DBMS > AnzoGraph DB vs. Firebase Realtime Database vs. MySQL vs. Solr

System Properties Comparison AnzoGraph DB vs. Firebase Realtime Database vs. MySQL vs. Solr

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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

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

Widely used open source RDBMS

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Graph DBMS
RDF store

Document store

Relational DBMS

Key/Value like access via memcached API

Search engine

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

cambridgesemantics.com/anzograph

firebase.google.com/products/realtime-database

www.mysql.com

solr.apache.org

Technical documentation

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

firebase.google.com/docs/database

dev.mysql.com/doc

solr.apache.org/resources.html

Developer

Cambridge Semantics

Google

acquired by Google 2014

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Initial release

2018

2012

1995

2006

Current release

2.3, January 2021

8.4.0, April 2024

9.6.1, May 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

Apache Version 2

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

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

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

yes

with proprietary extensions

Solr Parallel SQL Interface

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
Proprietary native API

Java API
RESTful HTTP/JSON API

Supported programming languages

C++
Java
Python

Java
JavaScript
Objective-C

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

limited functionality with using 'rules'

yes

proprietary syntax

Java plugins

Triggers

Callbacks are triggered when data changes

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

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

Eventual 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

optimistic locking

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

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

Users with fine-grained authorization concept

no user groups or roles

yes

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