DBMS > FatDB vs. Google Cloud Firestore vs. MySQL vs. OpenTSDB vs. Solr

System Properties Comparison FatDB vs. Google Cloud Firestore vs. MySQL vs. OpenTSDB vs. Solr

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

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.

Widely used open source RDBMS

Scalable Time Series DBMS based on HBase

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

Primary database model

Document store
Key-value store

Document store

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Search engine

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	1.68
Rank	#142	Overall
	#12	Time Series DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

firebase.google.com/products/firestore

www.mysql.com

opentsdb.net

solr.apache.org

Technical documentation

firebase.google.com/docs/firestore

dev.mysql.com/doc

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

solr.apache.org/resources.html

Developer

FatCloud

Google

Oracle

since 2010, originally MySQL AB, then Sun

currently maintained by Yahoo and other contributors

Apache Software Foundation

Initial release

2012

2017

1995

2011

2006

Current release

8.4.0, April 2024

9.6.1, May 2024

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

LGPL

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C and C++

Java

Server operating systems

Windows

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux
Windows

All OS with a Java VM

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

Data scheme

schema-free

yes

schema-free

yes

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

Typing

predefined data types such as float or date

yes

numeric data for metrics, strings for tags

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

All search fields are automatically indexed

SQL

Support of SQL

Via inetgration in SQL Server

yes

with proprietary extensions

Solr Parallel SQL Interface

APIs and other access methods

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

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

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API
Telnet API

Java API
RESTful HTTP/JSON API

Supported programming languages

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Erlang
Go
Java
Python
R
Ruby

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

Server-side scripts

Stored procedures

yes

via applications

yes, Firebase Rules & Cloud Functions

yes

proprietary syntax

Java plugins

Triggers

yes

via applications

yes, with Cloud Functions

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

based on HBase

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

Multi-source replication
Source-replica replication

selectable replication factor

based on HBase

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Using Cloud Dataflow

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

based on HBase

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

yes

ACID

not for MyISAM storage engine

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

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.

Users with fine-grained authorization concept

no user groups or roles

yes

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