DBMS > Cachelot.io vs. IRONdb vs. RRDtool vs. SwayDB

System Properties Comparison Cachelot.io vs. IRONdb vs. RRDtool vs. SwayDB

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Editorial information provided by DB-Engines

Name

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

Description

In-memory caching system

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Key-value store

Time Series DBMS

Key-value store

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

Score	0.00
Rank	#385	Overall
	#61	Key-value stores

Score	1.70
Rank	#136	Overall
	#11	Time Series DBMS

Score	0.00
Rank	#384	Overall
	#60	Key-value stores

Website

cachelot.io

www.circonus.com/solutions/time-series-database/

oss.oetiker.ch/rrdtool

swaydb.simer.au

Technical documentation

docs.circonus.com/irondb/category/getting-started

oss.oetiker.ch/rrdtool/doc

Developer

Circonus LLC.

Tobias Oetiker

Simer Plaha

Initial release

2015

2017

1999

2018

Current release

V0.10.20, January 2018

1.8.0, 2022

License

Commercial or Open Source

Open Source

Simplified BSD License

commercial

Open Source

GPL V2 and FLOSS

Open Source

GNU Affero GPL V3.0

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

C++

C and C++

Implementations in Java (e.g. RRD4J) and C# available

Scala

Server operating systems

FreeBSD
Linux
OS X

Linux

HP-UX
Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

Numeric data only

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.

Exporting into and restoring from XML files possible

Secondary indexes

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

APIs and other access methods

Memcached protocol

HTTP API

in-process shared library
Pipes

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

Java
Kotlin
Scala

Server-side scripts

Stored procedures

yes, in Lua

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Automatic, metric affinity per node

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

configurable replication factor, datacenter aware

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate consistency per node, eventual consistency across nodes

none

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

by using the rrdcached daemon

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

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

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