DBMS > Bangdb vs. Lovefield vs. RDF4J vs. Teradata

System Properties Comparison Bangdb vs. Lovefield vs. RDF4J vs. Teradata

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Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

Embeddable relational database for web apps written in pure JavaScript

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

RDF store

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Website

bangdb.com

google.github.io/lovefield

rdf4j.org

www.teradata.com

Technical documentation

docs.bangdb.com

github.com/google/lovefield/blob/master/docs/spec_index.md

rdf4j.org/documentation

docs.teradata.com

Developer

Sachin Sinha, BangDB

Google

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Teradata

Initial release

2012

2014

2004

1984

Current release

BangDB 2.0, October 2021

2.1.12, February 2017

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

JavaScript

Java

Server operating systems

Linux

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux
OS X
Unix
Windows

hosted
Linux

Data scheme

schema-free

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

SQL like support with command line tool

SQL-like query language

via JavaScript builder pattern

yes

SQL 2016 + extensions

APIs and other access methods

Proprietary protocol
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

C
C#
C++
Java
Python

JavaScript

Java
PHP
Python

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes, Notifications (with Streaming only)

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

none

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

none

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

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

in-memory storage is supported as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes

using MemoryDB

yes

User concepts

Access control

yes (enterprise version only)

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

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2 April 2013, Paul Andlinger

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