DBMS > AnzoGraph DB vs. Citus vs. InfluxDB vs. MySQL

System Properties Comparison AnzoGraph DB vs. Citus vs. InfluxDB vs. MySQL

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Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

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

DBMS for storing time series, events and metrics

Widely used open source RDBMS

Primary database model

Key/Value like access via memcached API

Secondary database models

Document store

Spatial DBMS

with GEO package

Document store
Spatial DBMS

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

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

cambridgesemantics.com/anzograph

www.citusdata.com

www.influxdata.com/products/influxdb-overview

www.mysql.com

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

docs.citusdata.com

docs.influxdata.com/influxdb

dev.mysql.com/doc

Developer

Cambridge Semantics

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2018

2010

2013

1995

Current release

2.3, January 2021

8.1, December 2018

2.7.6, April 2024

8.3.0, January 2024

License

Commercial or Open Source

commercial

free trial version available

Open Source

AGPL, commercial license also available

Open Source

MIT-License; commercial enterprise version available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C and C++

Server operating systems

Linux

Linux
OS X

through Homebrew

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data and Strings

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

Secondary indexes

yes

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

yes

standard, with numerous extensions

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

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

HTTP API
JSON over UDP

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C++
Java
Python

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

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

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

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

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

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

in enterprise version only

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Source-replica replication

other methods possible by using 3rd party extensions

selectable replication factor

in enterprise version only

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Depending on used storage engine

yes

User concepts

Access control

Access rights for users and roles

fine grained access rights according to SQL-standard

simple rights management via user accounts

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

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Market metrics

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Licensing and pricing models

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