DBMS > DataFS vs. Lovefield vs. Oracle NoSQL vs. SQLite

System Properties Comparison DataFS vs. Lovefield vs. Oracle NoSQL vs. SQLite

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Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Embeddable relational database for web apps written in pure JavaScript

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Widely used embeddable, in-process RDBMS

Primary database model

Object oriented DBMS

Relational DBMS

Document store
Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Graph DBMS

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

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

newdatabase.com

google.github.io/lovefield

www.oracle.com/database/nosql/technologies/nosql

www.sqlite.org

Technical documentation

dev.mobiland.com/Overview.xsp

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

docs.oracle.com/en/database/other-databases/nosql-database/index.html

www.sqlite.org/docs.html

Developer

Mobiland AG

Google

Oracle

Dwayne Richard Hipp

Initial release

2018

2014

2011

2000

Current release

1.1.263, October 2022

2.1.12, February 2017

23.3, December 2023

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

Public Domain

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

Windows

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

tested with Chrome, Firefox, IE, Safari

Linux
Solaris SPARC/x86

server-less

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

dynamic column types

Typing

predefined data types such as float or date

yes

optional

yes

not rigid because of 'dynamic typing' concept.

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

yes

SQL-92 is not fully supported

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

RESTful HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

.Net
C
C#
C++
VB.Net

JavaScript

C
C#
Go
Java
JavaScript (Node.js)
Python

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

Triggers

no, except callback-events from server when changes happened

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

yes

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

yes

In-memory capabilities

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

yes

using MemoryDB

yes

off heap cache

yes

User concepts

Access control

Windows-Profile

Access rights for users and roles

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