DBMS > Citus vs. Faircom DB vs. Riak KV vs. SQLite

System Properties Comparison Citus vs. Faircom DB vs. Riak KV vs. SQLite

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Editorial information provided by DB-Engines

Name

Description

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

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

Distributed, fault tolerant key-value store

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

Secondary database models

Document store

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

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

www.citusdata.com

www.faircom.com/products/faircom-db

www.sqlite.org

Technical documentation

docs.citusdata.com

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

www.tiot.jp/riak-docs/riak/kv/latest

www.sqlite.org/docs.html

Developer

FairCom Corporation

OpenSource, formerly Basho Technologies

Dwayne Richard Hipp

Initial release

2010

1979

2009

2000

Current release

8.1, December 2018

V12, November 2020

3.2.0, December 2022

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

commercial

Restricted, free version available

Open Source

Apache version 2, commercial enterprise edition

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

ANSI C, C++

Erlang

Server operating systems

Linux

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

easily portable to other OSs

Linux
OS X

server-less

Data scheme

yes

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

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

yes

not rigid because of 'dynamic typing' concept.

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

specific XML type available, but no XML query functionality

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

yes

standard, with numerous extensions

yes, ANSI SQL with proprietary extensions

yes

SQL-92 is not fully supported

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

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

HTTP API
Native Erlang Interface

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

.Net, JavaScript, C/C++

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

no "single point of failure"

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

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

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

yes

Transaction concepts

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

ACID

tunable from ACID to Eventually Consistent

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

fine grained access rights according to SQL-standard

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

yes, using Riak Security

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