DBMS > Greenplum vs. mSQL vs. Tibero vs. Vertica

System Properties Comparison Greenplum vs. mSQL vs. Tibero vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

mSQL (Mini SQL) is a simple and lightweight RDBMS

A secure RDBMS, designed for easy portability from Oracle

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

Column oriented

Secondary database models

Document store
Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	7.71
Rank	#46	Overall
	#29	Relational DBMS

Score	1.26
Rank	#163	Overall
	#74	Relational DBMS

Score	1.46
Rank	#151	Overall
	#70	Relational DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

greenplum.org

hughestech.com.au/products/msql

us.tmaxsoft.com/products/tibero

www.vertica.com

Technical documentation

docs.greenplum.org

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

vertica.com/documentation

Developer

Pivotal Software Inc.

Hughes Technologies

TmaxSoft

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2005

1994

2003

2005

Current release

7.0.0, September 2023

4.4, October 2021

7, September 2022

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free licenses can be provided

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

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C and Assembler

C++

Server operating systems

Linux

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

AIX
HP-UX
Linux
Solaris
Windows

Linux

Data scheme

yes

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

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

since Version 4.2

yes

Secondary indexes

yes

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

SQL

Support of SQL

yes

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

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

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

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

character-based, interactive, front-end utility

Supported programming languages

C
Java
Perl
Python
R

C
C++
Delphi
Java
Perl
PHP
Tcl

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

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

Server-side scripts

Stored procedures

yes

Persistent Stored Procedure (PSM)

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

by range, hash, list or composite

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Multi-source replication
Source-replica replication

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

none

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

In-memory capabilities

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

planned for next version

User concepts

Access control

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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

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More resources

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