DBMS > GigaSpaces vs. Microsoft SQL Server vs. Oracle Berkeley DB vs. SQLite

System Properties Comparison GigaSpaces vs. Microsoft SQL Server vs. Oracle Berkeley DB vs. SQLite

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Editorial information provided by DB-Engines

Name

Description

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Microsofts flagship relational DBMS

Widely used in-process key-value store

Widely used embeddable, in-process RDBMS

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Secondary database models

Graph DBMS
Search engine

Document store
Graph DBMS
Spatial DBMS

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

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

www.gigaspaces.com

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

www.oracle.com/database/technologies/related/berkeleydb.html

www.sqlite.org

Technical documentation

docs.gigaspaces.com/latest/landing.html

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

docs.oracle.com/cd/E17076_05/html/index.html

www.sqlite.org/docs.html

Developer

Gigaspaces Technologies

Microsoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Dwayne Richard Hipp

Initial release

2000

1989

1994

2000

Current release

15.5, September 2020

SQL Server 2022, November 2022

18.1.40, May 2020

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

commercial

restricted free version is available

Open Source

commercial license available

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

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Implementation language

Java, C++, .Net

C++

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

Linux
macOS
Solaris
Windows

Linux
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

server-less

Data scheme

schema-free

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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.

XML can be used for describing objects metadata

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

SQL interfaced based on SQLite is available

yes

SQL-92 is not fully supported

APIs and other access methods

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

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

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

.Net
C++
Java
Python
Scala

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

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

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

Triggers

yes, event driven architecture

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes, but depending on the SQL-Server Edition

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

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

via file-system locks

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

Role-based access control

fine grained access rights according to SQL-standard

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