DBMS > Faircom EDGE vs. Newts vs. Oracle Berkeley DB vs. Quasardb

System Properties Comparison Faircom EDGE vs. Newts vs. Oracle Berkeley DB vs. Quasardb

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Editorial information provided by DB-Engines

Name

Description

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

Time Series DBMS based on Cassandra

Widely used in-process key-value store

Distributed, high-performance timeseries database

Primary database model

Key-value store
Relational DBMS

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Time Series DBMS

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

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

www.faircom.com/products/faircom-edge

opennms.github.io/newts

www.oracle.com/database/technologies/related/berkeleydb.html

quasar.ai

Technical documentation

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

github.com/OpenNMS/newts/wiki

docs.oracle.com/cd/E17076_05/html/index.html

doc.quasar.ai/master

Developer

FairCom Corporation

OpenNMS Group

Oracle

originally developed by Sleepycat, which was acquired by Oracle

quasardb

Initial release

1979

2014

1994

2009

Current release

V3, October 2020

18.1.40, May 2020

3.14.1, January 2024

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache 2.0

Open Source

commercial license available

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

ANSI C, C++

Java

C, Java, C++ (depending on the Berkeley DB edition)

C++

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

Linux
OS X
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

BSD
Linux
OS X
Windows

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

schema-free

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

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.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

with tags

SQL

Support of SQL

yes

ANSI SQL queries

yes

SQL interfaced based on SQLite is available

SQL-like query language

APIs and other access methods

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

HTTP REST
Java API

HTTP API

Supported programming languages

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

Java

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

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

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

Sharding

based on Cassandra

none

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

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

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

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

Transient mode

User concepts

Access control

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

Cryptographically strong user authentication and audit trail

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