DBMS > Lovefield vs. Manticore Search vs. Oracle Coherence vs. OrigoDB vs. RDF4J

System Properties Comparison Lovefield vs. Manticore Search vs. Oracle Coherence vs. OrigoDB vs. RDF4J

Editorial information provided by DB-Engines

Name

Description

Embeddable relational database for web apps written in pure JavaScript

Multi-storage database for search, including full-text search.

Oracles in-memory data grid solution

A fully ACID in-memory object graph database

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Relational DBMS

Search engine

Key-value store

Document store
Object oriented DBMS

RDF store

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

google.github.io/lovefield

manticoresearch.com

www.oracle.com/java/coherence

origodb.com

rdf4j.org

Technical documentation

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

manual.manticoresearch.com

docs.oracle.com/en/middleware/standalone/coherence

origodb.com/docs

rdf4j.org/documentation

Developer

Google

Manticore Software

Oracle

Robert Friberg et al

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

Initial release

2014

2017

2007

2009

under the name LiveDB

2004

Current release

2.1.12, February 2017

6.0, February 2023

14.1, August 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

GPL version 2

commercial

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

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

Implementation language

JavaScript

C++

Java

Server operating systems

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

tested with Chrome, Firefox, IE, Safari

FreeBSD
Linux
macOS
Windows

All OS with a Java VM

Linux
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

Fixed schema

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

yes

User defined using .NET types and collections

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.

Can index from XML

can be achieved using .NET

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

SQL-like query language

APIs and other access methods

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

JCache
JPA
RESTful HTTP API

.NET Client API
HTTP API
LINQ

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

JavaScript

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

.Net
C++
Java

.Net

Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

Using read-only observers

yes

Live Events

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

horizontal partitioning

client side managed; servers are not synchronized

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Synchronous replication based on Galera library

yes, with selectable consistency level

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

depending on model

Transaction concepts

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

ACID

yes

isolated transactions for atomic changes and binary logging for safe writes

configurable

ACID

Isolation support depends on the API used

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

The original contents of fields are not stored in the Manticore index.

yes

optionally

yes

Write ahead log

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

yes

User concepts

Access control

authentification to access the cache via certificates or http basic authentication

Role based authorization

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