DBMS > atoti vs. Lovefield vs. SWC-DB

System Properties Comparison atoti vs. Lovefield vs. SWC-DB

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Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Embeddable relational database for web apps written in pure JavaScript

A high performance, scalable Wide Column DBMS

Primary database model

Object oriented DBMS

Relational DBMS

Wide column store

Secondary database models

Time Series DBMS

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

atoti.io

google.github.io/lovefield

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

docs.atoti.io

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

Developer

ActiveViam

Google

Alex Kashirin

Initial release

2014

2020

Current release

2.1.12, February 2017

0.5, April 2021

License

Commercial or Open Source

commercial

free versions available

Open Source

Apache 2.0

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

JavaScript

C++

Server operating systems

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

tested with Chrome, Firefox, IE, Safari

Linux

Data scheme

yes

schema-free

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

Multidimensional Expressions (MDX)

SQL-like query language

via JavaScript builder pattern

SQL-like query language

APIs and other access methods

Proprietary protocol
Thrift

Supported programming languages

JavaScript

C++

Server-side scripts

Stored procedures

Python

Triggers

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

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

User concepts

Access control

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