DBMS > Amazon DocumentDB vs. Faircom DB vs. FeatureBase vs. MonetDB

System Properties Comparison Amazon DocumentDB vs. Faircom DB vs. FeatureBase vs. MonetDB

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Editorial information provided by DB-Engines

Name

Description

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

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.

A relational database management system that stores data in columns

Primary database model

Document store

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.91
Rank	#124	Overall
	#22	Document stores

Score	0.18
Rank	#314	Overall
	#45	Key-value stores
	#141	Relational DBMS

Score	0.23
Rank	#296	Overall
	#134	Relational DBMS

Score	1.72
Rank	#135	Overall
	#62	Relational DBMS

Website

aws.amazon.com/documentdb

www.faircom.com/products/faircom-db

www.featurebase.com

www.monetdb.org

Technical documentation

aws.amazon.com/documentdb/resources

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

docs.featurebase.com

www.monetdb.org/Documentation

Developer

FairCom Corporation

Molecula and Pilosa Open Source Contributors

MonetDB BV

Initial release

2019

1979

2017

2004

Current release

V13, July 2024

2022, May 2022

Dec2023 (11.49), December 2023

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

Mozilla Public License 2.0

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++

Server operating systems

hosted

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux
macOS

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

schema-free

schema free, schema optional, schema required, partial schema,

yes

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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.

Secondary indexes

yes

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

SQL queries

yes

SQL 2003 with some extensions

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

gRPC
JDBC
Kafka Connector
ODBC

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

Java
Python

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

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

Sharding via remote tables

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, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

yes

none

Source-replica replication available in experimental status

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

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

Atomic single-document operations

tunable from ACID to Eventually Consistent

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

yes, using Linux fsync

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 access rights according to SQL-standard with additional protections for files

fine grained access rights according to SQL-standard

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