DBMS > Apache Derby vs. Bangdb vs. Greenplum

System Properties Comparison Apache Derby vs. Bangdb vs. Greenplum

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Editorial information provided by DB-Engines

Name

Description

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Converged and high performance database for device data, events, time series, document and graph

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.

Primary database model

Relational DBMS

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	4.12
Rank	#69	Overall
	#37	Relational DBMS

Score	0.06
Rank	#353	Overall
	#48	Document stores
	#36	Graph DBMS
	#35	Time Series DBMS

Score	7.39
Rank	#47	Overall
	#29	Relational DBMS

Website

db.apache.org/derby

bangdb.com

greenplum.org

Technical documentation

db.apache.org/derby/manuals/index.html

docs.bangdb.com

docs.greenplum.org

Developer

Apache Software Foundation

Sachin Sinha, BangDB

Pivotal Software Inc.

Initial release

1997

2012

2005

Current release

10.17.1.0, November 2023

BangDB 2.0, October 2021

7.0.0, September 2023

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

BSD 3

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, C++

Server operating systems

All OS with a Java VM

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

yes

SQL like support with command line tool

yes

APIs and other access methods

JDBC

Proprietary protocol
RESTful HTTP API

JDBC
ODBC

Supported programming languages

Java

C
C#
C++
Java
Python

C
Java
Perl
Python
R

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor, Knob for CAP (enterprise version only)

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable consistency, set CAP knob accordingly

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

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes

yes, run db with in-memory only mode

User concepts

Access control

fine grained access rights according to SQL-standard

yes (enterprise version only)

fine grained access rights according to SQL-standard

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