DBMS > EJDB vs. IBM Cloudant vs. Microsoft Azure Cosmos DB vs. PostgreSQL

System Properties Comparison EJDB vs. IBM Cloudant vs. Microsoft Azure Cosmos DB vs. PostgreSQL

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Editorial information provided by DB-Engines

Name

Description

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Database as a Service offering based on Apache CouchDB

Globally distributed, horizontally scalable, multi-model database service

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Primary database model

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Secondary database models

Spatial DBMS

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	2.68
Rank	#106	Overall
	#20	Document stores

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

Score	645.54
Rank	#4	Overall
	#4	Relational DBMS

Website

github.com/Softmotions/ejdb

www.ibm.com/products/cloudant

azure.microsoft.com/services/cosmos-db

www.postgresql.org

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

cloud.ibm.com/docs/Cloudant

learn.microsoft.com/azure/cosmos-db

www.postgresql.org/docs

Developer

Softmotions

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Microsoft

PostgreSQL Global Development Group

www.postgresql.org/developer

Initial release

2012

2010

2014

1989

1989: Postgres, 1996: PostgreSQL

Current release

16.3, May 2024

License

Commercial or Open Source

Open Source

GPLv2

commercial

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Erlang

Server operating systems

server-less

hosted

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

JSON types

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.

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

yes

standard with numerous extensions

APIs and other access methods

in-process shared library

RESTful HTTP/JSON API

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

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

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C#
Java
JavaScript
Objective-C
PHP
Ruby

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

JavaScript

user defined functions

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

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

partitioning by range, list and (since PostgreSQL 11) by hash

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

Source-replica replication

other methods possible by using 3rd party extensions

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

atomic operations within a document possible

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

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.

User concepts

Access control

Access rights for users can be defined per database

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

More information provided by the system vendor

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