DBMS > LeanXcale vs. Lovefield vs. RDF4J vs. SiteWhere

System Properties Comparison LeanXcale vs. Lovefield vs. RDF4J vs. SiteWhere

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Editorial information provided by DB-Engines

Name

Description

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

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.

M2M integration platform for persisting/querying time series data

Primary database model

Key-value store
Relational DBMS

Relational DBMS

RDF store

Time Series DBMS

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

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	0.06
Rank	#383	Overall
	#43	Time Series DBMS

Website

www.leanxcale.com

google.github.io/lovefield

rdf4j.org

github.com/sitewhere/sitewhere

Technical documentation

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

rdf4j.org/documentation

sitewhere1.sitewhere.io/index.html

Developer

LeanXcale

Google

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

SiteWhere

Initial release

2015

2014

2004

2010

Current release

2.1.12, February 2017

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Common Public Attribution License Version 1.0

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

JavaScript

Java

Server operating systems

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

tested with Chrome, Firefox, IE, Safari

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Data scheme

yes

RDF Schemas

predefined scheme

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.

Secondary indexes

yes

SQL

Support of SQL

yes

through Apache Derby

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

HTTP REST

Supported programming languages

C
Java
Scala

JavaScript

Java
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

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

yes

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

Users with fine-grained authorization concept

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More resources

Recent citations in the news

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provided by Google News

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11 July 2017, Open Source For You

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