DBMS > Apache Phoenix vs. Greenplum vs. HugeGraph vs. Oracle Berkeley DB vs. Sadas Engine

System Properties Comparison Apache Phoenix vs. Greenplum vs. HugeGraph vs. Oracle Berkeley DB vs. Sadas Engine

Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

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 fast-speed and highly-scalable Graph DBMS

Widely used in-process key-value store

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

Primary database model

Relational DBMS

Graph DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	2.02
Rank	#130	Overall
	#63	Relational DBMS

Score	9.23
Rank	#47	Overall
	#30	Relational DBMS

Score	0.15
Rank	#339	Overall
	#33	Graph DBMS

Score	2.52
Rank	#114	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.03
Rank	#379	Overall
	#156	Relational DBMS

Website

phoenix.apache.org

greenplum.org

github.com/hugegraph
hugegraph.apache.org

www.oracle.com/database/technologies/related/berkeleydb.html

www.sadasengine.com

Technical documentation

phoenix.apache.org

docs.greenplum.org

hugegraph.apache.org/docs

docs.oracle.com/cd/E17076_05/html/index.html

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

Developer

Apache Software Foundation

Pivotal Software Inc.

Baidu

Oracle

originally developed by Sleepycat, which was acquired by Oracle

SADAS s.r.l.

Initial release

2014

2005

2018

1994

2006

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

7.0.0, September 2023

0.9

18.1.40, May 2020

8.0

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

commercial license available

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

Java

C, Java, C++ (depending on the Berkeley DB edition)

C++

Server operating systems

Linux
Unix
Windows

Linux

Linux
macOS
Unix

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

AIX
Linux
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

yes

schema-free

yes

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

only with the Berkeley DB XML edition

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

JDBC

JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC
Proprietary protocol

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

C
Java
Perl
Python
R

Groovy
Java
Python

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

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

Server-side scripts

Stored procedures

user defined functions

yes

asynchronous Gremlin script jobs

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

yes

depending on used storage backend, e.g. Cassandra and HBase

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

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.

yes

managed by 'Learn by Usage'

User concepts

Access control

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

fine grained access rights according to SQL-standard

Users, roles and permissions

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

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More resources

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