DBMS > CouchDB vs. GridGain vs. MySQL vs. NSDb vs. STSdb

System Properties Comparison CouchDB vs. GridGain vs. MySQL vs. NSDb vs. STSdb

Editorial information provided by DB-Engines

Name

Description

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

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

Widely used open source RDBMS

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Document store

Key-value store
Relational DBMS

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Key-value store

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store
Spatial DBMS

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

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

github.com/STSSoft/STSdb4

Technical documentation

docs.couchdb.org/en/stable

www.gridgain.com/docs/index.html

dev.mysql.com/doc

nsdb.io/Architecture

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

GridGain Systems, Inc.

Oracle

since 2010, originally MySQL AB, then Sun

STS Soft SC

Initial release

2005

2007

1995

2017

2011

Current release

3.3.3, December 2023

GridGain 8.5.1

8.4.0, April 2024

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache Version 2.0

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

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Implementation language

Erlang

Java, C++, .Net

C and C++

Java, Scala

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS

Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

yes

primitive types and user defined types (classes)

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

all fields are automatically indexed

SQL

Support of SQL

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

yes

with proprietary extensions

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

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

ADO.NET
JDBC
ODBC
Proprietary native API

gRPC
HTTP REST
WebSocket

.NET Client API

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

C#
C++
Java
PHP
Python
Ruby
Scala

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

Java
Scala

C#
Java

Server-side scripts

Stored procedures

View functions in JavaScript

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

yes

proprietary syntax

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes (replicated cache)

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

atomic operations within a single document possible

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

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 can be defined per database

Security Hooks for custom implementations

Users with fine-grained authorization concept

no user groups or roles

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