DBMS > Cachelot.io vs. Cubrid vs. Sphinx vs. Trafodion

System Properties Comparison Cachelot.io vs. Cubrid vs. Sphinx vs. Trafodion

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Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

In-memory caching system

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

Open source search engine for searching in data from different sources, e.g. relational databases

Transactional SQL-on-Hadoop DBMS

Primary database model

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

cachelot.io

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

sphinxsearch.com

trafodion.apache.org

Technical documentation

cubrid.org/manuals

sphinxsearch.com/docs

trafodion.apache.org/documentation.html

Developer

CUBRID Corporation, CUBRID Foundation

Sphinx Technologies Inc.

Apache Software Foundation, originally developed by HP

Initial release

2015

2008

2001

2014

Current release

11.0, January 2021

3.5.1, February 2023

2.3.0, February 2019

License

Commercial or Open Source

Open Source

Simplified BSD License

Open Source

Apache Version 2.0

Open Source

GPL version 2, commercial licence available

Open Source

Apache 2.0

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

C++

C, C++, Java

C++

C++, Java

Server operating systems

FreeBSD
Linux
OS X

Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux

Data scheme

schema-free

yes

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

full-text index on all search fields

yes

SQL

Support of SQL

yes

SQL-like query language (SphinxQL)

yes

APIs and other access methods

Memcached protocol

ADO.NET
JDBC
ODBC
OLE DB

Proprietary protocol

ADO.NET
JDBC
ODBC

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

none

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

The original contents of fields are not stored in the Sphinx index.

yes

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

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More resources

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