DBMS > JaguarDB vs. Microsoft SQL Server vs. Oracle Coherence vs. Spark SQL

System Properties Comparison JaguarDB vs. Microsoft SQL Server vs. Oracle Coherence vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Performant, highly scalable DBMS for AI and IoT applications

Microsofts flagship relational DBMS

Oracles in-memory data grid solution

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

Primary database model

Key-value store
Vector DBMS

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.09
Rank	#356	Overall
	#53	Key-value stores
	#13	Vector DBMS

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Score	2.02
Rank	#131	Overall
	#22	Key-value stores

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

www.jaguardb.com

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

www.oracle.com/java/coherence

spark.apache.org/sql

Technical documentation

www.jaguardb.com/support.html

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

docs.oracle.com/en/middleware/standalone/coherence

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

Developer

DataJaguar, Inc.

Microsoft

Oracle

Apache Software Foundation

Initial release

2015

1989

2007

2014

Current release

3.3 July 2023

SQL Server 2022, November 2022

14.1, August 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

GPL V3.0

commercial

restricted free version is available

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

the server part. Clients available in other languages

C++

Java

Scala

Server operating systems

Linux

Linux
Windows

All OS with a Java VM

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

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

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

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

JCache
JPA
RESTful HTTP API

JDBC
ODBC

Supported programming languages

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

.Net
C++
Java

Java
Python
R
Scala

Server-side scripts

Stored procedures

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

Triggers

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes, but depending on the SQL-Server Edition

yes, with selectable consistency level

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

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

rights management via user accounts

fine grained access rights according to SQL-standard

authentification to access the cache via certificates or http basic authentication

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