DBMS > AnzoGraph DB vs. Ignite vs. InfinityDB vs. MySQL

System Properties Comparison AnzoGraph DB vs. Ignite vs. InfinityDB 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

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

A Java embedded Key-Value Store which extends the Java Map interface

Widely used open source RDBMS

Primary database model

Graph DBMS
RDF store

Key-value store
Relational DBMS

Key-value store

Relational DBMS

Key/Value like access via memcached API

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	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

cambridgesemantics.com/anzograph

ignite.apache.org

boilerbay.com

www.mysql.com

Technical documentation

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

apacheignite.readme.io/docs

boilerbay.com/infinitydb/manual

dev.mysql.com/doc

Developer

Cambridge Semantics

Apache Software Foundation

Boiler Bay Inc.

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2018

2015

2002

1995

Current release

2.3, January 2021

Apache Ignite 2.6

4.0

8.3.0, January 2024

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache 2.0

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

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Implementation language

C++, Java, .Net

Java

C and C++

Server operating systems

Linux

Linux
OS X
Solaris
Windows

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

Secondary indexes

yes

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

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

ANSI-99 for query and DML statements, subset of DDL

yes

with proprietary extensions

APIs and other access methods

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

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C++
Java
Python

C#
C++
Java
PHP
Python
Ruby
Scala

Java

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

yes (compute grid and cache interceptors can be used instead)

yes

proprietary syntax

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

none

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

yes (replicated cache)

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

manual creation possible, using inversions based on multi-value capability

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

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

User concepts

Access control

Access rights for users and roles

Security Hooks for custom implementations

Users with fine-grained authorization concept

no user groups or roles

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