DBMS > Oracle NoSQL vs. Postgres-XL vs. RavenDB vs. Yanza

System Properties Comparison Oracle NoSQL vs. Postgres-XL vs. RavenDB vs. Yanza

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Editorial information provided by DB-Engines

Name

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

Description

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

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

Open Source Operational and Transactional Enterprise NoSQL Document Database

Time Series DBMS for IoT Applications

Primary database model

Document store
Key-value store
Relational DBMS

Relational DBMS

Document store

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Website

www.oracle.com/database/nosql/technologies/nosql

www.postgres-xl.org

ravendb.net

yanza.com

Technical documentation

docs.oracle.com/en/database/other-databases/nosql-database/index.html

www.postgres-xl.org/documentation

ravendb.net/docs

Developer

Oracle

Hibernating Rhinos

Yanza

Initial release

2011

2014

since 2012, originally named StormDB

2010

2015

Current release

23.3, December 2023

10 R1, October 2018

5.4, July 2022

License

Commercial or Open Source

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

Mozilla public license

Open Source

AGPL version 3, commercial license available

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Server operating systems

Linux
Solaris SPARC/x86

Linux
macOS

Linux
macOS
Raspberry Pi
Windows

Windows

Data scheme

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

schema-free

Typing

predefined data types such as float or date

optional

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

distributed, parallel query execution

SQL-like query language (RQL)

APIs and other access methods

RESTful HTTP API

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

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

HTTP API

Supported programming languages

C
C#
Go
Java
JavaScript (Node.js)
Python

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

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

any language that supports HTTP calls

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

configurable

ACID within a storage node (=shard)

ACID

MVCC

ACID, Cluster-wide transaction available

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

off heap cache

User concepts

Access control

Access rights for users and roles

fine grained access rights according to SQL-standard

Authorization levels configured per client per database

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