DBMS > Apache IoTDB vs. HyperSQL vs. LeanXcale vs. Newts

System Properties Comparison Apache IoTDB vs. HyperSQL vs. LeanXcale vs. Newts

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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

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Time Series DBMS based on Cassandra

Primary database model

Time Series DBMS

Relational DBMS

Key-value store
Relational DBMS

Time Series DBMS

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

Score	1.19
Rank	#176	Overall
	#15	Time Series DBMS

Score	3.90
Rank	#85	Overall
	#46	Relational DBMS

Score	0.35
Rank	#283	Overall
	#41	Key-value stores
	#128	Relational DBMS

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Website

iotdb.apache.org

hsqldb.org

www.leanxcale.com

opennms.github.io/newts

Technical documentation

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

hsqldb.org/web/hsqlDocsFrame.html

github.com/OpenNMS/newts/wiki

Developer

Apache Software Foundation

LeanXcale

OpenNMS Group

Initial release

2018

2001

2015

2014

Current release

1.1.0, April 2023

2.7.2, June 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

based on BSD license

commercial

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

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

Linux
OS X
Windows

Data scheme

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

through Apache Derby

APIs and other access methods

JDBC
Native API

HTTP API

JDBC via HTTP
JDBC
ODBC

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

HTTP REST
Java API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

All languages supporting JDBC/ODBC
Java

C
Java
Scala

Java

Server-side scripts

Stored procedures

yes

Java, SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

Sharding

based on Cassandra

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

selectable replication factor

based on Cassandra

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

User concepts

Access control

yes

fine grained access rights according to SQL-standard

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