DBMS > atoti vs. Datomic vs. HyperSQL vs. YottaDB

System Properties Comparison atoti vs. Datomic vs. HyperSQL vs. YottaDB

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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

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

A fast and solid embedded Key-value store

Primary database model

Object oriented DBMS

Relational DBMS

Key-value store

Secondary database models

Relational DBMS

using the Octo plugin

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	3.23
Rank	#93	Overall
	#48	Relational DBMS

Score	0.28
Rank	#306	Overall
	#44	Key-value stores

Website

Technical documentation

docs.atoti.io

docs.datomic.com

hsqldb.org/web/hsqlDocsFrame.html

yottadb.com/resources/documentation

Developer

ActiveViam

Cognitect

YottaDB, LLC

Initial release

2012

2001

Current release

1.0.7075, December 2023

2.7.2, June 2023

License

Commercial or Open Source

commercial

free versions available

commercial

limited edition free

Open Source

based on BSD license

Open Source

AGPL 3.0

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

Java, Clojure

Java

Server operating systems

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Docker
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)

yes

by using the Octo plugin

APIs and other access methods

RESTful HTTP API

HTTP API

JDBC via HTTP
JDBC
ODBC

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

Clojure
Java

All languages supporting JDBC/ODBC
Java

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

Python

yes

Transaction Functions

Java, SQL

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

none

But extensive use of caching in the application peers

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

none

yes

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

optimistic locking

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Users and groups based on OS-security mechanisms

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