DBMS > Apache IoTDB vs. Ehcache vs. H2 vs. Ultipa

System Properties Comparison Apache IoTDB vs. Ehcache vs. H2 vs. Ultipa

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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

A widely adopted Java cache with tiered storage options

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

High performance Graph DBMS supporting HTAP high availability cluster deployment

Primary database model

Secondary database models

Spatial DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	4.64
Rank	#68	Overall
	#8	Key-value stores

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	0.19
Rank	#330	Overall
	#30	Graph DBMS

Website

Technical documentation

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

www.ehcache.org/documentation

www.h2database.com/html/main.html

www.ultipa.com/document

Developer

Apache Software Foundation

Terracotta Inc, owned by Software AG

Thomas Mueller

Ultipa

Initial release

2018

2009

2005

2019

Current release

1.1.0, April 2023

3.10.0, March 2022

2.2.220, July 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available

Open Source

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

commercial

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

Data scheme

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

JCache

JDBC
ODBC

RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Java

C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Java Stored Procedures and User-Defined Functions

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

by using Terracotta Server

none

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

yes

by using Terracotta Server

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

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

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

supports JTA and can work as an XA resource

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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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