DBMS > Greenplum vs. GridDB vs. Hawkular Metrics vs. Sadas Engine

System Properties Comparison Greenplum vs. GridDB vs. Hawkular Metrics vs. Sadas Engine

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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.

Scalable in-memory time series database optimized for IoT and Big Data

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Key-value store
Relational DBMS

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

Score	7.71
Rank	#46	Overall
	#29	Relational DBMS

Score	1.91
Rank	#123	Overall
	#10	Time Series DBMS

Score	0.01
Rank	#377	Overall
	#39	Time Series DBMS

Score	0.00
Rank	#385	Overall
	#159	Relational DBMS

Website

Technical documentation

docs.greenplum.org

docs.griddb.net

www.hawkular.org/hawkular-metrics/docs/user-guide

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Pivotal Software Inc.

Toshiba Corporation

Community supported by Red Hat

SADAS s.r.l.

Initial release

2005

2013

2014

2006

Current release

7.0.0, September 2023

5.1, August 2022

8.0

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

Apache 2.0

commercial

free trial version available

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++

Java

C++

Server operating systems

Linux

Linux
OS X
Windows

AIX
Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

SQL92, SQL-like TQL (Toshiba Query Language)

yes

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

HTTP REST

JDBC
ODBC
Proprietary protocol

Supported programming languages

C
Java
Perl
Python
R

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

Go
Java
Python
Ruby

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

based on Cassandra

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID at container level

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

managed by 'Learn by Usage'

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users can be defined per database

Access rights for users, groups and roles according to SQL-standard

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More resources

Recent citations in the news

Chapter 1. Introducing the Greenplum Database
6 December 2018, O'Reilly Media

VMware Greenplum on AWS: Parallel Postgres for Enterprise Analytics at Scale
9 September 2019, AWS Blog

Greenplum 6 ventures outside the analytic box
19 March 2019, ZDNet

EMC and Greenplum Dress Elephant for IT Parade
8 December 2011, WIRED

EMC Introduces Breakthrough 'Big Data' Computing System
13 October 2010, PR Newswire

provided by Google News

TOSHIBA DIGITAL SOLUTIONS CORPORATION
1 November 2020, global.toshiba

Toshiba launches cloudy managed IoT database service running its own GridDB
8 April 2021, The Register

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3 December 2019, global.toshiba

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17 June 2020, global.toshiba

IoT: Opt for the Right Open Source Database
11 August 2023, Open Source For You

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