DBMS > AnzoGraph DB vs. Geode vs. GridGain vs. Microsoft SQL Server

System Properties Comparison AnzoGraph DB vs. Geode vs. GridGain vs. Microsoft SQL Server

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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

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

GridGain is an in-memory computing platform, built on Apache Ignite

Microsofts flagship relational DBMS

Primary database model

Graph DBMS
RDF store

Key-value store

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store
Graph 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	1.86
Rank	#134	Overall
	#24	Key-value stores

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Website

cambridgesemantics.com/anzograph

geode.apache.org

www.gridgain.com

www.microsoft.com/en-us/sql-server

Technical documentation

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

geode.apache.org/docs

www.gridgain.com/docs/index.html

learn.microsoft.com/en-US/sql/sql-server

Developer

Cambridge Semantics

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

GridGain Systems, Inc.

Microsoft

Initial release

2018

2002

2007

1989

Current release

2.3, January 2021

1.1, February 2017

GridGain 8.5.1

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

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Implementation language

Java

Java, C++, .Net

C++

Server operating systems

Linux

All OS with a Java VM

the JDK (8 or later) is also required

Linux
OS X
Solaris
Windows

Linux
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

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

SQL-like query language (OQL)

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

yes

APIs and other access methods

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

Java Client API
Memcached protocol
RESTful HTTP API

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

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

C++
Java
Python

.Net
All JVM based languages
C++
Groovy
Java
Scala

C#
C++
Java
PHP
Python
Ruby
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

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

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Cache Event Listeners

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Multi-source replication

yes (replicated cache)

yes, but depending on the SQL-Server Edition

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

ACID

yes, on a single node

ACID

Concurrency

Support for concurrent manipulation of data

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

Access rights per client and object definable

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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