DBMS > Badger vs. NSDb vs. RDFox

System Properties Comparison Badger vs. NSDb vs. RDFox

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Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

High performance knowledge graph and semantic reasoning engine

Primary database model

Key-value store

Time Series DBMS

Graph DBMS
RDF store

Secondary database models

Relational DBMS

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

Score	0.20
Rank	#325	Overall
	#49	Key-value stores

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Score	0.24
Rank	#309	Overall
	#25	Graph DBMS
	#14	RDF stores

Website

github.com/dgraph-io/badger

nsdb.io

www.oxfordsemantic.tech

Technical documentation

godoc.org/github.com/dgraph-io/badger

nsdb.io/Architecture

docs.oxfordsemantic.tech

Developer

DGraph Labs

Oxford Semantic Technologies

Initial release

2017

Current release

6.0, Septermber 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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, Scala

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
macOS

Linux
macOS
Windows

Data scheme

schema-free

yes

RDF schemas

Typing

predefined data types such as float or date

yes: int, bigint, decimal, string

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.

Secondary indexes

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

APIs and other access methods

gRPC
HTTP REST
WebSocket

RESTful HTTP API
SPARQL 1.1

Supported programming languages

Java
Scala

C
Java

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

replication via a shared file system

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

Foreign keys

Referential integrity

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

Using Apache Lucene

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

Roles, resources, and access types

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