DBMS > Machbase Neo vs. Oracle Berkeley DB vs. Postgres-XL vs. PostgreSQL vs. Yanza

System Properties Comparison Machbase Neo vs. Oracle Berkeley DB vs. Postgres-XL vs. PostgreSQL vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

TimeSeries DBMS for AIoT and BigData

Widely used in-process key-value store

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Time Series DBMS for IoT Applications

Primary database model

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	0.17
Rank	#337	Overall
	#30	Time Series DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	636.25
Rank	#4	Overall
	#4	Relational DBMS

Website

machbase.com

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

www.postgres-xl.org

www.postgresql.org

yanza.com

Technical documentation

machbase.com/dbms

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

www.postgres-xl.org/documentation

www.postgresql.org/docs

Developer

Machbase

Oracle

originally developed by Sleepycat, which was acquired by Oracle

PostgreSQL Global Development Group

www.postgresql.org/developer

Yanza

Initial release

2013

1994

2014

since 2012, originally named StormDB

1989

1989: Postgres, 1996: PostgreSQL

2015

Current release

V8.0, August 2023

18.1.40, May 2020

10 R1, October 2018

16.3, May 2024

License

Commercial or Open Source

commercial

free test version available

Open Source

commercial license available

Open Source

Mozilla public license

Open Source

BSD

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

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Implementation language

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

Server operating systems

Linux
macOS
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
macOS

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Windows

Data scheme

yes

schema-free

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

only with the Berkeley DB XML edition

yes

XML type, but no XML query functionality

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

SQL interfaced based on SQLite is available

yes

distributed, parallel query execution

yes

standard with numerous extensions

APIs and other access methods

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HTTP API

Supported programming languages

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

.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++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

any language that supports HTTP calls

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes

only for the SQL API

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

partitioning by range, list and (since PostgreSQL 11) by hash

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

other methods possible by using 3rd party extensions

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

MVCC

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

volatile and lookup table

yes

User concepts

Access control

simple password-based access control

fine grained access rights according to SQL-standard

More information provided by the system vendor

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