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

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

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Editorial information provided by DB-Engines

Name

Description

In-memory caching system

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

GridGain is an in-memory computing platform, built on Apache Ignite

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

Primary database model

Key-value store

Relational DBMS

Key-value store
Relational DBMS

Search engine

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

Score	0.00
Rank	#396	Overall
	#64	Key-value stores

Score	1.31
Rank	#169	Overall
	#78	Relational DBMS

Score	1.53
Rank	#155	Overall
	#26	Key-value stores
	#73	Relational DBMS

Score	6.03
Rank	#60	Overall
	#6	Search engines

Website

cachelot.io

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

www.gridgain.com

sphinxsearch.com

Technical documentation

cubrid.org/manuals

www.gridgain.com/docs/index.html

sphinxsearch.com/docs

Developer

CUBRID Corporation, CUBRID Foundation

GridGain Systems, Inc.

Sphinx Technologies Inc.

Initial release

2015

2008

2007

2001

Current release

11.0, January 2021

GridGain 8.5.1

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Simplified BSD License

Open Source

Apache Version 2.0

commercial

Open Source

GPL version 2, commercial licence available

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

Java, C++, .Net

C++

Server operating systems

FreeBSD
Linux
OS X

Linux
Windows

Linux
OS X
Solaris
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

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.

yes

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

yes

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language (SphinxQL)

APIs and other access methods

Memcached protocol

ADO.NET
JDBC
ODBC
OLE DB

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Proprietary protocol

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#
C++
Java
PHP
Python
Ruby
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

Java Stored Procedures

yes (compute grid and cache interceptors can be used instead)

Triggers

yes

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes (replicated cache)

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

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.

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

Security Hooks for custom implementations

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DB-Engines blog posts

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