DBMS > FatDB vs. Heroic vs. Newts vs. Percona Server for MongoDB vs. Quasardb

System Properties Comparison FatDB vs. Heroic vs. Newts vs. Percona Server for MongoDB vs. Quasardb

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.

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

Time Series DBMS based on Cassandra

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

Distributed, high-performance timeseries database

Primary database model

Document store
Key-value store

Time Series DBMS

Document store

Time Series DBMS

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	0.60
Rank	#246	Overall
	#39	Document stores

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Website

github.com/spotify/heroic

opennms.github.io/newts

www.percona.com/mongodb/software/percona-server-for-mongodb

quasar.ai

Technical documentation

spotify.github.io/heroic

github.com/OpenNMS/newts/wiki

docs.percona.com/percona-distribution-for-mongodb

doc.quasar.ai/master

Developer

FatCloud

Spotify

OpenNMS Group

Percona

quasardb

Initial release

2012

2014

2015

2009

Current release

3.4.10-2.10, November 2017

3.14.1, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

GPL Version 2

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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

C++

Server operating systems

Windows

Linux
OS X
Windows

Linux

BSD
Linux
OS X
Windows

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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 Elasticsearch

yes

with tags

SQL

Support of SQL

Via inetgration in SQL Server

SQL-like query language

APIs and other access methods

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

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

HTTP REST
Java API

proprietary protocol using JSON

HTTP API

Supported programming languages

Java

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

yes

via applications

JavaScript

Triggers

yes

via applications

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

selectable replication factor

based on Cassandra

Source-replica replication

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

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 LevelDB

In-memory capabilities

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

yes

via In-Memory Engine

yes

Transient mode

User concepts

Access control

Can implement custom security layer via applications

Access rights for users and roles

Cryptographically strong user authentication and audit trail

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