DBMS > Apache IoTDB vs. Bangdb vs. H2 vs. Hazelcast vs. HyperSQL

System Properties Comparison Apache IoTDB vs. Bangdb vs. H2 vs. Hazelcast vs. HyperSQL

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

Converged and high performance database for device data, events, time series, document and graph

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

A widely adopted in-memory data grid

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Primary database model

Time Series DBMS

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Spatial DBMS

Document store

JSON support with IMDG 3.12

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	3.23
Rank	#93	Overall
	#48	Relational DBMS

Website

Technical documentation

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

docs.bangdb.com

www.h2database.com/html/main.html

hazelcast.org/imdg/docs

hsqldb.org/web/hsqlDocsFrame.html

Developer

Apache Software Foundation

Sachin Sinha, BangDB

Thomas Mueller

Hazelcast

Initial release

2018

2012

2005

2008

2001

Current release

1.1.0, April 2023

BangDB 2.0, October 2021

2.2.220, July 2023

5.3.6, November 2023

2.7.2, June 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD 3

Open Source

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

Open Source

Apache Version 2; commercial licenses available

Open Source

based on BSD license

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

C, C++

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Data scheme

yes

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

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.

yes

the object must implement a serialization strategy

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL-like query language

SQL like support with command line tool

yes

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

Proprietary protocol
RESTful HTTP API

JDBC
ODBC

JCache
JPA
Memcached protocol
RESTful HTTP API

HTTP API

JDBC via HTTP
JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C#
C++
Java
Python

Java

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

All languages supporting JDBC/ODBC
Java

Server-side scripts

Stored procedures

yes

Java Stored Procedures and User-Defined Functions

yes

Event Listeners, Executor Services

Java, SQL

Triggers

yes

yes, Notifications (with Streaming only)

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

none

Sharding

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

selectable replication factor, Knob for CAP (enterprise version only)

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

yes

Replicated Map

none

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

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

yes, run db with in-memory only mode

yes

User concepts

Access control

yes

yes (enterprise version only)

fine grained access rights according to SQL-standard

Role-based access control

fine grained access rights according to SQL-standard

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