DBMS > IRONdb vs. OpenMLDB vs. Quasardb vs. STSdb

System Properties Comparison IRONdb vs. OpenMLDB vs. Quasardb vs. STSdb

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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

An open-source machine learning database that provides a feature platform for training and inference

Distributed, high-performance timeseries database

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

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	0.02
Rank	#367	Overall
	#37	Time Series DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

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

openmldb.ai

quasar.ai

github.com/STSSoft/STSdb4

Technical documentation

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

openmldb.ai/docs/zh/main

doc.quasar.ai/master

Developer

Circonus LLC.

4 Paradigm Inc.

quasardb

STS Soft SC

Initial release

2017

2020

2009

2011

Current release

V0.10.20, January 2018

2024-2 February 2024

3.14.1, January 2024

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

GPLv2, commercial license available

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++

C++, Java, Scala

C++

Server operating systems

Linux

BSD
Linux
OS X
Windows

Windows

Data scheme

schema-free

Fixed schema

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

integer and binary

yes

primitive types and user defined types (classes)

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

with tags

SQL

Support of SQL

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

yes

SQL-like query language

APIs and other access methods

HTTP API

JDBC
SQLAlchemy

HTTP API

.NET Client API

Supported programming languages

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

C++
Go
Java
Python
Scala

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

C#
Java

Server-side scripts

Stored procedures

yes, in Lua

Triggers

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

horizontal partitioning

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

Source-replica replication

Source-replica replication with selectable replication factor

none

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 per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

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

by using LevelDB

yes

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 access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

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