DBMS > GridGain vs. IBM Db2 vs. Microsoft SQL Server vs. SiteWhere vs. SWC-DB

System Properties Comparison GridGain vs. IBM Db2 vs. Microsoft SQL Server vs. SiteWhere vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

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

Common in IBM host environments, 2 different versions for host and Windows/Linux

Microsofts flagship relational DBMS

M2M integration platform for persisting/querying time series data

A high performance, scalable Wide Column DBMS

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

Time Series DBMS

Wide column store

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Document store
Graph DBMS
Spatial DBMS

Time Series DBMS

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

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

Score	127.49
Rank	#8	Overall
	#5	Relational DBMS

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Score	0.06
Rank	#367	Overall
	#36	Time Series DBMS

Score	0.01
Rank	#387	Overall
	#13	Wide column stores

Website

www.gridgain.com

www.ibm.com/products/db2

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

github.com/sitewhere/sitewhere

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

www.gridgain.com/docs/index.html

www.ibm.com/docs/en/db2

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

sitewhere1.sitewhere.io/index.html

Developer

GridGain Systems, Inc.

IBM

Microsoft

SiteWhere

Alex Kashirin

Initial release

2007

1983

host version

1989

2010

2020

Current release

GridGain 8.5.1

12.1, October 2016

SQL Server 2022, November 2022

0.5, April 2021

License

Commercial or Open Source

commercial

free version is available

commercial

restricted free version is available

Open Source

Common Public Attribution License Version 1.0

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, C++, .Net

C and C++

C++

Java

C++

Server operating systems

Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux
Windows

Linux
OS X
Windows

Linux

Data scheme

yes

predefined scheme

schema-free

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

yes

SQL-like query language

APIs and other access methods

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

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

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

HTTP REST

Proprietary protocol
Thrift

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

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

C++

Server-side scripts

Stored procedures

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

yes

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

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

only with Windows/Unix/Linux Version

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

Sharding

based on HBase

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

with separate tools (MQ, InfoSphere)

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

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

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.