DBMS > Apache IoTDB vs. H2 vs. IBM Db2 warehouse vs. JSqlDb vs. PouchDB

System Properties Comparison Apache IoTDB vs. H2 vs. IBM Db2 warehouse vs. JSqlDb vs. PouchDB

Editorial information provided by DB-Engines

Name

JSqlDB seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

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

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

Cloud-based data warehousing service

JavaScript Query Language Database, stores JavaScript objects and primitives

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Time Series DBMS

Relational DBMS

Document store
Object oriented DBMS

Document store

Secondary database models

Spatial DBMS

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	7.57
Rank	#47	Overall
	#30	Relational DBMS

Score	1.26
Rank	#162	Overall
	#73	Relational DBMS

Score	2.18
Rank	#114	Overall
	#21	Document stores

Website

iotdb.apache.org

www.h2database.com

www.ibm.com/products/db2/warehouse

jsqldb.org (offline)

pouchdb.com

Technical documentation

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

www.h2database.com/html/main.html

pouchdb.com/guides

Developer

Apache Software Foundation

Thomas Mueller

IBM

Konrad von Backstrom

Apache Software Foundation

Initial release

2018

2005

2014

2018

2012

Current release

1.1.0, April 2023

2.2.220, July 2023

0.8, December 2018

7.1.1, June 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

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

commercial

Open Source

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

JavaScript

Server operating systems

All OS with a Java VM (>= 1.8)

All OS with a Java VM

hosted

Linux
macOS
Windows

server-less, requires a JavaScript environment (browser, Node.js)

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.

Import/export of XML data possible

Secondary indexes

yes

via views

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

JDBC
ODBC

.NET Client API
JDBC
ODBC
OLE DB

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Java

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

JavaScript

Server-side scripts

Stored procedures

yes

Java Stored Procedures and User-Defined Functions

PL/SQL, SQL PL

functions in JavaScript

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

Sharding

none

Sharding

with a proxy-based framework, named couchdb-lounge

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

none

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

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

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

using RocksDB

yes

by using IndexedDB, WebSQL or LevelDB as backend

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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More resources

DB-Engines blog posts

New kids on the block: database management systems implemented in JavaScript
1 December 2014, Matthias Gelbmann

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