DBMS > AgensGraph vs. BigchainDB vs. Citus vs. MySQL vs. Postgres-XL

System Properties Comparison AgensGraph vs. BigchainDB vs. Citus vs. MySQL vs. Postgres-XL

Editorial information provided by DB-Engines

Name

Description

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

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

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

Widely used open source RDBMS

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Primary database model

Graph DBMS
Relational DBMS

Document store

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store

Document store
Spatial DBMS

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

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

Score	0.82
Rank	#212	Overall
	#36	Document stores

Score	2.15
Rank	#123	Overall
	#60	Relational DBMS

Score	1087.72
Rank	#2	Overall
	#2	Relational DBMS

Score	0.52
Rank	#256	Overall
	#117	Relational DBMS

Website

bitnine.net/agensgraph

Technical documentation

bitnine.net/documentation

bigchaindb.readthedocs.io/en/latest

docs.citusdata.com

dev.mysql.com/doc

www.postgres-xl.org/documentation

Developer

Bitnine Global Inc.

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2016

2010

1995

2014

since 2012, originally named StormDB

Current release

2.1, December 2018

8.1, December 2018

8.3.0, January 2024

10 R1, October 2018

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

AGPL v3

Open Source

AGPL, commercial license also available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Mozilla public license

Cloud-based only

Only available as a cloud service

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Implementation language

Python

C and C++

Server operating systems

Linux
OS X
Windows

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS

Data scheme

depending on used data model

schema-free

yes

Typing

predefined data types such as float or date

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

yes

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

standard, with numerous extensions

yes

with proprietary extensions

yes

distributed, parallel query execution

APIs and other access methods

Cypher Query Language
JDBC

CLI Client
RESTful HTTP API

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

ADO.NET
JDBC
ODBC
Proprietary native API

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

Supported programming languages

C
Java
JavaScript
Python

Go
Haskell
Java
JavaScript
Python
Ruby

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

Server-side scripts

Stored procedures

yes

user defined functions

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

yes

proprietary syntax

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

Source-replica replication

other methods possible by using 3rd party extensions

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

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

fine grained access rights according to SQL-standard

yes

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

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