DBMS > Apache IoTDB vs. Firebase Realtime Database vs. RethinkDB vs. Riak TS

System Properties Comparison Apache IoTDB vs. Firebase Realtime Database vs. RethinkDB vs. Riak TS

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Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

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

Primary database model

Time Series DBMS

Document store

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	2.74
Rank	#105	Overall
	#19	Document stores

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

iotdb.apache.org

firebase.google.com/products/realtime-database

rethinkdb.com

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

firebase.google.com/docs/database

rethinkdb.com/docs

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

Developer

Apache Software Foundation

Google

acquired by Google 2014

The Linux Foundation

since July 2017

Open Source, formerly Basho Technologies

Initial release

2018

2012

2009

2015

Current release

1.1.0, April 2023

2.4.1, August 2020

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2

Open Source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Erlang

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

Linux
OS X
Windows

Linux
OS X

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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

restricted

SQL

Support of SQL

SQL-like query language

yes, limited

APIs and other access methods

JDBC
Native API

Android
iOS
JavaScript API
RESTful HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Java
JavaScript
Objective-C

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

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

limited functionality with using 'rules'

Erlang

Triggers

yes

Callbacks are triggered when data changes

Client-side triggers through changefeeds

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

range based

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

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

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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

yes, based on authentication and database rules

yes

users and table-level permissions

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More resources

DB-Engines blog posts

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12 December 2019, Paul Andlinger

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