DBMS > Citus vs. Couchbase vs. IBM Cloudant vs. Realm vs. XTDB

System Properties Comparison Citus vs. Couchbase vs. IBM Cloudant vs. Realm vs. XTDB

Editorial information provided by DB-Engines

Name

Description

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

Database as a Service offering based on Apache CouchDB

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Document store

Secondary database models

Document store

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	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	7.60
Rank	#52	Overall
	#9	Document stores

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

www.citusdata.com

www.couchbase.com

www.ibm.com/products/cloudant

realm.io

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.citusdata.com

docs.couchbase.com

cloud.ibm.com/docs/Cloudant

realm.io/docs

www.xtdb.com/docs

Developer

Couchbase, Inc.

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Realm, acquired by MongoDB in May 2019

Juxt Ltd.

Initial release

2010

2011

2010

2014

2019

Current release

8.1, December 2018

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

1.19, September 2021

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

Open Source

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

commercial

Open Source

MIT License

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++, Go and Erlang

Erlang

Clojure

Server operating systems

Linux

Linux
OS X
Windows

hosted

Android
Backend: server-less
iOS
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

specific XML type available, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

standard, with numerous extensions

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

limited SQL, making use of Apache Calcite

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

RESTful HTTP/JSON API

HTTP REST
JDBC

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C#
Java
JavaScript
Objective-C
PHP
Ruby

.Net
Java

with Android only
Objective-C
React Native
Swift

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

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

View functions (Map-Reduce) in JavaScript

runs within the applications so server-side scripts are unnecessary

Triggers

yes

via the TAP protocol

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic Sharding

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

Multi-source replication

including cross data center replication
Source-replica replication

Multi-source replication
Source-replica replication

none

yes, each node contains all data

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

selectable on a per-operation basis

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

atomic operations within a document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

Ephemeral buckets

yes

In-Memory realm

User concepts

Access control

fine grained access rights according to SQL-standard

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

Access rights for users can be defined per database

yes

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