DBMS > Amazon DocumentDB vs. Faircom EDGE vs. MonetDB vs. PouchDB vs. RocksDB

System Properties Comparison Amazon DocumentDB vs. Faircom EDGE vs. MonetDB vs. PouchDB vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

A relational database management system that stores data in columns

JavaScript DBMS with an API inspired by CouchDB

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Document store

Key-value store
Relational DBMS

Relational DBMS

Document store

Key-value store

Secondary database models

Document store
Spatial DBMS

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

aws.amazon.com/documentdb

www.faircom.com/products/faircom-edge

www.monetdb.org

pouchdb.com

rocksdb.org

Technical documentation

aws.amazon.com/documentdb/resources

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

www.monetdb.org/Documentation

pouchdb.com/guides

github.com/facebook/rocksdb/wiki

Developer

FairCom Corporation

MonetDB BV

Apache Software Foundation

Facebook, Inc.

Initial release

2019

1979

2004

2012

2013

Current release

V3, October 2020

Dec2023 (11.49), December 2023

7.1.1, June 2019

8.11.4, April 2024

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Mozilla Public License 2.0

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

ANSI C, C++

JavaScript

C++

Server operating systems

hosted

Android
Linux

ARM, x86
Raspbian
Windows

FreeBSD
Linux
OS X
Solaris
Windows

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

Linux

Data scheme

schema-free

Flexible Schema (defined schema, partial schema, schema free)

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

SQL

Support of SQL

yes

ANSI SQL queries

yes

SQL 2003 with some extensions

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

HTTP REST

only for PouchDB Server
JavaScript API

C++ API
Java API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

JavaScript

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes, in SQL, C, R

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

File partitioning

Customizable business rules for partitioning

Sharding via remote tables

Sharding

with a proxy-based framework, named couchdb-lounge

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

yes

Synchronous and asynchronous realtime replication based on transaction logs

none

Source-replica replication available in experimental status

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency
Tunable Consistency

Eventual Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

when using SQL

yes

Transaction concepts

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

Atomic single-document operations

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

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

Access rights for users and roles

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

fine grained access rights according to SQL-standard

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