DBMS > GridDB vs. Kyligence Enterprise vs. Lovefield vs. RDF4J

System Properties Comparison GridDB vs. Kyligence Enterprise vs. Lovefield vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

Scalable in-memory time series database optimized for IoT and Big Data

A distributed analytics engine for big data, built on top of Apache Kylin

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.

Primary database model

Time Series DBMS

Relational DBMS

RDF store

Secondary database models

Key-value store
Relational DBMS

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Trend Chart

Score	1.91
Rank	#123	Overall
	#10	Time Series DBMS

Score	0.44
Rank	#259	Overall
	#118	Relational DBMS

Score	0.22
Rank	#306	Overall
	#138	Relational DBMS

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Website

griddb.net

kyligence.io/kyligence-enterprise

google.github.io/lovefield

rdf4j.org

Technical documentation

docs.griddb.net

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

rdf4j.org/documentation

Developer

Toshiba Corporation

Kyligence, Inc.

Google

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

Initial release

2013

2016

2014

2004

Current release

5.1, August 2022

2.1.12, February 2017

License

Commercial or Open Source

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

commercial

Open Source

Apache 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

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

Data scheme

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

yes

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

ANSI SQL for queries (using Apache Calcite)

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

JavaScript

Java
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID at container level

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

yes

in-memory storage is supported as well

In-memory capabilities

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

yes

using MemoryDB

User concepts

Access control

Access rights for users can be defined per database

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