DBMS > Firebase Realtime Database vs. Riak TS vs. Sequoiadb vs. Solr

System Properties Comparison Firebase Realtime Database vs. Riak TS vs. Sequoiadb vs. Solr

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Editorial information provided by DB-Engines

Name

Description

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

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

NewSQL database with distributed OLTP and SQL

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Document store

Time Series DBMS

Document store
Relational DBMS

Search engine

Secondary database models

Spatial DBMS

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

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

firebase.google.com/products/realtime-database

www.sequoiadb.com

solr.apache.org

Technical documentation

firebase.google.com/docs/database

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

www.sequoiadb.com/en/index.php?m=Files&a=index

solr.apache.org/resources.html

Developer

Google

acquired by Google 2014

Open Source, formerly Basho Technologies

Sequoiadb Ltd.

Apache Software Foundation

Initial release

2012

2015

2013

2006

Current release

3.0.0, September 2022

9.6.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Server: AGPL; Client: Apache V2

Open Source

Apache Version 2

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

C++

Java

Server operating systems

hosted

Linux
OS X

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

yes

supports customizable data types and automatic typing

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.

yes

Secondary indexes

yes

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

yes, limited

SQL-like query language

Solr Parallel SQL Interface

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

HTTP API
Native Erlang Interface

proprietary protocol using JSON

Java API
RESTful HTTP/JSON API

Supported programming languages

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
C++
Java
PHP
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

limited functionality with using 'rules'

Erlang

JavaScript

Java plugins

Triggers

Callbacks are triggered when data changes

yes

pre-commit hooks and post-commit hooks

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Eventual Consistency

Foreign keys

Referential integrity

links between datasets can be stored

Transaction concepts

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

yes

Document is locked during a transaction

optimistic locking

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

yes, based on authentication and database rules

simple password-based access control

yes

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More resources

DB-Engines blog posts

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Recent citations in the news

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

Atos cybersecurity blog: Misconfigured Firebase: A real-time cyber threat
18 January 2024, Atos

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14 May 2024, TechCrunch

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