DBMS > CouchDB vs. Google Cloud Firestore vs. RocksDB vs. Stardog

System Properties Comparison CouchDB vs. Google Cloud Firestore vs. RocksDB vs. Stardog

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Editorial information provided by DB-Engines

Name

Description

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

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.

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Document store

Key-value store

Graph DBMS
RDF store

Secondary database models

Spatial DBMS

using the Geocouch extension

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

Score	10.26
Rank	#45	Overall
	#7	Document stores

Score	8.96
Rank	#48	Overall
	#8	Document stores

Score	4.00
Rank	#84	Overall
	#11	Key-value stores

Score	2.05
Rank	#129	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

couchdb.apache.org

firebase.google.com/products/firestore

rocksdb.org

www.stardog.com

Technical documentation

docs.couchdb.org/en/stable

firebase.google.com/docs/firestore

github.com/facebook/rocksdb/wiki

docs.stardog.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Google

Facebook, Inc.

Stardog-Union

Initial release

2005

2017

2013

2010

Current release

3.3.3, December 2023

8.11.4, April 2024

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

BSD

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Erlang

C++

Java

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

hosted

Linux

Linux
macOS
Windows

Data scheme

schema-free

schema-free and OWL/RDFS-schema support

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.

Import/export of XML data possible

Secondary indexes

yes

via views

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

RESTful HTTP/JSON API

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

C++ API
Java API

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

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

C
C++
Go
Java
Perl
Python
Ruby

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

View functions in JavaScript

yes, Firebase Rules & Cloud Functions

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

yes, with Cloud Functions

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

yes

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

atomic operations within a single document possible

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

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 can be defined per database

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

Access rights for users and roles

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