DBMS > GridGain vs. Microsoft SQL Server vs. SWC-DB vs. Titan vs. VelocityDB

System Properties Comparison GridGain vs. Microsoft SQL Server vs. SWC-DB vs. Titan vs. VelocityDB

Editorial information provided by DB-Engines

Name

Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.

Description

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

Microsofts flagship relational DBMS

A high performance, scalable Wide Column DBMS

Titan is a Graph DBMS optimized for distributed clusters.

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Wide column store

Graph DBMS

Graph DBMS
Object oriented DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Time Series DBMS

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

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

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Score	0.11
Rank	#354	Overall
	#37	Graph DBMS
	#15	Object oriented DBMS

Website

www.gridgain.com

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

github.com/kashirin-alex/swc-db
www.swcdb.org

github.com/thinkaurelius/titan

velocitydb.com

Technical documentation

www.gridgain.com/docs/index.html

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

github.com/thinkaurelius/titan/wiki

velocitydb.com/UserGuide

Developer

GridGain Systems, Inc.

Microsoft

Alex Kashirin

Aurelius, owned by DataStax

VelocityDB Inc

Initial release

2007

1989

2020

2012

2011

Current release

GridGain 8.5.1

SQL Server 2022, November 2022

0.5, April 2021

7.x

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

GPL V3

Open Source

Apache license, version 2.0

commercial

Cloud-based only

Only available as a cloud service

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Implementation language

Java, C++, .Net

C++

Java

Server operating systems

Linux
OS X
Solaris
Windows

Linux
Windows

Linux

Linux
OS X
Unix
Windows

Any that supports .NET

Data scheme

yes

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

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

yes

SQL-like query language

APIs and other access methods

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

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

Proprietary protocol
Thrift

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

.Net

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

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

C++

Clojure
Java
Python

.Net

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

Triggers

yes (cache interceptors and events)

yes

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

yes

via pluggable storage backends

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes, but depending on the SQL-Server Edition

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graph

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

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

User authentification and security via Rexster Graph Server

Based on Windows Authentication

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