DBMS > NSDb vs. Oracle Berkeley DB vs. Stardog vs. Weaviate

System Properties Comparison NSDb vs. Oracle Berkeley DB vs. Stardog vs. Weaviate

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Editorial information provided by DB-Engines

Name

Description

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

Widely used in-process key-value store

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

An AI-native realtime vector database engine that integrates scalable machine learning models.

Primary database model

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Graph DBMS
RDF store

Vector DBMS

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

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

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

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	1.52
Rank	#153	Overall
	#5	Vector DBMS

Website

nsdb.io

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

www.stardog.com

github.com/weaviate/weaviate
weaviate.io

Technical documentation

nsdb.io/Architecture

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

docs.stardog.com

weaviate.io/developers/weaviate

Developer

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Stardog-Union

Weaviate B.V.

Initial release

2017

1994

2010

2019

Current release

18.1.40, May 2020

7.3.0, May 2020

1.19, May 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

commercial license available

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Open Source

commercial license available with Weaviate Enterprise

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

Java

Server operating systems

Linux
macOS

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
macOS
Windows

Data scheme

schema-free

schema-free and OWL/RDFS-schema support

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

yes: int, bigint, decimal, string

yes

string, int, float, geo point, date, cross reference, fuzzy references

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

Import/export of XML data possible

Secondary indexes

all fields are automatically indexed

yes

supports real-time indexing in full-text and geospatial

yes

all data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed

SQL

Support of SQL

SQL-like query language

yes

SQL interfaced based on SQLite is available

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

GraphQL is used as query language

APIs and other access methods

gRPC
HTTP REST
WebSocket

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

Java
Scala

.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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

only for the SQL API

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication in HA-Cluster

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency in HA-Cluster

Eventual Consistency

Foreign keys

Referential integrity

yes

relationships in graphs

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

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

Access rights for users and roles

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

Weaviate is an open source vector database that is robust, scalable, cloud-native,...
» more

Competitive advantages

Flexible deployment - Free, open source or fully-managed cloud vector database service...
» more

Typical application scenarios

As a database supporting the development of generative AI and semantic search applications...
» more

Key customers

All companies that have data.
» more

Market metrics

As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
» more

Licensing and pricing models

Weaviate is open-source, and free to use. Weaviate is also available as a fully managed...
» more

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

Related products and services

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More resources

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