DBMS > AnzoGraph DB vs. Firebase Realtime Database vs. Interbase vs. MySQL vs. SiteWhere

System Properties Comparison AnzoGraph DB vs. Firebase Realtime Database vs. Interbase vs. MySQL vs. SiteWhere

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

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Light-weight proven RDBMS

originally from Borland

Widely used open source RDBMS

M2M integration platform for persisting/querying time series data

Primary database model

Graph DBMS
RDF store

Document store

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Secondary database models

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	14.29
Rank	#39	Overall
	#6	Document stores

Score	4.61
Rank	#72	Overall
	#39	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

cambridgesemantics.com/anzograph

firebase.google.com/products/realtime-database

www.embarcadero.com/products/interbase

www.mysql.com

github.com/sitewhere/sitewhere

Technical documentation

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

firebase.google.com/docs/database

docs.embarcadero.com/products/interbase

dev.mysql.com/doc

sitewhere1.sitewhere.io/index.html

Developer

Cambridge Semantics

Google

acquired by Google 2014

Embarcadero

Oracle

since 2010, originally MySQL AB, then Sun

SiteWhere

Initial release

2018

2012

1984

1995

2010

Current release

2.3, January 2021

InterBase 2020, December 2019

8.4.0, April 2024

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Common Public Attribution License Version 1.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C and C++

Java

Server operating systems

Linux

hosted

Android
iOS
Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

yes

predefined scheme

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.

export as XML data possible

yes

Secondary indexes

yes

SQL

Support of SQL

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

yes

with proprietary extensions

APIs and other access methods

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

Android
iOS
JavaScript API
RESTful HTTP API

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP REST

Supported programming languages

C++
Java
Python

Java
JavaScript
Objective-C

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

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

limited functionality with using 'rules'

yes

Interbase procedure and trigger language

yes

proprietary syntax

Triggers

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Interbase Change Views

Multi-source replication
Source-replica replication

selectable replication factor

based on HBase

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

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

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

yes

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

yes

table locks or row locks depending on storage engine

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

Access rights for users and roles

yes, based on authentication and database rules

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

Users with fine-grained authorization concept

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