DBMS > AgensGraph vs. CouchDB vs. Microsoft Azure Cosmos DB vs. Quasardb

System Properties Comparison AgensGraph vs. CouchDB vs. Microsoft Azure Cosmos DB vs. Quasardb

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Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

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

Globally distributed, horizontally scalable, multi-model database service

Distributed, high-performance timeseries database

Primary database model

Graph DBMS
Relational DBMS

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

Spatial DBMS

using the Geocouch extension

Spatial DBMS

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

Score	0.23
Rank	#315	Overall
	#26	Graph DBMS
	#140	Relational DBMS

Score	8.30
Rank	#47	Overall
	#7	Document stores

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

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Website

bitnine.net/agensgraph

couchdb.apache.org

azure.microsoft.com/services/cosmos-db

quasar.ai

Technical documentation

bitnine.net/documentation

docs.couchdb.org/en/stable

learn.microsoft.com/azure/cosmos-db

doc.quasar.ai/master

Developer

Bitnine Global Inc.

Apache Software Foundation

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

Microsoft

quasardb

Initial release

2016

2005

2014

2009

Current release

2.1, December 2018

3.3.3, December 2023

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

Apache version 2

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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

C++

Server operating systems

Linux
OS X
Windows

Android
BSD
Linux
OS X
Solaris
Windows

hosted

BSD
Linux
OS X
Windows

Data scheme

depending on used data model

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

integer and binary

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 properties auto-indexed by default

yes

with tags

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

Cypher Query Language
JDBC

RESTful HTTP/JSON API

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

HTTP API

Supported programming languages

C
Java
JavaScript
Python

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

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

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

yes

View functions in JavaScript

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

improved architecture with release 2.0

Sharding

Implicit feature of the cloud service

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session 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 single document possible

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

yes

by using LevelDB

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users can be defined per database

Access rights can be defined down to the item level

Cryptographically strong user authentication and audit trail

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