DBMS > IRONdb vs. Riak TS vs. TimescaleDB vs. Tkrzw

System Properties Comparison IRONdb vs. Riak TS vs. TimescaleDB vs. Tkrzw

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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

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

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Time Series DBMS

Key-value store

Secondary database models

Relational DBMS

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

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

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

www.timescale.com

dbmx.net/tkrzw

Technical documentation

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

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

docs.timescale.com

Developer

Circonus LLC.

Open Source, formerly Basho Technologies

Timescale

Mikio Hirabayashi

Initial release

2017

2015

2017

2020

Current release

V0.10.20, January 2018

3.0.0, September 2022

2.15.0, May 2024

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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 and C++

Erlang

C++

Server operating systems

Linux

Linux
OS X

Linux
OS X
Windows

Linux
macOS

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

restricted

yes

SQL

Support of SQL

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

yes, limited

yes

full PostgreSQL SQL syntax

APIs and other access methods

HTTP API

HTTP API
Native Erlang Interface

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

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

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes, in Lua

Erlang

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

Sharding

yes, across time and space (hash partitioning) attributes

none

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

selectable replication factor

Source-replica replication with hot standby and reads on replicas

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency per node, eventual consistency across nodes

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between datasets can be stored

yes

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

In-memory capabilities

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

yes

using specific database classes

User concepts

Access control

fine grained access rights according to SQL-standard

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