DBMS > DataFS vs. Microsoft Azure AI Search vs. NSDb vs. Spark SQL

System Properties Comparison DataFS vs. Microsoft Azure AI Search vs. NSDb vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Search-as-a-service for web and mobile app development

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Secondary database models

Graph DBMS

Vector DBMS

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

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

newdatabase.com

azure.microsoft.com/en-us/services/search

nsdb.io

spark.apache.org/sql

Technical documentation

dev.mobiland.com/Overview.xsp

learn.microsoft.com/en-us/azure/search

nsdb.io/Architecture

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Mobiland AG

Microsoft

Apache Software Foundation

Initial release

2018

2015

2017

2014

Current release

1.1.263, October 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java, Scala

Scala

Server operating systems

Windows

hosted

Linux
macOS

Linux
OS X
Windows

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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.

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

RESTful HTTP API

gRPC
HTTP REST
WebSocket

JDBC
ODBC

Supported programming languages

.Net
C
C#
C++
VB.Net

C#
Java
JavaScript
Python

Java
Scala

Java
Python
R
Scala

Server-side scripts

Stored procedures

Triggers

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

Implicit feature of the cloud service

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

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

Using Apache Lucene

yes

In-memory capabilities

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

User concepts

Access control

Windows-Profile

yes

using Azure authentication

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