DBMS > Cubrid vs. HyperSQL vs. Microsoft SQL Server vs. NCache vs. Vertica

System Properties Comparison Cubrid vs. HyperSQL vs. Microsoft SQL Server vs. NCache vs. Vertica

Editorial information provided by DB-Engines

Name

Description

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Microsofts flagship relational DBMS

Open-Source and Enterprise in-memory Key-Value Store

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Column oriented

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Search engine

Using distributed Lucene

Spatial DBMS
Time Series DBMS

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

Score	1.04
Rank	#187	Overall
	#87	Relational DBMS

Score	3.23
Rank	#93	Overall
	#48	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.96
Rank	#195	Overall
	#29	Key-value stores

Score	10.06
Rank	#42	Overall
	#26	Relational DBMS

Website

cubrid.com (korean)
cubrid.org (english)

hsqldb.org

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

www.alachisoft.com/ncache

www.vertica.com

Technical documentation

cubrid.org/manuals

hsqldb.org/web/hsqlDocsFrame.html

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

www.alachisoft.com/resources/docs

vertica.com/documentation

Developer

CUBRID Corporation, CUBRID Foundation

Microsoft

Alachisoft

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2008

2001

1989

2005

Current release

11.0, January 2021

2.7.2, June 2023

SQL Server 2022, November 2022

5.3.3, April 2024

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

based on BSD license

commercial

restricted free version is available

Open Source

Enterprise Edition available

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++, Java

Java

C++

C#, .NET, .NET Core, Java

C++

Server operating systems

Linux
Windows

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux
Windows

Linux

Data scheme

yes

schema-free

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

HTTP API

JDBC via HTTP
JDBC
ODBC

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

IDistributedCache
JCache
LINQ
Proprietary native API

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

C
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

All languages supporting JDBC/ODBC
Java

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

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

Java Stored Procedures

Java, SQL

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

support for stored procedures with SQL-Server CLR

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

Notifications

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

none

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

yes

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

yes, but depending on the SQL-Server Edition

yes, with selectable consistency level

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking and pessimistic locking

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

fine grained access rights according to SQL-standard

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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