DBMS > AnzoGraph DB vs. Firebase Realtime Database vs. Kinetica vs. MySQL vs. SQLite

System Properties Comparison AnzoGraph DB vs. Firebase Realtime Database vs. Kinetica vs. MySQL vs. SQLite

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.

Fully vectorized database across both GPUs and CPUs

Widely used open source RDBMS

Widely used embeddable, in-process RDBMS

Primary database model

Graph DBMS
RDF store

Document store

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

cambridgesemantics.com/anzograph

firebase.google.com/products/realtime-database

www.kinetica.com

www.mysql.com

www.sqlite.org

Technical documentation

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

firebase.google.com/docs/database

docs.kinetica.com

dev.mysql.com/doc

www.sqlite.org/docs.html

Developer

Cambridge Semantics

Google

acquired by Google 2014

Kinetica

Oracle

since 2010, originally MySQL AB, then Sun

Dwayne Richard Hipp

Initial release

2018

2012

1995

2000

Current release

2.3, January 2021

7.1, August 2021

8.4.0, April 2024

3.45.3 (15 April 2024), 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

Public Domain

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

C and C++

Server operating systems

Linux

hosted

Linux

FreeBSD
Linux
OS X
Solaris
Windows

server-less

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

Secondary indexes

yes

SQL

Support of SQL

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

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

SQL-92 is not fully supported

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

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C++
Java
Python

Java
JavaScript
Objective-C

C++
Java
JavaScript (Node.js)
Python

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

user defined functions and aggregates

limited functionality with using 'rules'

user defined functions

yes

proprietary syntax

Triggers

Callbacks are triggered when data changes

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Source-replica replication

Multi-source replication
Source-replica replication

none

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 or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

yes

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

via file-system locks

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

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights for users and roles

yes, based on authentication and database rules

Access rights for users and roles on table level

Users with fine-grained authorization concept

no user groups or roles

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