DBMS > Lovefield vs. RDF4J vs. Snowflake vs. Stardog vs. Typesense

System Properties Comparison Lovefield vs. RDF4J vs. Snowflake vs. Stardog vs. Typesense

Editorial information provided by DB-Engines

Name

Description

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.

Cloud-based data warehousing service for structured and semi-structured data

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

A typo-tolerant, in-memory search engine optimized for instant search-as-you-type experiences and developer productivity

Primary database model

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

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

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

Score	0.76
Rank	#219	Overall
	#14	Search engines

Website

google.github.io/lovefield

Technical documentation

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

rdf4j.org/documentation

docs.snowflake.net/manuals/index.html

docs.stardog.com

typesense.org/docs

Developer

Google

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

Snowflake Computing Inc.

Stardog-Union

Initial release

2014

2004

2014

2010

2015

Current release

2.1.12, February 2017

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

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

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

JavaScript

Java

C++

Server operating systems

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

tested with Chrome, Firefox, IE, Safari

Linux
OS X
Unix
Windows

hosted

Linux
macOS
Windows

Linux

Data scheme

yes

RDF Schemas

yes

support of semi-structured data formats (JSON, XML, Avro)

schema-free and OWL/RDFS-schema support

schema-free

pre-defined schema optional

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

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

yes

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

yes

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

APIs and other access methods

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

CLI Client
JDBC
ODBC

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

RESTful HTTP API

Supported programming languages

JavaScript

Java
PHP
Python

JavaScript (Node.js)
Python

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

.Net

community maintained
Clojure

community maintained
Dart

community maintained
Go

community maintained
Java

community maintained
JavaScript
Perl

community maintained
PHP
Python
Ruby
Rust

community maintained
Swift

community maintained

Server-side scripts

Stored procedures

yes

user defined functions

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

Using read-only observers

yes

similar concept for controling cloud resources

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

none

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Multi-source replication in HA-Cluster

Multi-source replication using RAFT

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

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

Durability

Support for making data persistent

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

using MemoryDB

yes

User concepts

Access control

Users with fine-grained authorization concept, user roles and pluggable authentication

Access rights for users and roles

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