DBMS > Cubrid vs. Google BigQuery vs. NCache vs. Sphinx vs. STSdb

System Properties Comparison Cubrid vs. Google BigQuery vs. NCache vs. Sphinx vs. STSdb

Editorial information provided by DB-Engines

Name

Description

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

Large scale data warehouse service with append-only tables

Open-Source and Enterprise in-memory Key-Value Store

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Secondary database models

Document store
Search engine

Using distributed Lucene

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

Score	1.31
Rank	#169	Overall
	#78	Relational DBMS

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	0.98
Rank	#196	Overall
	#29	Key-value stores

Score	6.03
Rank	#60	Overall
	#6	Search engines

Score	0.06
Rank	#365	Overall
	#55	Key-value stores

Website

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

cloud.google.com/bigquery

www.alachisoft.com/ncache

sphinxsearch.com

github.com/STSSoft/STSdb4

Technical documentation

cubrid.org/manuals

cloud.google.com/bigquery/docs

www.alachisoft.com/resources/docs

sphinxsearch.com/docs

Developer

CUBRID Corporation, CUBRID Foundation

Google

Alachisoft

Sphinx Technologies Inc.

STS Soft SC

Initial release

2008

2010

2005

2001

2011

Current release

11.0, January 2021

5.3.3, April 2024

3.5.1, February 2023

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Enterprise Edition available

Open Source

GPL version 2, commercial licence available

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++, Java

C#, .NET, .NET Core, Java

C++

Server operating systems

Linux
Windows

hosted

Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

yes

primitive types and user defined types (classes)

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

SQL

Support of SQL

yes

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

SQL-like query language (SphinxQL)

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

RESTful HTTP/JSON API

IDistributedCache
JCache
LINQ
Proprietary native API

Proprietary protocol

.NET Client API

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
Java

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

in JavaScript

support for stored procedures with SQL-Server CLR

Triggers

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

none

yes

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes, with selectable consistency level

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

optimistic locking and pessimistic locking

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.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

Market Leader in .NET Distributed Caching since 2005.
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Licensing and pricing models

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