DBMS > Apache IoTDB vs. H2 vs. MariaDB vs. SWC-DB vs. TimescaleDB

System Properties Comparison Apache IoTDB vs. H2 vs. MariaDB vs. SWC-DB vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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

MySQL application compatible open source RDBMS, enhanced with high availability, security, interoperability and performance capabilities. MariaDB ColumnStore provides a column-oriented storage engine and MariaDB Xpand supports distributed SQL.

A high performance, scalable Wide Column DBMS

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

Primary database model

Secondary database models

Spatial DBMS

Document store
Graph DBMS

with OQGraph storage engine
Spatial DBMS

Time Series DBMS

Relational DBMS

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

Score	1.19
Rank	#176	Overall
	#15	Time Series DBMS

Score	8.22
Rank	#50	Overall
	#32	Relational DBMS

Score	93.81
Rank	#13	Overall
	#9	Relational DBMS

Score	0.01
Rank	#387	Overall
	#13	Wide column stores

Score	4.87
Rank	#74	Overall
	#4	Time Series DBMS

Website

iotdb.apache.org

www.h2database.com

mariadb.com

Site of MariaDB Corporation
mariadb.org

Site of MariaDB Foundation

github.com/kashirin-alex/swc-db
www.swcdb.org

www.timescale.com

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

www.h2database.com/html/main.html

mariadb.com/kb/en/library

docs.timescale.com

Developer

Apache Software Foundation

Thomas Mueller

MariaDB Corporation Ab (MariaDB Enterprise),
MariaDB Foundation (community MariaDB Server)

The lead developer Monty Widenius is the original author of MySQL

Alex Kashirin

Timescale

Initial release

2018

2005

2009

Fork of MySQL, which was first released in 1995

2020

2017

Current release

1.1.0, April 2023

2.2.220, July 2023

11.3.2, February 2024

0.5, April 2021

2.13.0, November 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

GPL version 2, commercial enterprise subscription available

Open Source

GPL V3

Open Source

Apache 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

Java

C and C++

C++

Server operating systems

All OS with a Java VM (>= 1.8)

All OS with a Java VM

FreeBSD
Linux
Solaris
Windows

ColumnStore storage engine not available on Windows

Linux

Linux
OS X
Windows

Data scheme

yes

Dynamic columns are supported

schema-free

yes

Typing

predefined data types such as float or date

yes

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

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC
Native API

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Proprietary protocol
Thrift

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

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Java

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

C++

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

Java Stored Procedures and User-Defined Functions

yes

PL/SQL compatibility added with version 10.3

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

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

none

several options for horizontal partitioning and Sharding

Sharding

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

With clustering: 2 database servers on different computers operate on identical copies of a database

Multi-source replication
Source-replica replication

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

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

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

not for in-memory storage engine

yes

In-memory capabilities

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

yes

with MEMORY storage engine

User concepts

Access control

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

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