DBMS > Faircom EDGE vs. Hawkular Metrics vs. mSQL vs. Riak KV vs. ToroDB

System Properties Comparison Faircom EDGE vs. Hawkular Metrics vs. mSQL vs. Riak KV vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

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

mSQL (Mini SQL) is a simple and lightweight RDBMS

Distributed, fault tolerant key-value store

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Document store

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	#379	Overall
	#40	Time Series DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

www.faircom.com/products/faircom-edge

www.hawkular.org

hughestech.com.au/products/msql

github.com/torodb/server

Technical documentation

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

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

www.tiot.jp/riak-docs/riak/kv/latest

Developer

FairCom Corporation

Community supported by Red Hat

Hughes Technologies

OpenSource, formerly Basho Technologies

8Kdata

Initial release

1979

2014

1994

2009

2016

Current release

V3, October 2020

4.4, October 2021

3.2.0, December 2022

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache 2.0

commercial

free licenses can be provided

Open Source

Apache version 2, commercial enterprise edition

Open Source

AGPL-V3

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

Erlang

Java

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

Linux
OS X
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
OS X

All OS with a Java 7 VM

Data scheme

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

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

yes

string, integer, double, boolean, date, object_id

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

restricted

SQL

Support of SQL

yes

ANSI SQL queries

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

APIs and other access methods

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

HTTP REST

JDBC
ODBC

HTTP API
Native Erlang Interface

Supported programming languages

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

Go
Java
Python
Ruby

C
C++
Delphi
Java
Perl
PHP
Tcl

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

Erlang

Triggers

yes

via Hawkular Alerting

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

Sharding

based on Cassandra

none

Sharding

no "single point of failure"

Sharding

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

none

selectable replication factor

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

none

Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

links between data sets can be stored

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

In-memory capabilities

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

yes

User concepts

Access control

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

yes, using Riak Security

Access rights for users and roles

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