DBMS > Apache IoTDB vs. H2GIS vs. Oracle Coherence

System Properties Comparison Apache IoTDB vs. H2GIS vs. Oracle Coherence

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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

Spatial extension of H2

Oracles in-memory data grid solution

Primary database model

Time Series DBMS

Spatial DBMS

Key-value store

Secondary database models

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	0.08
Rank	#368	Overall
	#7	Spatial DBMS

Score	1.97
Rank	#129	Overall
	#22	Key-value stores

Website

iotdb.apache.org

www.h2gis.org

www.oracle.com/java/coherence

Technical documentation

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

www.h2gis.org/docs/home

docs.oracle.com/en/middleware/standalone/coherence

Developer

Apache Software Foundation

CNRS

Oracle

Initial release

2018

2013

2007

Current release

1.1.0, April 2023

14.1, August 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

LGPL 3.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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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

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
JPA
RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Java

.Net
C++
Java

Server-side scripts

Stored procedures

yes

based on H2

Triggers

yes

Live Events

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

yes

based on H2

yes, with selectable consistency level

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
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

optionally

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

based on H2

authentification to access the cache via certificates or http basic authentication

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