DBMS > Drizzle vs. GridGain vs. JaguarDB vs. Microsoft SQL Server vs. SiteWhere

System Properties Comparison Drizzle vs. GridGain vs. JaguarDB vs. Microsoft SQL Server vs. SiteWhere

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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

Performant, highly scalable DBMS for AI and IoT applications

Microsofts flagship relational DBMS

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Key-value store
Vector DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	0.00
Rank	#385	Overall
	#61	Key-value stores
	#16	Vector DBMS

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	0.07
Rank	#347	Overall
	#33	Time Series DBMS

Website

www.gridgain.com

www.jaguardb.com

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

github.com/sitewhere/sitewhere

Technical documentation

www.gridgain.com/docs/index.html

www.jaguardb.com/support.html

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

sitewhere1.sitewhere.io/index.html

Developer

Drizzle project, originally started by Brian Aker

GridGain Systems, Inc.

DataJaguar, Inc.

Microsoft

SiteWhere

Initial release

2008

2007

2015

1989

2010

Current release

7.2.4, September 2012

GridGain 8.5.1

3.3 July 2023

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

GNU GPL

commercial, open source

Open Source

GPL V3.0

commercial

restricted free version is available

Open Source

Common Public Attribution License Version 1.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java, C++, .Net, Python, REST, SQL

C++

the server part. Clients available in other languages

C++

Java

Server operating systems

FreeBSD
Linux
OS X

Linux
OS X
Solaris
Windows
z/OS

Linux

Linux
Windows

Linux
OS X
Windows

Data scheme

yes

predefined scheme

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

yes

with proprietary extensions

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

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

APIs and other access methods

JDBC

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

JDBC
ODBC

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

HTTP REST

Supported programming languages

C
C++
Java
PHP

C#
C++
Java
PHP
Python
Ruby
Scala

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

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)

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

Triggers

hooks for callbacks inside the server can be used.

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes (replicated cache)

Multi-source replication

yes, but depending on the SQL-Server Edition

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Role-based access control
Security Hooks for custom implementations

rights management via user accounts

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

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