DBMS > atoti vs. Datomic vs. Fauna vs. SWC-DB

System Properties Comparison atoti vs. Datomic vs. Fauna 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.

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

A high performance, scalable Wide Column DBMS

Primary database model

Object oriented DBMS

Relational DBMS

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Wide column store

Secondary database models

Time Series DBMS

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

Score	0.61
Rank	#243	Overall
	#10	Object oriented DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	1.55
Rank	#151	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

atoti.io

www.datomic.com

fauna.com

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

Technical documentation

docs.atoti.io

docs.datomic.com

docs.fauna.com

Developer

ActiveViam

Cognitect

Fauna, Inc.

Alex Kashirin

Initial release

2012

2014

2020

Current release

1.0.7075, December 2023

0.5, April 2021

License

Commercial or Open Source

commercial

free versions available

commercial

limited edition free

commercial

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Java, Clojure

Scala

C++

Server operating systems

All OS with a Java VM

hosted

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

APIs and other access methods

RESTful HTTP API

Proprietary protocol
Thrift

Supported programming languages

Clojure
Java

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

C++

Server-side scripts

Stored procedures

Python

yes

Transaction Functions

user defined functions

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

none

But extensive use of caching in the application peers

horizontal partitioning

consistent hashing

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Multi-source replication

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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

User concepts

Access control

Identity management, authentication, and access control

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