DBMS > Greenplum vs. Hive vs. Riak TS

System Properties Comparison Greenplum vs. Hive vs. Riak TS

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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.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	9.23
Rank	#47	Overall
	#30	Relational DBMS

Score	62.59
Rank	#18	Overall
	#12	Relational DBMS

Score	0.25
Rank	#308	Overall
	#26	Time Series DBMS

Website

greenplum.org

hive.apache.org

Technical documentation

docs.greenplum.org

cwiki.apache.org/confluence/display/Hive/Home

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Pivotal Software Inc.

Apache Software Foundation

initially developed by Facebook

Open Source, formerly Basho Technologies

Initial release

2005

2012

2015

Current release

7.0.0, September 2023

3.1.3, April 2022

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2

Open Source

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

Erlang

Server operating systems

Linux

All OS with a Java VM

Linux
OS X

Data scheme

yes

schema-free

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

Secondary indexes

yes

restricted

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes, limited

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
Thrift

HTTP API
Native Erlang Interface

Supported programming languages

C
Java
Perl
Python
R

C++
Java
PHP
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

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

Access rights for users, groups and roles

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More resources

DB-Engines blog posts

Why is Hadoop not listed in the DB-Engines Ranking?
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