DBMS > AllegroGraph vs. Citus vs. CouchDB vs. Datastax Enterprise vs. Microsoft Azure Cosmos DB

System Properties Comparison AllegroGraph vs. Citus vs. CouchDB vs. Datastax Enterprise vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Description

High performance, persistent RDF store with additional support for Graph DBMS

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

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

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Document store

with version 6.5
Graph DBMS
RDF store
Vector DBMS

Relational DBMS

Document store

Wide column store

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

Document store

Spatial DBMS

using the Geocouch extension

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

Spatial DBMS

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

Score	1.06
Rank	#187	Overall
	#30	Document stores
	#17	Graph DBMS
	#7	RDF stores
	#7	Vector DBMS

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	5.80
Rank	#60	Overall
	#4	Wide column stores

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

Website

allegrograph.com

www.citusdata.com

couchdb.apache.org

www.datastax.com/products/datastax-enterprise

azure.microsoft.com/services/cosmos-db

Technical documentation

franz.com/agraph/support/documentation/current/agraph-introduction.html

docs.citusdata.com

docs.couchdb.org/en/stable

docs.datastax.com

learn.microsoft.com/azure/cosmos-db

Developer

Franz Inc.

Apache Software Foundation

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

DataStax

Microsoft

Initial release

2004

2010

2005

2011

2014

Current release

8.0, December 2023

8.1, December 2018

3.3.3, December 2023

6.8, April 2020

License

Commercial or Open Source

commercial

Limited community edition free

Open Source

AGPL, commercial license also available

Open Source

Apache version 2

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Datastax Astra DB: Astra DB simplifies cloud-native Cassandra application development for your apps, microservices and functions. Deploy in minutes on AWS, Google Cloud, Azure, and have it managed for you by the experts, with serverless, pay-as-you-go pricing.

Implementation language

Erlang

Java

Server operating systems

Linux
OS X
Windows

Linux

Android
BSD
Linux
OS X
Solaris
Windows

Linux
OS X

hosted

Data scheme

yes

RDF schemas

yes

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.

bulk load of XML files possible

yes

specific XML type available, but no XML query functionality

Secondary indexes

yes

via views

yes

All properties auto-indexed by default

SQL

Support of SQL

SPARQL is used as query language

yes

standard, with numerous extensions

SQL-like DML and DDL statements (CQL); Spark SQL

SQL-like query language

APIs and other access methods

RESTful HTTP API
SPARQL

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

RESTful HTTP/JSON API

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

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

Supported programming languages

C#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

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

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

Server-side scripts

Stored procedures

yes

JavaScript or Common Lisp

user defined functions

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

View functions in JavaScript

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

with Federation

Sharding

improved architecture with release 2.0

Sharding

no "single point of failure"

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

other methods possible by using 3rd party extensions

Multi-source replication
Source-replica replication

configurable replication factor, datacenter aware, advanced replication for edge computing

yes

Implicit feature of the cloud service

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

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session 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

Atomicity and isolation are supported for single operations

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

User concepts

Access control

Users with fine-grained authorization concept, user roles and pluggable authentication

fine grained access rights according to SQL-standard

Access rights for users can be defined per database

Access rights for users can be defined per object

Access rights can be defined down to the item level

More information provided by the system vendor

Specific characteristics

Knowledge Graph Platform Leader FedShard - Designed for Entity-Event Knowledge Graph...
» more

DataStax Enterprise is scale-out data infrastructure for enterprises that need to...
» more

Competitive advantages

AllegroGraph is uniquely suited to support adhoc queries through SPARQL, Prolog and...
» more

Supporting the following application requirements: Zero downtime - Built on Apache...
» more

Typical application scenarios

Applications that must be massively and linearly scalable with 100% uptime and able...
» more

Key customers

Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
» more

Market metrics

Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
» more

Licensing and pricing models

Annual subscription
» more

News

Can Neuro-Symbolic AI Solve AI’s Weaknesses?
17 April 2024

100 Companies That Matter in KM – Franz Inc.
3 April 2024

Exploring AllegroGraph v8 – Unleashing the Power of Neuro-Symbolic AI (Recorded Webinar)
9 February 2024

What is Neuro-Symbolic AI?
23 January 2024

Allegro CL v11 – Now Available! – The Neuro-Symbolic AI Programming Platform
8 January 2024

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.