DBMS > IRONdb vs. MaxDB vs. PlanetScale vs. Spark SQL vs. SQLite

System Properties Comparison IRONdb vs. MaxDB vs. PlanetScale vs. Spark SQL vs. SQLite

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

A robust and reliable RDBMS optimized to run all major SAP solutions

Scalable, distributed, serverless MySQL database platform built on top of Vitess

Spark SQL is a component on top of 'Spark Core' for structured data processing

Widely used embeddable, in-process RDBMS

Primary database model

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	2.32
Rank	#112	Overall
	#55	Relational DBMS

Score	1.59
Rank	#151	Overall
	#70	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

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

Technical documentation

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

maxdb.sap.com/documentation

planetscale.com/docs

spark.apache.org/docs/latest/sql-programming-guide.html

www.sqlite.org/docs.html

Developer

Circonus LLC.

SAP, acquired from Software AG (Adabas-D) in 1997

PlanetScale

Apache Software Foundation

Dwayne Richard Hipp

Initial release

2017

1984

2020

2014

2000

Current release

V0.10.20, January 2018

7.9.10.12, February 2024

3.5.0 ( 2.13), September 2023

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

commercial

Limited community edition free

commercial

Open Source

Apache 2.0

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C and C++

C++

Scala

Server operating systems

Linux

AIX
HP-UX
Linux
Solaris
Windows

Docker
Linux
macOS

Linux
OS X
Windows

server-less

Data scheme

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

not rigid because of 'dynamic typing' concept.

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

SQL

Support of SQL

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

yes

with proprietary extensions

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

APIs and other access methods

HTTP API

ADO.NET
JDBC
ODBC
WebDAV

ADO.NET
JDBC
MySQL protocol
ODBC

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

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

.Net
C#
Java
Perl
PHP
Python

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

Java
Python
R
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes, in Lua

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

none

Sharding

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

Source-replica replication

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

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

ACID at shard level

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

via file-system locks

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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