DBMS > Ehcache vs. Lovefield vs. RDF4J vs. Spark SQL

System Properties Comparison Ehcache vs. Lovefield vs. RDF4J vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

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.

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

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

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.ehcache.org

google.github.io/lovefield

rdf4j.org

spark.apache.org/sql

Technical documentation

www.ehcache.org/documentation

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

rdf4j.org/documentation

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Terracotta Inc, owned by Software AG

Google

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

Apache Software Foundation

Initial release

2009

2014

2004

2014

Current release

3.10.0, March 2022

2.1.12, February 2017

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache 2.0

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

Java

JavaScript

Java

Scala

Server operating systems

All OS with a Java VM

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

schema-free

yes

RDF Schemas

yes

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

SQL-like query language

via JavaScript builder pattern

SQL-like DML and DDL statements

APIs and other access methods

JCache

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC

Supported programming languages

Java

JavaScript

Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Triggers

yes

Cache Event Listeners

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

supports JTA and can work as an XA resource

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

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