DBMS > Apache IoTDB vs. Kingbase vs. PouchDB vs. TimesTen

System Properties Comparison Apache IoTDB vs. Kingbase vs. PouchDB vs. TimesTen

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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

An enterprise-class RDBMS compatible with PostgreSQL and Oracle and widely used in China.

JavaScript DBMS with an API inspired by CouchDB

In-Memory RDBMS compatible to Oracle

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.45
Rank	#262	Overall
	#123	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

iotdb.apache.org

www.kingbase.com.cn

pouchdb.com

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

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

pouchdb.com/guides

docs.oracle.com/database/timesten-18.1

Developer

Apache Software Foundation

BeiJing KINGBASE Information technologies inc.

Apache Software Foundation

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2018

1999

2012

1998

Current release

1.1.0, April 2023

V8.0, August 2021

7.1.1, June 2019

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

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

C and Java

JavaScript

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Windows

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

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

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.

yes

Secondary indexes

yes

via views

yes

SQL

Support of SQL

SQL-like query language

Standard with numerous extensions

yes

APIs and other access methods

JDBC
Native API

ADO.NET
gokb
JDBC
kdbndp
ODBC
PDI
PDO
Pro*C
psycopg2
QT

HTTP REST

only for PouchDB Server
JavaScript API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

JavaScript

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

user defined functions

View functions in JavaScript

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

with a proxy-based framework, named couchdb-lounge

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

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

Multi-source replication
Source-replica replication

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

Immediate Consistency or Eventual Consistency depending on configuration

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

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

yes

by means of logfiles and checkpoints

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
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