DBMS > Apache IoTDB vs. gStore vs. H2 vs. Microsoft Azure AI Search

System Properties Comparison Apache IoTDB vs. gStore vs. H2 vs. Microsoft Azure AI Search

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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 native Graph DBMS to store and maintain very large RDF datasets.

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

Search-as-a-service for web and mobile app development

Primary database model

Secondary database models

Spatial DBMS

Vector DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.14
Rank	#342	Overall
	#34	Graph DBMS
	#16	RDF stores

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	5.52
Rank	#59	Overall
	#6	Search engines

Website

iotdb.apache.org

en.gstore.cn

www.h2database.com

azure.microsoft.com/en-us/services/search

Technical documentation

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

en.gstore.cn/#/enDocs

www.h2database.com/html/main.html

learn.microsoft.com/en-us/azure/search

Developer

Apache Software Foundation

Thomas Mueller

Microsoft

Initial release

2018

2016

2005

2015

Current release

1.1.0, April 2023

1.2, November 2023

2.2.220, July 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD

Open Source

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

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

All OS with a Java VM

hosted

Data scheme

yes

schema-free and OWL/RDFS-schema support

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

HTTP API
SPARQL 1.1

JDBC
ODBC

RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

Java

C#
Java
JavaScript
Python

Server-side scripts

Stored procedures

yes

Java Stored Procedures and User-Defined Functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

Sharding

Implicit feature of the cloud service

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

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

yes

Implicit feature of the cloud service

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (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

Users, roles and permissions, Role-Based Access Control (RBAC) supported

fine grained access rights according to SQL-standard

yes

using Azure authentication

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