DBMS > AgensGraph vs. Citus vs. Quasardb

System Properties Comparison AgensGraph vs. Citus 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

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

Distributed, high-performance timeseries database

Primary database model

Graph DBMS
Relational DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Document store

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Trend Chart

Score	0.16
Rank	#333	Overall
	#30	Graph DBMS
	#146	Relational DBMS

Score	2.15
Rank	#123	Overall
	#60	Relational DBMS

Score	0.19
Rank	#327	Overall
	#30	Time Series DBMS

Website

bitnine.net/agensgraph

www.citusdata.com

quasar.ai

Technical documentation

bitnine.net/documentation

docs.citusdata.com

doc.quasar.ai/master

Developer

Bitnine Global Inc.

quasardb

Initial release

2016

2010

2009

Current release

2.1, December 2018

8.1, December 2018

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

AGPL, commercial license also available

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

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Server operating systems

Linux
OS X
Windows

Linux

BSD
Linux
OS X
Windows

Data scheme

depending on used data model

yes

schema-free

Typing

predefined data types such as float or date

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.

yes

specific XML type available, but no XML query functionality

Secondary indexes

yes

with tags

SQL

Support of SQL

yes

standard, with numerous extensions

SQL-like query language

APIs and other access methods

Cypher Query Language
JDBC

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

HTTP API

Supported programming languages

C
Java
JavaScript
Python

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

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

Server-side scripts

Stored procedures

yes

user defined functions

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

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

Cryptographically strong user authentication and audit trail

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More resources

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