DBMS > Coveo vs. Firebase Realtime Database vs. Riak KV vs. Stardog

System Properties Comparison Coveo vs. Firebase Realtime Database vs. Riak KV vs. Stardog

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Editorial information provided by DB-Engines

Name

Description

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Distributed, fault tolerant key-value store

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

Primary database model

Search engine

Document store

Key-value store

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

Graph DBMS
RDF store

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

Score	2.14
Rank	#116	Overall
	#11	Search engines

Score	13.60
Rank	#39	Overall
	#6	Document stores

Score	3.84
Rank	#76	Overall
	#9	Key-value stores

Score	1.93
Rank	#121	Overall
	#10	Graph DBMS
	#6	RDF stores

Website

www.coveo.com

firebase.google.com/products/realtime-database

www.stardog.com

Technical documentation

docs.coveo.com

firebase.google.com/docs/database

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

docs.stardog.com

Developer

Coveo

Google

acquired by Google 2014

OpenSource, formerly Basho Technologies

Stardog-Union

Initial release

2012

2009

2010

Current release

3.2.0, December 2022

7.3.0, May 2020

License

Commercial or Open Source

commercial

Open Source

Apache version 2, commercial enterprise edition

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

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

Implementation language

Erlang

Java

Server operating systems

hosted

Linux
OS X

Linux
macOS
Windows

Data scheme

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

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

restricted

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 API

Android
iOS
JavaScript API
RESTful HTTP API

HTTP API
Native Erlang Interface

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#
Java
JavaScript
Python

Java
JavaScript
Objective-C

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

limited functionality with using 'rules'

Erlang

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

Callbacks are triggered when data changes

yes

pre-commit hooks and post-commit hooks

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

no "single point of failure"

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Eventual Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

links between data sets can be stored

yes

relationships in graphs

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

granular access controls, API key management, content filters

yes, based on authentication and database rules

yes, using Riak Security

Access rights for users and roles

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More resources

DB-Engines blog posts

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12 December 2019, Paul Andlinger

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