DBMS > jBASE vs. Kinetica vs. Microsoft Azure Cosmos DB vs. NSDb vs. RavenDB

System Properties Comparison jBASE vs. Kinetica vs. Microsoft Azure Cosmos DB vs. NSDb vs. RavenDB

Editorial information provided by DB-Engines

Name

Description

A robust multi-value DBMS comprising development tools and middleware

Fully vectorized database across both GPUs and CPUs

Globally distributed, horizontally scalable, multi-model database service

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

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Multivalue DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Document store

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	1.49
Rank	#156	Overall
	#3	Multivalue DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	2.84
Rank	#101	Overall
	#18	Document stores

Website

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

www.kinetica.com

azure.microsoft.com/services/cosmos-db

nsdb.io

ravendb.net

Technical documentation

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

docs.kinetica.com

learn.microsoft.com/azure/cosmos-db

nsdb.io/Architecture

ravendb.net/docs

Developer

Rocket Software (formerly Zumasys)

Kinetica

Microsoft

Hibernating Rhinos

Initial release

1991

2012

2014

2017

2010

Current release

5.7

7.1, August 2021

5.4, July 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

AGPL version 3, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

Java, Scala

Server operating systems

AIX
Linux
Windows

Linux

hosted

Linux
macOS

Linux
macOS
Raspberry Pi
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

optional

yes

JSON types

yes: int, bigint, decimal, string

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

Secondary indexes

yes

All properties auto-indexed by default

all fields are automatically indexed

yes

SQL

Support of SQL

Embedded SQL for jBASE in BASIC

SQL-like DML and DDL statements

SQL-like query language

SQL-like query language (RQL)

APIs and other access methods

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

JDBC
ODBC
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

gRPC
HTTP REST
WebSocket

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

Supported programming languages

.Net
Basic
Jabbascript
Java

C++
Java
JavaScript (Node.js)
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Java
Scala

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

Server-side scripts

Stored procedures

yes

user defined functions

JavaScript

yes

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

Implicit feature of the cloud service

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual 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.

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID, Cluster-wide transaction available

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

In-memory capabilities

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

yes

GPU vRAM or System RAM

User concepts

Access control

Access rights can be defined down to the item level

Access rights for users and roles on table level

Access rights can be defined down to the item level

Authorization levels configured per client per database

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