DBMS > IRONdb vs. MySQL vs. Splice Machine vs. Tkrzw

System Properties Comparison IRONdb vs. MySQL vs. Splice Machine 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

Widely used open source RDBMS

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

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

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Key-value store

Secondary database models

Document store
Spatial DBMS

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

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

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

www.mysql.com

splicemachine.com

dbmx.net/tkrzw

Technical documentation

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

dev.mysql.com/doc

splicemachine.com/how-it-works

Developer

Circonus LLC.

Oracle

since 2010, originally MySQL AB, then Sun

Splice Machine

Mikio Hirabayashi

Initial release

2017

1995

2014

2020

Current release

V0.10.20, January 2018

8.4.0, April 2024

3.1, March 2021

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

AGPL 3.0, commercial license available

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.

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

C and C++

Java

C++

Server operating systems

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Linux
macOS

Data scheme

schema-free

yes

schema-free

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

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

yes

with proprietary extensions

yes

APIs and other access methods

HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes, in Lua

yes

proprietary syntax

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark 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

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes, multi-version concurrency control (MVCC)

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

Users with fine-grained authorization concept

no user groups or roles

Access rights for users, groups and roles according to SQL-standard

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