DBMS > GeoSpock vs. GridGain vs. Microsoft SQL Server vs. Neo4j vs. OrientDB

System Properties Comparison GeoSpock vs. GridGain vs. Microsoft SQL Server vs. Neo4j vs. OrientDB

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Spatial and temporal data processing engine for extreme data scale

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

Microsofts flagship relational DBMS

Scalable, ACID-compliant graph database designed with a high-performance distributed cluster architecture, available in self-hosted and cloud offerings

Multi-model DBMS (Document, Graph, Key/Value)

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Graph DBMS

Document store
Graph DBMS
Key-value store

Secondary database models

Time Series DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	44.46
Rank	#23	Overall
	#1	Graph DBMS

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Website

geospock.com

www.gridgain.com

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

neo4j.com

orientdb.org

Technical documentation

www.gridgain.com/docs/index.html

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

neo4j.com/docs

www.orientdb.com/docs/last/index.html

Developer

GeoSpock

GridGain Systems, Inc.

Microsoft

Neo4j, Inc.

OrientDB LTD; CallidusCloud; SAP

Initial release

2007

1989

2007

2010

Current release

2.0, September 2019

GridGain 8.5.1

SQL Server 2022, November 2022

5.19, April 2024

3.2.29, March 2024

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

GPL version3, commercial licenses available

Open Source

Apache version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

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Implementation language

Java, Javascript

Java, C++, .Net

C++

Java, Scala

Java

Server operating systems

hosted

Linux
OS X
Solaris
Windows

Linux
Windows

Linux

Can also be used server-less as embedded Java database.
OS X
Solaris
Windows

All OS with a Java JDK (>= JDK 6)

Data scheme

yes

schema-free and schema-optional

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

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

temporal, categorical

yes

pluggable indexing subsystem, by default Apache Lucene

yes

SQL

Support of SQL

ANSI SQL for query only (using Presto)

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

yes

SQL-like query language, no joins

APIs and other access methods

JDBC

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

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

Bolt protocol
Cypher query language
Java API
Neo4j-OGM

Object Graph Mapper
RESTful HTTP API
Spring Data Neo4j
TinkerPop 3

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

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

.Net
Clojure
Elixir
Go
Groovy
Haskell
Java
JavaScript
Perl
PHP
Python
Ruby
Scala

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

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

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

yes

User defined Procedures and Functions

Java, Javascript

Triggers

yes (cache interceptors and events)

yes

via event handler

Hooks

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

yes using Neo4j Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes, but depending on the SQL-Server Edition

Causal Clustering using Raft protocol

available in in Enterprise Version only

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Causal and Eventual Consistency configurable in Causal Cluster setup
Immediate Consistency in stand-alone mode

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

relationship in graphs

Transaction concepts

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

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 can be defined per table

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

Users, roles and permissions. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)

Access rights for users and roles; record level security configurable

More information provided by the system vendor

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Market metrics

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Licensing and pricing models

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