DBMS > Datomic vs. GBase vs. HyperSQL vs. Quasardb

System Properties Comparison Datomic vs. GBase vs. HyperSQL vs. Quasardb

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Editorial information provided by DB-Engines

Name

Description

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

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Distributed, high-performance timeseries database

Primary database model

Relational DBMS

Time Series DBMS

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

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	3.23
Rank	#93	Overall
	#48	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Website

Technical documentation

docs.datomic.com

hsqldb.org/web/hsqlDocsFrame.html

doc.quasar.ai/master

Developer

Cognitect

General Data Technology Co., Ltd.

quasardb

Initial release

2012

2004

2001

2009

Current release

1.0.6735, June 2023

GBase 8a, GBase 8s, GBase 8c

2.7.2, June 2023

3.14.1, January 2024

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

based on BSD license

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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, Clojure

C, Java, Python

Java

C++

Server operating systems

All OS with a Java VM

Linux

All OS with a Java VM

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

BSD
Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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.

yes

Secondary indexes

yes

with tags

SQL

Support of SQL

Standard with numerous extensions

yes

SQL-like query language

APIs and other access methods

RESTful HTTP API

ADO.NET
C API
JDBC
ODBC

HTTP API

JDBC via HTTP
JDBC
ODBC

HTTP API

Supported programming languages

Clojure
Java

All languages supporting JDBC/ODBC
Java

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions

Java, SQL

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

horizontal partitioning (by range, list and hash) and vertical partitioning

none

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes

none

Source-replica replication with selectable replication factor

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

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

Durability

Support for making data persistent

yes

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

yes

by using LevelDB

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

Transient mode

User concepts

Access control

yes

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

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