DBMS > Faircom DB vs. GeoSpock vs. Hawkular Metrics vs. KairosDB vs. Riak KV

System Properties Comparison Faircom DB vs. GeoSpock vs. Hawkular Metrics vs. KairosDB vs. Riak KV

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Spatial and temporal data processing engine for extreme data scale

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

Distributed Time Series DBMS based on Cassandra or H2

Distributed, fault tolerant key-value store

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

Key-value store

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

Secondary database models

Time Series DBMS

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

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	0.62
Rank	#239	Overall
	#20	Time Series DBMS

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

Website

www.faircom.com/products/faircom-db

geospock.com

www.hawkular.org

github.com/kairosdb/kairosdb

Technical documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

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

kairosdb.github.io

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

Developer

FairCom Corporation

GeoSpock

Community supported by Red Hat

OpenSource, formerly Basho Technologies

Initial release

1979

2014

2013

2009

Current release

V12, November 2020

2.0, September 2019

1.2.2, November 2018

3.2.0, December 2022

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Apache version 2, commercial enterprise edition

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

ANSI C, C++

Java, Javascript

Java

Erlang

Server operating systems

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

hosted

Linux
OS X
Windows

Linux
OS X

Data scheme

schema free, schema optional, schema required, partial schema,

yes

schema-free

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

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

temporal, categorical

restricted

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

ANSI SQL for query only (using Presto)

APIs and other access methods

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

JDBC

HTTP REST

Graphite protocol
HTTP REST
Telnet API

HTTP API
Native Erlang Interface

Supported programming languages

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

Go
Java
Python
Ruby

Java
JavaScript

Node.js
PHP
Python

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, horizontal partitioning, sharding

Customizable business rules for table partitioning

Automatic sharding

Sharding

based on Cassandra

Sharding

based on Cassandra

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

selectable replication factor

based on Cassandra

selectable replication factor

based on Cassandra

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

tunable from ACID to Eventually Consistent

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Yes, tunable from durable to delayed durability to in-memory

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 access rights according to SQL-standard with additional protections for files

Access rights for users can be defined per table

simple password-based access control

yes, using Riak Security

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