DBMS > BaseX vs. Infobright vs. Oracle Berkeley DB vs. TDengine vs. TimesTen

System Properties Comparison BaseX vs. Infobright vs. Oracle Berkeley DB vs. TDengine vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Widely used in-process key-value store

Time Series DBMS and big data platform

In-Memory RDBMS compatible to Oracle

Primary database model

Native XML DBMS

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Time Series DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	1.73
Rank	#142	Overall
	#4	Native XML DBMS

Score	0.96
Rank	#194	Overall
	#91	Relational DBMS

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

Score	2.60
Rank	#107	Overall
	#8	Time Series DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

basex.org

ignitetech.com/softwarelibrary/infobrightdb

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

github.com/taosdata/TDengine
tdengine.com

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

Technical documentation

docs.basex.org

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

docs.tdengine.com

docs.oracle.com/database/timesten-18.1

Developer

BaseX GmbH

Ignite Technologies Inc.; formerly InfoBright Inc.

Oracle

originally developed by Sleepycat, which was acquired by Oracle

TDEngine, previously Taos Data

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2007

2005

1994

2019

1998

Current release

10.7, August 2023

18.1.40, May 2020

3.0, August 2022

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

BSD license

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

Open Source

commercial license available

Open Source

AGPL V3, also commercial editions available

commercial

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

Server operating systems

Linux
OS X
Windows

Linux
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

XQuery supports 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

only with the Berkeley DB XML edition

Secondary indexes

yes

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

Standard SQL with extensions for time-series applications

yes

APIs and other access methods

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

ADO.NET
JDBC
ODBC

JDBC
RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.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

C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Rust

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

PL/SQL

Triggers

yes

via events

yes

only for the SQL API

yes, via alarm monitoring

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

multiple readers, single writer

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Users with fine-grained authorization concept on 4 levels

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

TDengine™ is a next generation data historian purpose-built for Industry 4.0 and...
» more

Competitive advantages

High Performance at any Scale: TDengine is purpose-built for handling massive industrial...
» more

Typical application scenarios

TDengine is designed for Industrial IoT scenarios, including: Manufacturing Connected...
» more

Market metrics

TDengine has garnered over 22,500 stars on GitHub and is used in over 50 countries...
» more

Licensing and pricing models

TDengine OSS is an open source, cloud native time series database. It includes built-in...
» more

News

Can Typical Time-Series Databases Replace Data Historians?
8 May 2024

TDengine 3.3.0.0 Release Notes
7 May 2024

How to Unlock Value from Industrial Data with AI and ML Technology
6 May 2024

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19 April 2024

Why We Need the Next Generation Data Historian
15 April 2024

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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