DBMS > NSDb vs. Oracle NoSQL vs. Quasardb

System Properties Comparison NSDb vs. Oracle NoSQL vs. Quasardb

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Editorial information provided by DB-Engines

Name

Description

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

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

Distributed, high-performance timeseries database

Primary database model

Time Series DBMS

Document store
Key-value store
Relational DBMS

Time Series DBMS

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

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

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

nsdb.io

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

quasar.ai

Technical documentation

nsdb.io/Architecture

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

doc.quasar.ai/master

Developer

Oracle

quasardb

Initial release

2017

2011

2009

Current release

23.3, December 2023

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

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

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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, Scala

Java

C++

Server operating systems

Linux
macOS

Linux
Solaris SPARC/x86

BSD
Linux
OS X
Windows

Data scheme

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

schema-free

Typing

predefined data types such as float or date

yes: int, bigint, decimal, string

optional

yes

integer and binary

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.

Secondary indexes

all fields are automatically indexed

yes

with tags

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

gRPC
HTTP REST
WebSocket

RESTful HTTP API

HTTP API

Supported programming languages

Java
Scala

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

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

consistent hashing

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

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

configurable

ACID within a storage node (=shard)

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Using Apache Lucene

yes

by using LevelDB

In-memory capabilities

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

yes

off heap cache

yes

Transient mode

User concepts

Access control

Access rights for users and roles

Cryptographically strong user authentication and audit trail

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