DBMS > Couchbase vs. Galaxybase vs. InfinityDB vs. mSQL vs. Sphinx

System Properties Comparison Couchbase vs. Galaxybase vs. InfinityDB vs. mSQL vs. Sphinx

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

Scalable, ACID-compliant native distributed parallel graph platform

A Java embedded Key-Value Store which extends the Java Map interface

mSQL (Mini SQL) is a simple and lightweight RDBMS

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

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

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

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	0.07
Rank	#377	Overall
	#40	Graph DBMS

Score	0.08
Rank	#365	Overall
	#55	Key-value stores

Score	1.27
Rank	#169	Overall
	#76	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

www.couchbase.com

galaxybase.com

boilerbay.com

hughestech.com.au/products/msql

sphinxsearch.com

Technical documentation

docs.couchbase.com

boilerbay.com/infinitydb/manual

sphinxsearch.com/docs

Developer

Couchbase, Inc.

Chuanglin(Createlink) Technology Co., Ltd 浙江创邻科技有限公司

Boiler Bay Inc.

Hughes Technologies

Sphinx Technologies Inc.

Initial release

2011

2017

2002

1994

2001

Current release

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

Nov 20, November 2021

4.0

4.4, October 2021

3.5.1, February 2023

License

Commercial or Open Source

Open Source

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

commercial

free licenses can be provided

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++, Go and Erlang

C and Java

Java

C++

Server operating systems

Linux
OS X
Windows

Linux

All OS with a Java VM

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

Strong typed schema

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

manual creation possible, using inversions based on multi-value capability

yes

full-text index on all search fields

SQL

Support of SQL

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

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

SQL-like query language (SphinxQL)

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

Browser interface
console (shell)
Graph API (Gremlin)
OpenCypher
Proprietary native API

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

JDBC
ODBC

Proprietary protocol

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

Go
Java
Python

Java

C
C++
Delphi
Java
Perl
PHP
Tcl

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

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

user defined procedures and functions

Triggers

yes

via the TAP protocol

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

Sharding

none

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

none

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

READ-COMMITTED or SERIALIZED

none

Foreign keys

Referential integrity

yes

Relationships in graphs

manual creation possible, using inversions based on multi-value capability

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

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

Ephemeral buckets

yes

User concepts

Access control

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

Role-based access control

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