DBMS > Faircom DB vs. Riak KV vs. searchxml vs. Vitess

System Properties Comparison Faircom DB vs. Riak KV vs. searchxml vs. Vitess

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Editorial information provided by DB-Engines

Name

Description

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

DBMS for structured and unstructured content wrapped with an application server

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Key-value store
Relational DBMS

Key-value store

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

Native XML DBMS
Search engine

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

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

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	0.03
Rank	#390	Overall
	#7	Native XML DBMS
	#24	Search engines

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Website

www.faircom.com/products/faircom-db

www.searchxml.net/category/products

vitess.io

Technical documentation

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

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

www.searchxml.net/support/handouts

vitess.io/docs

Developer

FairCom Corporation

OpenSource, formerly Basho Technologies

informationpartners gmbh

The Linux Foundation, PlanetScale

Initial release

1979

2009

2015

2013

Current release

V12, November 2020

3.2.0, December 2022

1.0

15.0.2, December 2022

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache version 2, commercial enterprise edition

commercial

Open Source

Apache Version 2.0, commercial licenses available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

ANSI C, C++

Erlang

C++

Server operating systems

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

easily portable to other OSs

Linux
OS X

Windows

Docker
Linux
macOS

Data scheme

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

schema-free

yes

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

yes

with proprietary extensions

APIs and other access methods

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

HTTP API
Native Erlang Interface

RESTful HTTP API
WebDAV
XQuery
XSLT

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

.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

C++

most other programming languages supported via APIs

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

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

Erlang

yes

on the application server

yes

proprietary syntax

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

no "single point of failure"

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

sychronisation to multiple collections

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

Eventual Consistency

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

links between data sets can be stored

yes

not for MyISAM storage engine

Transaction concepts

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

tunable from ACID to Eventually Consistent

multiple readers, single writer

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

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 with additional protections for files

yes, using Riak Security

Domain, group and role-based access control at the document level and for application services

Users with fine-grained authorization concept

no user groups or roles

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More resources

Recent citations in the news

FairCom kicks off new era of database technology USA - English
10 November 2020, PR Newswire

provided by Google News

Basho Revamps Riak Open-Source Database
22 September 2023, InformationWeek

Riak NoSQL Database: Use Cases and Best Practices
23 December 2011, InfoQ.com

Basho, Maker of Riak NoSQL Database, Raises $25M
13 January 2015, Data Center Knowledge

Riak NoSQL snapped up by Bet365
12 September 2017, ComputerWeekly.com

Basho to Bolster Riak with DB Plug-Ins
5 May 2014, Datanami

provided by Google News

PlanetScale Unveils Distributed MySQL Database Service Based on Vitess
18 May 2021, Datanami

They scaled YouTube -- now they’ll shard everyone with PlanetScale
13 December 2018, TechCrunch

PlanetScale Serves up Vitess-Powered Serverless MySQL
23 November 2021, The New Stack

PlanetScale offers undo button to reverse schema migration without losing data
24 March 2022, The Register

Massively Scaling MySQL Using Vitess
19 February 2019, InfoQ.com

provided by Google News

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