DBMS > Faircom DB vs. Google Cloud Firestore vs. Valentina Server vs. Vitess

System Properties Comparison Faircom DB vs. Google Cloud Firestore vs. Valentina Server vs. Vitess

Please select another system to include it in the comparison.

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.

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

Object-relational database and reports server

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Key-value store
Relational DBMS

Document store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

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

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

www.faircom.com/products/faircom-db

firebase.google.com/products/firestore

www.valentina-db.net

vitess.io

Technical documentation

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

firebase.google.com/docs/firestore

valentina-db.com/docs/dokuwiki/v5/doku.php

vitess.io/docs

Developer

FairCom Corporation

Google

Paradigma Software

The Linux Foundation, PlanetScale

Initial release

1979

2017

1999

2013

Current release

V12, November 2020

5.7.5

15.0.2, December 2022

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

Apache Version 2.0, commercial licenses available

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

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

easily portable to other OSs

hosted

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.

Secondary indexes

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

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

ODBC

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

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

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

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++

yes, Firebase Rules & Cloud Functions

yes

proprietary syntax

Triggers

yes

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

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).

Multi-source replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

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

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

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

yes

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

show all

Recent citations in the news

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

World's First Converged IIoT Hub to be Showcased at IoT Tech Expo
3 September 2021, Automation.com

provided by Google News

Google's AI-First Strategy Brings Vector Support To Cloud Databases
1 March 2024, Forbes

Realtime vs Cloud Firestore: Which Firebase Database to go?
8 March 2024, Appinventiv

Google's Cloud Firestore is now generally available
31 January 2019, ZDNet

Google launches Cloud Firestore, a new document database for app developers
3 October 2017, TechCrunch

Google's Cloud-Native NoSQL Database Cloud Firestore Is Now Generally Available
8 February 2019, InfoQ.com

provided by Google News

A Look at Valentina — SitePoint
18 April 2014, SitePoint

MySQL GUI Tools for Windows and Ubuntu/Linux: Top 8 free or open source
7 December 2018, H2S Media

provided by Google News

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

PlanetScale grabs YouTube-developed open-source tech, promises Vitess DBaaS with on-the-fly schema changes
18 May 2021, The Register

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

With Vitess 4.0, database vendor matures cloud-native platform
13 November 2019, TechTarget

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

provided by Google News

Share this page