DBMS > Cubrid vs. Oracle Berkeley DB vs. Riak TS vs. Solr

System Properties Comparison Cubrid vs. Oracle Berkeley DB vs. Riak TS vs. Solr

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Editorial information provided by DB-Engines

Name

Description

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

Widely used in-process key-value store

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Time Series DBMS

Search engine

Secondary database models

Spatial DBMS

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

cubrid.com (korean)
cubrid.org (english)

www.oracle.com/database/technologies/related/berkeleydb.html

solr.apache.org

Technical documentation

cubrid.org/manuals

docs.oracle.com/cd/E17076_05/html/index.html

www.tiot.jp/riak-docs/riak/ts/latest

solr.apache.org/resources.html

Developer

CUBRID Corporation, CUBRID Foundation

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Open Source, formerly Basho Technologies

Apache Software Foundation

Initial release

2008

1994

2015

2006

Current release

11.0, January 2021

18.1.40, May 2020

3.0.0, September 2022

9.6.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

commercial license available

Open Source

Apache Version 2

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++, Java

C, Java, C++ (depending on the Berkeley DB edition)

Erlang

Java

Server operating systems

Linux
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
OS X

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

only with the Berkeley DB XML edition

yes

Secondary indexes

yes

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

yes, limited

Solr Parallel SQL Interface

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

HTTP API
Native Erlang Interface

Java API
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

Erlang

Java plugins

Triggers

yes

only for the SQL API

yes

pre-commit hooks and post-commit hooks

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

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

User concepts

Access control

fine grained access rights according to SQL-standard

yes

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