DBMS > Cubrid vs. Greenplum vs. IRONdb vs. Spark SQL

System Properties Comparison Cubrid vs. Greenplum vs. IRONdb vs. Spark SQL

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Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

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.

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	8.37
Rank	#48	Overall
	#30	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

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

greenplum.org

www.circonus.com/solutions/time-series-database/

spark.apache.org/sql

Technical documentation

cubrid.org/manuals

docs.greenplum.org

docs.circonus.com/irondb/category/getting-started

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

CUBRID Corporation, CUBRID Foundation

Pivotal Software Inc.

Circonus LLC.

Apache Software Foundation

Initial release

2008

2005

2017

2014

Current release

11.0, January 2021

7.0.0, September 2023

V0.10.20, January 2018

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C, C++, Java

C and C++

Scala

Server operating systems

Linux
Windows

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

JDBC
ODBC

HTTP API

JDBC
ODBC

Supported programming languages

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

C
Java
Perl
Python
R

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

yes

yes, in Lua

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Automatic, metric affinity per node

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

configurable replication factor, datacenter aware

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

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.

User concepts

Access control

fine grained access rights according to SQL-standard

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