DBMS > Citus vs. FatDB vs. Google Cloud Firestore vs. Heroic vs. OpenTSDB

System Properties Comparison Citus vs. FatDB vs. Google Cloud Firestore vs. Heroic vs. OpenTSDB

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

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.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Scalable Time Series DBMS based on HBase

Primary database model

Relational DBMS

Document store
Key-value store

Document store

Time Series DBMS

Secondary database models

Document store

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

Score	2.06
Rank	#117	Overall
	#56	Relational DBMS

Score	6.46
Rank	#54	Overall
	#9	Document stores

Score	0.02
Rank	#365	Overall
	#37	Time Series DBMS

Score	1.58
Rank	#143	Overall
	#12	Time Series DBMS

Website

www.citusdata.com

firebase.google.com/products/firestore

github.com/spotify/heroic

opentsdb.net

Technical documentation

docs.citusdata.com

firebase.google.com/docs/firestore

spotify.github.io/heroic

opentsdb.net/docs/build/html/index.html

Developer

FatCloud

Google

Spotify

currently maintained by Yahoo and other contributors

Initial release

2010

2012

2017

2014

2011

Current release

8.1, December 2018

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

commercial

Open Source

Apache 2.0

Open Source

LGPL

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

Server operating systems

Linux

Windows

hosted

Linux
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

numeric data for metrics, strings for tags

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

specific XML type available, but no XML query functionality

Secondary indexes

yes

via Elasticsearch

SQL

Support of SQL

yes

standard, with numerous extensions

Via inetgration in SQL Server

APIs and other access methods

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

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

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

HQL (Heroic Query Language, a JSON-based language)
HTTP API

HTTP API
Telnet API

Supported programming languages

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

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

Erlang
Go
Java
Python
R
Ruby

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

via applications

yes, Firebase Rules & Cloud Functions

Triggers

yes

via applications

yes, with Cloud Functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

selectable replication factor

Multi-source replication

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

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.

User concepts

Access control

fine grained access rights according to SQL-standard

Can implement custom security layer via applications

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

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More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

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Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

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Recent citations in the news

Microsoft Expands Azure PostgreSQL Moxy Through Citus Data Purchase
31 May 2024, Data Center Knowledge

Microsoft acquires Citus Data, re-affirming its commitment to Open Source and accelerating Azure PostgreSQL performance and scale
24 January 2019, Microsoft

Microsoft acquires Citus Data
24 January 2019, TechCrunch

Microsoft acquires Citus Data, creators of a cloud-friendly version of the PostgreSQL database
24 January 2019, GeekWire

Microsoft acquires another open-source company, Citus Data
24 January 2019, CNBC

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23 March 2020, Wccftech

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27 October 2020, VentureBeat

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