DBMS > CouchDB vs. Hypertable vs. MarkLogic vs. Microsoft Azure Cosmos DB

System Properties Comparison CouchDB vs. Hypertable vs. MarkLogic vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

An open source BigTable implementation based on distributed file systems such as Hadoop

Operational and transactional Enterprise NoSQL database

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Document store

Wide column store

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

using the Geocouch extension

Spatial DBMS

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

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	5.18
Rank	#63	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#7	Search engines

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

couchdb.apache.org

www.marklogic.com

azure.microsoft.com/services/cosmos-db

Technical documentation

docs.couchdb.org/en/stable

docs.marklogic.com

learn.microsoft.com/azure/cosmos-db

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Hypertable Inc.

MarkLogic Corp.

Microsoft

Initial release

2005

2009

2001

2014

Current release

3.3.3, December 2023

0.9.8.11, March 2016

11.0, December 2022

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

GNU version 3. Commercial license available

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Erlang

C++

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

an inofficial Windows port is available

Linux
OS X
Windows

hosted

Data scheme

schema-free

Schema can be enforced

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

via views

restricted

only exact value or prefix value scans

yes

All properties auto-indexed by default

SQL

Support of SQL

yes

SQL92

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

C++ API
Thrift

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

C++
Java
Perl
PHP
Python
Ruby

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

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

View functions in JavaScript

yes

via XQuery or JavaScript

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor on file system level

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

Transaction concepts

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

atomic operations within a single document possible

ACID

can act as a resource manager in an XA/JTA transaction

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes, with Range Indexes

User concepts

Access control

Access rights for users can be defined per database

Role-based access control at the document and subdocument levels

Access rights can be defined down to the item level

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