DBMS > Cubrid vs. Graph Engine vs. Microsoft SQL Server vs. Sqrrl vs. Teradata

System Properties Comparison Cubrid vs. Graph Engine vs. Microsoft SQL Server vs. Sqrrl vs. Teradata

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

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

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Microsofts flagship relational DBMS

Adaptable, secure NoSQL built on Apache Accumulo

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

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

www.graphengine.io

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

sqrrl.com

www.teradata.com

Technical documentation

cubrid.org/manuals

www.graphengine.io/docs/manual

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

docs.teradata.com

Developer

CUBRID Corporation, CUBRID Foundation

Microsoft

Amazon

originally Sqrrl Data, Inc.

Teradata

Initial release

2008

2010

1989

2012

1984

Current release

11.0, January 2021

SQL Server 2022, November 2022

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

MIT License

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

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Implementation language

C, C++, Java

.NET and C

C++

Java

Server operating systems

Linux
Windows

.NET

Linux
Windows

Linux

hosted
Linux

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

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

yes

SQL 2016 + extensions

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

RESTful HTTP API

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

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

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

C#
C++
F#
Visual Basic

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

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

Java Stored Procedures

yes

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

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

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

Sharding

making use of Hadoop

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes, but depending on the SQL-Server Edition

selectable replication factor

making use of Hadoop

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic updates per row, document, or graph entity

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

fine grained access rights according to SQL-standard

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