DBMS > GridGain vs. Microsoft SQL Server vs. Percona Server for MySQL vs. PouchDB vs. Valentina Server

System Properties Comparison GridGain vs. Microsoft SQL Server vs. Percona Server for MySQL vs. PouchDB vs. Valentina Server

Editorial information provided by DB-Engines

Name

Description

GridGain is an in-memory computing platform, built on Apache Ignite

Microsofts flagship relational DBMS

Enhanced drop-in replacement for MySQL based on XtraDB or TokuDB storage engines with improved performance and additional diagnostic and management features.

JavaScript DBMS with an API inspired by CouchDB

Object-relational database and reports server

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Document 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	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	2.03
Rank	#122	Overall
	#57	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Website

www.gridgain.com

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

www.percona.com/software/mysql-database/percona-server

pouchdb.com

www.valentina-db.net

Technical documentation

www.gridgain.com/docs/index.html

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

www.percona.com/downloads/Percona-Server-LATEST

pouchdb.com/guides

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

GridGain Systems, Inc.

Microsoft

Percona

Apache Software Foundation

Paradigma Software

Initial release

2007

1989

2008

2012

1999

Current release

GridGain 8.5.1

SQL Server 2022, November 2022

8.0.36-28, 2024

7.1.1, June 2019

5.7.5

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

GPL version 2

Open Source

commercial

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++, .Net

C++

C and C++

JavaScript

Server operating systems

Linux
OS X
Solaris
Windows

Linux
Windows

Linux

server-less, requires a JavaScript environment (browser, Node.js)

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

via views

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

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

ADO.NET
JDBC
ODBC

HTTP REST

only for PouchDB Server
JavaScript API

ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

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

Ada
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

JavaScript

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

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

yes

View functions in JavaScript

yes

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication
XtraDB Cluster

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

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

by using IndexedDB, WebSQL or LevelDB as backend

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

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

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