DBMS > Amazon DocumentDB vs. DataFS vs. Faircom DB vs. Infobright vs. OpenTSDB

System Properties Comparison Amazon DocumentDB vs. DataFS vs. Faircom DB vs. Infobright vs. OpenTSDB

Editorial information provided by DB-Engines

Name

Description

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

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Scalable Time Series DBMS based on HBase

Primary database model

Document store

Object oriented DBMS

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Graph DBMS

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

Score	1.91
Rank	#131	Overall
	#24	Document stores

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	1.02
Rank	#192	Overall
	#90	Relational DBMS

Score	1.68
Rank	#142	Overall
	#12	Time Series DBMS

Website

aws.amazon.com/documentdb

newdatabase.com

www.faircom.com/products/faircom-db

ignitetech.com/softwarelibrary/infobrightdb

opentsdb.net

Technical documentation

aws.amazon.com/documentdb/resources

dev.mobiland.com/Overview.xsp

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

opentsdb.net/docs/build/html/index.html

Developer

Mobiland AG

FairCom Corporation

Ignite Technologies Inc.; formerly InfoBright Inc.

currently maintained by Yahoo and other contributors

Initial release

2019

2018

1979

2005

2011

Current release

1.1.263, October 2022

V12, November 2020

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

Open Source

LGPL

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++

Java

Server operating systems

hosted

Windows

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

easily portable to other OSs

Linux
Windows

Data scheme

schema-free

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

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

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

yes

numeric data for metrics, strings for tags

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

Knowledge Grid Technology used instead

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

.NET Client API
Proprietary client DLL
WinRT client

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

ADO.NET
JDBC
ODBC

HTTP API
Telnet API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

.Net
C
C#
C++
VB.Net

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

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

Erlang
Go
Java
Python
R
Ruby

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

Triggers

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

none

Proprietary Sharding system

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

none

Sharding

based on HBase

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).

Source-replica replication

selectable replication factor

based on HBase

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

Immediate Consistency

based on HBase

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

ACID

tunable from ACID to Eventually Consistent

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

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

Windows-Profile

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

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

More information provided by the system vendor

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

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