DBMS > Couchbase vs. Cubrid vs. Sphinx vs. TDengine vs. YDB

System Properties Comparison Couchbase vs. Cubrid vs. Sphinx vs. TDengine vs. YDB

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

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

Time Series DBMS and big data platform

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Document store
Relational DBMS

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

Relational DBMS

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

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	1.04
Rank	#187	Overall
	#87	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	2.68
Rank	#106	Overall
	#9	Time Series DBMS

Score	0.33
Rank	#287	Overall
	#43	Document stores
	#132	Relational DBMS

Website

www.couchbase.com

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

sphinxsearch.com

github.com/taosdata/TDengine
tdengine.com

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

docs.couchbase.com

cubrid.org/manuals

sphinxsearch.com/docs

docs.tdengine.com

ydb.tech/en/docs

Developer

Couchbase, Inc.

CUBRID Corporation, CUBRID Foundation

Sphinx Technologies Inc.

TDEngine, previously Taos Data

Yandex

Initial release

2011

2008

2001

2019

Current release

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

11.0, January 2021

3.5.1, February 2023

3.0, August 2022

License

Commercial or Open Source

Open Source

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

Open Source

Apache Version 2.0

Open Source

GPL version 2, commercial licence available

Open Source

AGPL V3, also commercial editions available

Open Source

Apache 2.0; commercial license 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, C++, Java

C++

Server operating systems

Linux
OS X
Windows

Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
Windows

Linux

Data scheme

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

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

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

yes

SQL-like query language (SphinxQL)

Standard SQL with extensions for time-series applications

SQL-like query language (YQL)

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

ADO.NET
JDBC
ODBC
OLE DB

Proprietary protocol

JDBC
RESTful HTTP API

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

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

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

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

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

Java Stored Procedures

Triggers

yes

via the TAP protocol

yes

yes, via alarm monitoring

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

none

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

Source-replica replication

none

yes

Active-passive shard replication

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

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.

yes

Ephemeral buckets

User concepts

Access control

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

fine grained access rights according to SQL-standard

yes

Access rights defined for Yandex Cloud users

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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Market metrics

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Licensing and pricing models

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