DBMS > DataFS vs. GridGain vs. Hyprcubd vs. Microsoft SQL Server vs. Sphinx

System Properties Comparison DataFS vs. GridGain vs. Hyprcubd vs. Microsoft SQL Server vs. Sphinx

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

Serverless Time Series DBMS

Microsofts flagship relational DBMS

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Object oriented DBMS

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

Search engine

Secondary database models

Graph DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

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

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

newdatabase.com

www.gridgain.com

hyprcubd.com (offline)

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

sphinxsearch.com

Technical documentation

dev.mobiland.com/Overview.xsp

www.gridgain.com/docs/index.html

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

sphinxsearch.com/docs

Developer

Mobiland AG

GridGain Systems, Inc.

Hyprcubd, Inc.

Microsoft

Sphinx Technologies Inc.

Initial release

2018

2007

1989

2001

Current release

1.1.263, October 2022

GridGain 8.5.1

SQL Server 2022, November 2022

3.5.1, February 2023

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

C++

Server operating systems

Windows

Linux
OS X
Solaris
Windows

hosted

Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

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

full-text index on all search fields

SQL

Support of SQL

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

SQL-like query language

yes

SQL-like query language (SphinxQL)

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

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

gRPC (https)

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

Proprietary protocol

Supported programming languages

.Net
C
C#
C++
VB.Net

C#
C++
Java
PHP
Python
Ruby
Scala

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

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

Triggers

no, except callback-events from server when changes happened

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

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

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

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

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Windows-Profile

Security Hooks for custom implementations

token access

fine grained access rights according to SQL-standard

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