DBMS > Derby vs. EsgynDB vs. Greenplum vs. Hazelcast

System Properties Comparison Derby vs. EsgynDB vs. Greenplum vs. Hazelcast

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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.

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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 widely adopted in-memory data grid

Primary database model

Relational DBMS

Key-value store

Secondary database models

Document store
Spatial DBMS

Document store

JSON support with IMDG 3.12

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

Score	4.71
Rank	#69	Overall
	#37	Relational DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	8.37
Rank	#48	Overall
	#30	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Website

Technical documentation

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

docs.greenplum.org

hazelcast.org/imdg/docs

Developer

Apache Software Foundation

Esgyn

Pivotal Software Inc.

Hazelcast

Initial release

1997

2015

2005

2008

Current release

10.17.1.0, November 2023

7.0.0, September 2023

5.3.6, November 2023

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2; commercial licenses 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

C++, Java

Java

Server operating systems

All OS with a Java VM

Linux

All OS with a Java VM

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

yes

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

JDBC

ADO.NET
JDBC
ODBC

JDBC
ODBC

JCache
JPA
Memcached protocol
RESTful HTTP API

Supported programming languages

Java

All languages supporting JDBC/ODBC/ADO.Net

C
Java
Perl
Python
R

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Event Listeners, Executor Services

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication between multi datacenters

Source-replica replication

yes

Replicated Map

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 or Eventual Consistency selectable by user

Raft Consensus Algorithm

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

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

User concepts

Access control

fine grained access rights according to SQL-standard

Role-based access control

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