DBMS > Apache Druid vs. DataFS vs. FatDB vs. Hawkular Metrics vs. PouchDB

System Properties Comparison Apache Druid vs. DataFS vs. FatDB vs. Hawkular Metrics vs. PouchDB

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

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Relational DBMS
Time Series DBMS

Object oriented DBMS

Document store
Key-value store

Time Series DBMS

Document store

Secondary database models

Graph DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	2.34
Rank	#112	Overall
	#21	Document stores

Website

Technical documentation

druid.apache.org/docs/latest/design

dev.mobiland.com/Overview.xsp

www.hawkular.org/hawkular-metrics/docs/user-guide

pouchdb.com/guides

Developer

Apache Software Foundation and contributors

Mobiland AG

FatCloud

Community supported by Red Hat

Apache Software Foundation

Initial release

2012

2018

2012

2014

2012

Current release

29.0.1, April 2024

1.1.263, October 2022

7.1.1, June 2019

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

Apache 2.0

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

JavaScript

Server operating systems

Linux
OS X
Unix

Windows

Linux
OS X
Windows

server-less, requires a JavaScript environment (browser, Node.js)

Data scheme

yes

schema-less columns are supported

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

via views

SQL

Support of SQL

SQL for querying

Via inetgration in SQL Server

APIs and other access methods

JDBC
RESTful HTTP/JSON API

.NET Client API
Proprietary client DLL
WinRT client

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

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C
C#
C++
VB.Net

Go
Java
Python
Ruby

JavaScript

Server-side scripts

Stored procedures

yes

via applications

View functions in JavaScript

Triggers

no, except callback-events from server when changes happened

yes

via applications

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Proprietary Sharding system

Sharding

based on Cassandra

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

selectable replication factor

based on Cassandra

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Windows-Profile

Can implement custom security layer via applications

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More resources

DB-Engines blog posts

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