DBMS > Apache IoTDB vs. HyperSQL vs. Ingres vs. Transbase

System Properties Comparison Apache IoTDB vs. HyperSQL vs. Ingres vs. Transbase

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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

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Well established RDBMS

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Time Series DBMS

Relational DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	3.23
Rank	#93	Overall
	#48	Relational DBMS

Score	3.80
Rank	#82	Overall
	#44	Relational DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

iotdb.apache.org

hsqldb.org

www.actian.com/databases/ingres

www.transaction.de/en/products/transbase.html

Technical documentation

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

hsqldb.org/web/hsqlDocsFrame.html

docs.actian.com/ingres

www.transaction.de/en/products/transbase/features.html

Developer

Apache Software Foundation

Actian Corporation

Transaction Software GmbH

Initial release

2018

2001

1974

originally developed at University Berkely in early 1970s

1987

Current release

1.1.0, April 2023

2.7.2, June 2023

11.2, May 2022

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

based on BSD license

commercial

free development license

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++

Server operating systems

All OS with a Java VM (>= 1.8)

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

Typing

predefined data types such as float or date

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.

but tools for importing/exporting data from/to XML-files available

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

HTTP API

JDBC via HTTP
JDBC
ODBC

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

All languages supporting JDBC/ODBC
Java

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

Java, SQL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

horizontal partitioning

Ingres Star to access multiple databases simultaneously

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

none

Ingres Replicator

Source-replica replication

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

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

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

User concepts

Access control

yes

fine grained access rights according to SQL-standard

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