DBMS > Hive vs. JaguarDB vs. Microsoft SQL Server vs. MySQL vs. SWC-DB

System Properties Comparison Hive vs. JaguarDB vs. Microsoft SQL Server vs. MySQL vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

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

Performant, highly scalable DBMS for AI and IoT applications

Microsofts flagship relational DBMS

Widely used open source RDBMS

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

Key-value store
Vector DBMS

Relational DBMS

Key/Value like access via memcached API

Wide column store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

Time Series DBMS

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

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

hive.apache.org

www.jaguardb.com

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

www.mysql.com

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

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

www.jaguardb.com/support.html

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

dev.mysql.com/doc

Developer

Apache Software Foundation

initially developed by Facebook

DataJaguar, Inc.

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Alex Kashirin

Initial release

2012

2015

1989

1995

2020

Current release

3.1.3, April 2022

3.3 July 2023

SQL Server 2022, November 2022

8.4.0, April 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

GPL V3.0

commercial

restricted free version is available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

the server part. Clients available in other languages

C++

C and C++

C++

Server operating systems

All OS with a Java VM

Linux

Linux
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Data scheme

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

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

with proprietary extensions

SQL-like query language

APIs and other access methods

JDBC
ODBC
Thrift

JDBC
ODBC

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

ADO.NET
JDBC
ODBC
Proprietary native API

Proprietary protocol
Thrift

Supported programming languages

C++
Java
PHP
Python

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C++

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

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Access rights for users, groups and roles

rights management via user accounts

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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