DBMS > JaguarDB vs. Memcached vs. Microsoft SQL Server vs. Spark SQL

System Properties Comparison JaguarDB vs. Memcached vs. Microsoft SQL Server vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Performant, highly scalable DBMS for AI and IoT applications

In-memory key-value store, originally intended for caching

Microsofts flagship relational DBMS

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

Primary database model

Key-value store
Vector 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	20.74
Rank	#32	Overall
	#4	Key-value stores

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

www.jaguardb.com

www.memcached.org

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

spark.apache.org/sql

Technical documentation

www.jaguardb.com/support.html

github.com/memcached/memcached/wiki

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

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

Developer

DataJaguar, Inc.

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Microsoft

Apache Software Foundation

Initial release

2015

2003

1989

2014

Current release

3.3 July 2023

1.6.25, March 2024

SQL Server 2022, November 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

GPL V3.0

Open Source

BSD license

commercial

restricted free version is available

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++

Scala

Server operating systems

Linux

FreeBSD
Linux
OS X
Unix
Windows

Linux
Windows

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

Proprietary protocol

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

JDBC
ODBC

Supported programming languages

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

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

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

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

Repcached, a Memcached patch, provides this functionallity

yes, but depending on the SQL-Server Edition

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

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

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

yes

using SASL (Simple Authentication and Security Layer) protocol

fine grained access rights according to SQL-standard

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