DBMS > Greenplum vs. mSQL vs. OrigoDB vs. Splice Machine

System Properties Comparison Greenplum vs. mSQL vs. OrigoDB vs. Splice Machine

Please select another system to include it in the comparison.

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 fully ACID in-memory object graph database

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	8.37
Rank	#48	Overall
	#30	Relational DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

greenplum.org

hughestech.com.au/products/msql

origodb.com

splicemachine.com

Technical documentation

docs.greenplum.org

origodb.com/docs

splicemachine.com/how-it-works

Developer

Pivotal Software Inc.

Hughes Technologies

Robert Friberg et al

Splice Machine

Initial release

2005

1994

2009

under the name LiveDB

2014

Current release

7.0.0, September 2023

4.4, October 2021

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free licenses can be provided

Open Source

AGPL 3.0, commercial license 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

Java

Server operating systems

Linux

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

Linux
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

yes

A subset of ANSI SQL is implemented

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

yes

APIs and other access methods

JDBC
ODBC

.NET Client API
HTTP API
LINQ

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C
Java
Perl
Python
R

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

client side managed; servers are not synchronized

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

depending on model

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

Write ahead log

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

Role based authorization

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

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

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

1. Introducing the Greenplum Database - Data Warehousing with Greenplum [Book]
6 December 2018, O'Reilly Media

RSA: EMC integrates Hadoop with Greenplum database
26 February 2013, DatacenterDynamics

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

Greenplum 6 review: Jack of all trades, master of some
7 November 2019, InfoWorld

provided by Google News

Writing a Web Service in Perl
9 July 2003, PCQuest

Higher Education PS rules out ghost students before PAC - Zambia
29 November 2018, diggers.news

provided by Google News

Machine learning data pipeline outfit Splice Machine files for insolvency
26 August 2021, The Register

Splice Machine Launches the Splice Machine Feature Store to Simplify Feature Engineering and Democratize Machine ...
19 January 2021, PR Newswire

How Splice Machine's Data Platform for Intelligent Apps Works
29 September 2020, eWeek

Splice Machine Launches Feature Store to Simplify Feature Engineering
19 January 2021, Datanami

Distributed SQL System Review: Snowflake vs Splice Machine
18 September 2019, Towards Data Science

provided by Google News

Share this page