DBMS > Apache IoTDB vs. HyperSQL vs. RavenDB

System Properties Comparison Apache IoTDB vs. HyperSQL vs. RavenDB

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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

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Time Series DBMS

Relational DBMS

Document store

Secondary database models

Graph DBMS
Spatial 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	3.01
Rank	#101	Overall
	#17	Document stores

Website

iotdb.apache.org

hsqldb.org

ravendb.net

Technical documentation

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

hsqldb.org/web/hsqlDocsFrame.html

ravendb.net/docs

Developer

Apache Software Foundation

Hibernating Rhinos

Initial release

2018

2001

2010

Current release

1.1.0, April 2023

2.7.2, June 2023

5.4, July 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

based on BSD license

Open Source

AGPL version 3, commercial license available

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
macOS
Raspberry Pi
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

SQL-like query language (RQL)

APIs and other access methods

JDBC
Native API

HTTP API

JDBC via HTTP
JDBC
ODBC

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

All languages supporting JDBC/ODBC
Java

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Sharding

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

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID, Cluster-wide transaction available

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

Authorization levels configured per client per database

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