DBMS > Couchbase vs. H2 vs. HyperSQL vs. ReductStore vs. UniData,UniVerse

System Properties Comparison Couchbase vs. H2 vs. HyperSQL vs. ReductStore vs. UniData,UniVerse

Editorial information provided by DB-Engines

Name

Description

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

MultiValue database and application server with SQL mapping layer and meta database capabilities

Primary database model

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Spatial DBMS

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

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	3.16
Rank	#97	Overall
	#2	Multivalue DBMS

Website

www.couchbase.com

www.h2database.com

hsqldb.org

github.com/reductstore
www.reduct.store

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-unidata

Technical documentation

docs.couchbase.com

www.h2database.com/html/main.html

hsqldb.org/web/hsqlDocsFrame.html

www.reduct.store/docs

docs.rocketsoftware.com/bundle?cluster=true&labelkey=unidata&labelkey=prod_unidata

Developer

Couchbase, Inc.

Thomas Mueller

ReductStore LLC

Rocket Software

Initial release

2011

2005

2001

2023

1985

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

2.2.220, July 2023

2.7.2, June 2023

1.9, March 2024

License

Commercial or Open Source

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

based on BSD license

Open Source

Business Source License 1.1

commercial

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

C, C++, Go and Erlang

Java

C++, Rust

Server operating systems

Linux
OS X
Windows

All OS with a Java VM

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

Docker
Linux
macOS
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

optional

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++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

yes

APIs and other access methods

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

JDBC
ODBC

HTTP API

JDBC via HTTP
JDBC
ODBC

HTTP API

Java API

JPA
JDBC
ODBC
OLE DB
Proprietary protocol
RESTful HTTP API
SOAP-based API

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

Java

All languages supporting JDBC/ODBC
Java

C++
JavaScript (Node.js)
Python
Rust

.Net
Basic

U2 Basic
C
Java

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

Java Stored Procedures and User-Defined Functions

Java, SQL

yes

Triggers

yes

via the TAP protocol

yes

U2 Basic

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

With clustering: 2 database servers on different computers operate on identical copies of a database

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

Ephemeral buckets

yes

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

fine grained access rights according to SQL-standard

Access rights according to SQL-standard and operating system based

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