguarDB vs. Microsoft Azure Cosmos DB vs. Microsoft SQL Server

Editorial information provided by DB-Engines

Name

Description

A scalable, multi-tenant, MPP-architectured RDBMS for OLTP and OLAP operations, highly compatible with Oracle

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

Performant, highly scalable DBMS for AI and IoT applications

Globally distributed, horizontally scalable, multi-model database service

Microsofts flagship relational DBMS

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Key-value store
Vector DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	0.25
Rank	#303	Overall
	#137	Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

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

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

www.asiainfo.com/en_us/product_aisware_antdb_detail.html

www.gridgain.com

www.jaguardb.com

azure.microsoft.com/services/cosmos-db

www.microsoft.com/en-us/sql-server

Technical documentation

www.gridgain.com/docs/index.html

www.jaguardb.com/support.html

learn.microsoft.com/azure/cosmos-db

learn.microsoft.com/en-US/sql/sql-server

Developer

AsiaInfo Technologies Limited

GridGain Systems, Inc.

DataJaguar, Inc.

Microsoft

Initial release

2007

2015

2014

1989

Current release

GridGain 8.5.1

3.3 July 2023

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Open Source

GPL V3.0

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java, C++, .Net

C++

the server part. Clients available in other languages

C++

Server operating systems

Linux
OS X
Solaris
Windows

Linux

hosted

Linux
Windows

Data scheme

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

SQL2016 compliant

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

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like query language

yes

APIs and other access methods

JDBC
MySQL protocol compliant
ODBC

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

JDBC
ODBC

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

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

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

JavaScript

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

yes (cache interceptors and events)

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Multi-source replication

yes

Implicit feature of the cloud service

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Immediate Consistency

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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

Security Hooks for custom implementations

rights management via user accounts

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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