DBMS > DataFS vs. Hive vs. Microsoft SQL Server vs. RavenDB

System Properties Comparison DataFS vs. Hive vs. Microsoft SQL Server vs. RavenDB

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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.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Microsofts flagship relational DBMS

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Object oriented DBMS

Relational DBMS

Document store

Secondary database models

Graph DBMS

Document store
Graph DBMS
Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	2.84
Rank	#101	Overall
	#18	Document stores

Website

newdatabase.com

hive.apache.org

www.microsoft.com/en-us/sql-server

ravendb.net

Technical documentation

dev.mobiland.com/Overview.xsp

cwiki.apache.org/confluence/display/Hive/Home

learn.microsoft.com/en-US/sql/sql-server

ravendb.net/docs

Developer

Mobiland AG

Apache Software Foundation

initially developed by Facebook

Microsoft

Hibernating Rhinos

Initial release

2018

2012

1989

2010

Current release

1.1.263, October 2022

3.1.3, April 2022

SQL Server 2022, November 2022

5.4, July 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

restricted free version is available

Open Source

AGPL version 3, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

Windows

All OS with a Java VM

Linux
Windows

Linux
macOS
Raspberry Pi
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

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-like query language (RQL)

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

JDBC
ODBC
Thrift

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

.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
C
C#
C++
VB.Net

C++
Java
PHP
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

Triggers

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes, but depending on the SQL-Server Edition

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

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.

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

User concepts

Access control

Windows-Profile

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

Authorization levels configured per client per database

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