DBMS > CouchDB vs. Elasticsearch vs. Microsoft Azure Cosmos DB vs. YottaDB

System Properties Comparison CouchDB vs. Elasticsearch vs. Microsoft Azure Cosmos DB vs. YottaDB

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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 distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Globally distributed, horizontally scalable, multi-model database service

A fast and solid embedded Key-value store

Primary database model

Document store

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store
Spatial DBMS
Vector DBMS

Spatial DBMS

Relational DBMS

using the Octo plugin

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

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.20
Rank	#317	Overall
	#47	Key-value stores

Website

couchdb.apache.org

www.elastic.co/elasticsearch

azure.microsoft.com/services/cosmos-db

yottadb.com

Technical documentation

docs.couchdb.org/en/stable

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

learn.microsoft.com/azure/cosmos-db

yottadb.com/resources/documentation

Developer

Apache Software Foundation

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

Elastic

Microsoft

YottaDB, LLC

Initial release

2005

2010

2014

2001

Current release

3.3.3, December 2023

8.6, January 2023

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Elastic License

commercial

Open Source

AGPL 3.0

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

Docker
Linux

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

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.

Secondary indexes

yes

via views

yes

All search fields are automatically indexed

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like query language

by using the Octo plugin

APIs and other access methods

RESTful HTTP/JSON API

Java API
RESTful HTTP/JSON API

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

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

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

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

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

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

View functions in JavaScript

yes

JavaScript

Triggers

yes

by using the 'percolation' feature

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

yes

Implicit feature of the cloud service

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

ES-Hadoop Connector

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

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

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking

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.

Memcached and Redis integration

yes

User concepts

Access control

Access rights for users can be defined per database

Access rights can be defined down to the item level

Users and groups based on OS-security mechanisms

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