DBMS > Apache IoTDB vs. Google Cloud Firestore vs. Postgres-XL vs. RethinkDB vs. STSdb

System Properties Comparison Apache IoTDB vs. Google Cloud Firestore vs. Postgres-XL vs. RethinkDB vs. STSdb

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 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.

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	2.66
Rank	#107	Overall
	#20	Document stores

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

iotdb.apache.org

firebase.google.com/products/firestore

www.postgres-xl.org

rethinkdb.com

github.com/STSSoft/STSdb4

Technical documentation

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

firebase.google.com/docs/firestore

www.postgres-xl.org/documentation

rethinkdb.com/docs

Developer

Apache Software Foundation

Google

The Linux Foundation

since July 2017

STS Soft SC

Initial release

2018

2017

2014

since 2012, originally named StormDB

2009

2011

Current release

1.1.0, April 2023

10 R1, October 2018

2.4.1, August 2020

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Mozilla public license

Open Source

Apache Version 2

Open Source

GPLv2, commercial license available

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++

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

Linux
macOS

Linux
OS X
Windows

Windows

Data scheme

yes

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

yes

primitive types and user defined types (classes)

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

distributed, parallel query execution

APIs and other access methods

JDBC
Native API

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

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

.NET Client API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

C#
Java

Server-side scripts

Stored procedures

yes

yes, Firebase Rules & Cloud Functions

user defined functions

Triggers

yes

yes, with Cloud Functions

yes

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

horizontal partitioning

Sharding

range based

none

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

Multi-source replication

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Using Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

MVCC

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

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

fine grained access rights according to SQL-standard

yes

users and table-level permissions

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