DBMS > Derby vs. IRONdb vs. Kinetica vs. ReductStore

System Properties Comparison Derby vs. IRONdb vs. Kinetica vs. ReductStore

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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

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

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

Fully vectorized database across both GPUs and CPUs

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	4.60
Rank	#70	Overall
	#38	Relational DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Website

db.apache.org/derby

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

www.kinetica.com

github.com/reductstore
www.reduct.store

Technical documentation

db.apache.org/derby/manuals/index.html

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

docs.kinetica.com

www.reduct.store/docs

Developer

Apache Software Foundation

Circonus LLC.

Kinetica

ReductStore LLC

Initial release

1997

2017

2012

2023

Current release

10.17.1.0, November 2023

V0.10.20, January 2018

7.1, August 2021

1.9, March 2024

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

Business Source License 1.1

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

Java

C and C++

C, C++

C++, Rust

Server operating systems

All OS with a Java VM

Linux

Docker
Linux
macOS
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

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

SQL-like DML and DDL statements

APIs and other access methods

JDBC

HTTP API

JDBC
ODBC
RESTful HTTP API

HTTP API

Supported programming languages

Java

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

C++
Java
JavaScript (Node.js)
Python

C++
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

Java Stored Procedures

yes, in Lua

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

none

Automatic, metric affinity per node

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

configurable replication factor, datacenter aware

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

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

GPU vRAM or System RAM

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

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