DBMS > Google Cloud Firestore vs. Ingres vs. PouchDB vs. Riak KV vs. SQLite

System Properties Comparison Google Cloud Firestore vs. Ingres vs. PouchDB vs. Riak KV vs. SQLite

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.

Well established RDBMS

JavaScript DBMS with an API inspired by CouchDB

Distributed, fault tolerant key-value store

Widely used embeddable, in-process RDBMS

Primary database model

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

Relational DBMS

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

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	4.11
Rank	#81	Overall
	#44	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

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

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

firebase.google.com/products/firestore

www.actian.com/databases/ingres

pouchdb.com

www.sqlite.org

Technical documentation

firebase.google.com/docs/firestore

docs.actian.com/ingres

pouchdb.com/guides

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

www.sqlite.org/docs.html

Developer

Google

Actian Corporation

Apache Software Foundation

OpenSource, formerly Basho Technologies

Dwayne Richard Hipp

Initial release

2017

1974

originally developed at University Berkely in early 1970s

2012

2009

2000

Current release

11.2, May 2022

7.1.1, June 2019

3.2.0, December 2022

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2, commercial enterprise edition

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

JavaScript

Erlang

Server operating systems

hosted

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

server-less, requires a JavaScript environment (browser, Node.js)

Linux
OS X

server-less

Data scheme

schema-free

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

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.

but tools for importing/exporting data from/to XML-files available

Secondary indexes

yes

via views

restricted

yes

SQL

Support of SQL

yes

SQL-92 is not fully supported

APIs and other access methods

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

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

HTTP REST

only for PouchDB Server
JavaScript API

HTTP API
Native Erlang Interface

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

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

JavaScript

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

yes, Firebase Rules & Cloud Functions

yes

View functions in JavaScript

Erlang

Triggers

yes, with Cloud Functions

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding

with a proxy-based framework, named couchdb-lounge

Sharding

no "single point of failure"

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Ingres Replicator

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

selectable replication factor

none

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

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

via file-system locks

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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

yes, using Riak Security

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