DBMS > Apache IoTDB vs. Firebase Realtime Database vs. Hive vs. RethinkDB vs. Snowflake

System Properties Comparison Apache IoTDB vs. Firebase Realtime Database vs. Hive vs. RethinkDB vs. Snowflake

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.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

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	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	2.74
Rank	#105	Overall
	#19	Document stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

iotdb.apache.org

firebase.google.com/products/realtime-database

hive.apache.org

rethinkdb.com

www.snowflake.com

Technical documentation

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

firebase.google.com/docs/database

cwiki.apache.org/confluence/display/Hive/Home

rethinkdb.com/docs

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Google

acquired by Google 2014

Apache Software Foundation

initially developed by Facebook

The Linux Foundation

since July 2017

Snowflake Computing Inc.

Initial release

2018

2012

2009

2014

Current release

1.1.0, April 2023

3.1.3, April 2022

2.4.1, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2

Open Source

Apache Version 2

commercial

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

Java

C++

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

All OS with a Java VM

Linux
OS X
Windows

hosted

Data scheme

yes

schema-free

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
Native API

Android
iOS
JavaScript API
RESTful HTTP API

JDBC
ODBC
Thrift

CLI Client
JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Java
JavaScript
Objective-C

C++
Java
PHP
Python

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

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

limited functionality with using 'rules'

yes

user defined functions and integration of map-reduce

user defined functions

Triggers

yes

Callbacks are triggered when data changes

Client-side triggers through changefeeds

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

range based

yes

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

selectable replication factor

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

query execution via MapReduce

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

Atomic single-document operations

ACID

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

Access rights for users, groups and roles

yes

users and table-level permissions

Users with fine-grained authorization concept, user roles and pluggable authentication

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.