DBMS > CouchDB vs. Hazelcast vs. Microsoft Azure Cosmos DB vs. RavenDB

System Properties Comparison CouchDB vs. Hazelcast vs. Microsoft Azure Cosmos DB vs. RavenDB

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Editorial information provided by DB-Engines

Name

Description

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

A widely adopted in-memory data grid

Globally distributed, horizontally scalable, multi-model database service

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Document store

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Document store

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store

JSON support with IMDG 3.12

Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	10.26
Rank	#45	Overall
	#7	Document stores

Score	6.87
Rank	#55	Overall
	#6	Key-value stores

Score	29.85
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	3.01
Rank	#101	Overall
	#17	Document stores

Website

couchdb.apache.org

hazelcast.com

azure.microsoft.com/services/cosmos-db

ravendb.net

Technical documentation

docs.couchdb.org/en/stable

hazelcast.org/imdg/docs

learn.microsoft.com/azure/cosmos-db

ravendb.net/docs

Developer

Apache Software Foundation

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

Hazelcast

Microsoft

Hibernating Rhinos

Initial release

2005

2008

2014

2010

Current release

3.3.3, December 2023

5.3.6, November 2023

5.4, July 2022

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

AGPL version 3, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Erlang

Java

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

hosted

Linux
macOS
Raspberry Pi
Windows

Data scheme

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

the object must implement a serialization strategy

Secondary indexes

yes

via views

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (RQL)

APIs and other access methods

RESTful HTTP/JSON API

JCache
JPA
Memcached protocol
RESTful HTTP API

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

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Supported programming languages

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

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

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

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

View functions in JavaScript

yes

Event Listeners, Executor Services

JavaScript

yes

Triggers

yes

Events

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Replicated Map

yes

Implicit feature of the cloud service

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

Transaction concepts

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

atomic operations within a single document possible

one or two-phase-commit; repeatable reads; read commited

Multi-item ACID transactions with snapshot isolation within a partition

ACID, Cluster-wide transaction available

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

User concepts

Access control

Access rights for users can be defined per database

Role-based access control

Access rights can be defined down to the item level

Authorization levels configured per client per database

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