DBMS > Apache Jena - TDB vs. Kdb vs. Microsoft SQL Server vs. Oracle Berkeley DB

System Properties Comparison Apache Jena - TDB vs. Kdb vs. Microsoft SQL Server vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines

Name

Description

A RDF storage and query DBMS, shipped as an optional-use component of the Apache Jena framework

High performance Time Series DBMS

Microsofts flagship relational DBMS

Widely used in-process key-value store

Primary database model

RDF store

Time Series DBMS
Vector DBMS

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Secondary database models

Relational DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	3.62
Rank	#83	Overall
	#3	RDF stores

Score	7.71
Rank	#49	Overall
	#2	Time Series DBMS
	#1	Vector DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Website

jena.apache.org/documentation/tdb/index.html

kx.com

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

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

Technical documentation

jena.apache.org/documentation/tdb/index.html

code.kx.com

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

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

Developer

Apache Software Foundation

originally developed by HP Labs

Kx Systems, a division of First Derivatives plc

Microsoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2000

kdb was released 2000, kdb+ in 2003

1989

1994

Current release

4.9.0, July 2023

3.6, May 2018

SQL Server 2022, November 2022

18.1.40, May 2020

License

Commercial or Open Source

Open Source

Apache License, Version 2.0

commercial

free 32-bit version

commercial

restricted free version is available

Open Source

commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

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Implementation language

Java

C++

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

Server operating systems

All OS with a Java VM

Linux
OS X
Solaris
Windows

Linux
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

yes

RDF Schemas

yes

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

only with the Berkeley DB XML edition

Secondary indexes

yes

table attribute 'grouped'

yes

SQL

Support of SQL

SQL-like query language (q)

yes

SQL interfaced based on SQLite is available

APIs and other access methods

Fuseki

REST-style SPARQL HTTP Interface
Jena RDF API
RIO

RDF Input/Output

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

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

Supported programming languages

Java

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
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

Server-side scripts

Stored procedures

yes

user defined functions

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

Triggers

yes

via event handler

yes

with views

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes, but depending on the SQL-Server Edition

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

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

TDB Transactions

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

Access control via Jena Security

rights management via user accounts

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
» more

Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
» more

Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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Licensing and pricing models

upon request
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