DBMS > Hyprcubd vs. Ignite vs. Microsoft SQL Server vs. Oracle Coherence vs. Sphinx

System Properties Comparison Hyprcubd vs. Ignite vs. Microsoft SQL Server vs. Oracle Coherence vs. Sphinx

Editorial information provided by DB-Engines

Name

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

Description

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.

Microsofts flagship relational DBMS

Oracles in-memory data grid solution

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

Primary database model

Time Series DBMS

Key-value store
Relational DBMS

Relational DBMS

Key-value store

Search engine

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	3.11
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	1.97
Rank	#129	Overall
	#22	Key-value stores

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

hyprcubd.com (offline)

ignite.apache.org

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

www.oracle.com/java/coherence

sphinxsearch.com

Technical documentation

apacheignite.readme.io/docs

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

docs.oracle.com/en/middleware/standalone/coherence

sphinxsearch.com/docs

Developer

Hyprcubd, Inc.

Apache Software Foundation

Microsoft

Oracle

Sphinx Technologies Inc.

Initial release

2015

1989

2007

2001

Current release

Apache Ignite 2.6

SQL Server 2022, November 2022

14.1, August 2023

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

restricted free version is available

commercial

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

C++, Java, .Net

C++

Java

C++

Server operating systems

hosted

Linux
OS X
Solaris
Windows

Linux
Windows

All OS with a Java VM

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

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

SQL-like query language

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

yes

SQL-like query language (SphinxQL)

APIs and other access methods

gRPC (https)

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

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

JCache
JPA
RESTful HTTP API

Proprietary protocol

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
C++
Java

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

yes (cache interceptors and events)

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

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

yes, with selectable consistency level

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

Eventual Consistency

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

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

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

token access

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

authentification to access the cache via certificates or http basic authentication

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