DBMS > Google Cloud Firestore vs. Infobright vs. MonetDB vs. Riak KV

System Properties Comparison Google Cloud Firestore vs. Infobright vs. MonetDB vs. Riak KV

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Editorial information provided by DB-Engines

Name

Description

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.

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

A relational database management system that stores data in columns

Distributed, fault tolerant key-value store

Primary database model

Document store

Relational DBMS

Key-value store

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

Secondary database models

Document store
Spatial DBMS

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

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	1.02
Rank	#192	Overall
	#90	Relational DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

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

Website

firebase.google.com/products/firestore

ignitetech.com/softwarelibrary/infobrightdb

www.monetdb.org

Technical documentation

firebase.google.com/docs/firestore

www.monetdb.org/Documentation

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

Developer

Google

Ignite Technologies Inc.; formerly InfoBright Inc.

MonetDB BV

OpenSource, formerly Basho Technologies

Initial release

2017

2005

2004

2009

Current release

Dec2023 (11.49), December 2023

3.2.0, December 2022

License

Commercial or Open Source

commercial

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

Open Source

Mozilla Public License 2.0

Open Source

Apache version 2, commercial enterprise edition

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

Erlang

Server operating systems

hosted

Linux
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

Knowledge Grid Technology used instead

yes

restricted

SQL

Support of SQL

yes

SQL 2003 with some extensions

APIs and other access methods

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

ADO.NET
JDBC
ODBC

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

HTTP API
Native Erlang Interface

Supported programming languages

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

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

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

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

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

yes, in SQL, C, R

Erlang

Triggers

yes, with Cloud Functions

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding via remote tables

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Source-replica replication

none

Source-replica replication available in experimental status

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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, 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

exploiting MySQL or PostgreSQL frontend capabilities

fine grained access rights according to SQL-standard

yes, using Riak Security

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More resources

DB-Engines blog posts

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12 December 2019, Paul Andlinger

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