DBMS > Apache IoTDB vs. BaseX vs. Kinetica vs. NSDb

System Properties Comparison Apache IoTDB vs. BaseX vs. Kinetica vs. NSDb

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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

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

Fully vectorized database across both GPUs and CPUs

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	1.56
Rank	#141	Overall
	#4	Native XML DBMS

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Website

Technical documentation

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

docs.basex.org

docs.kinetica.com

nsdb.io/Architecture

Developer

Apache Software Foundation

BaseX GmbH

Kinetica

Initial release

2018

2007

2012

2017

Current release

1.1.0, April 2023

11.2, August 2024

7.1, August 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD license

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, C++

Java, Scala

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
OS X
Windows

Linux

Linux
macOS

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

XQuery supports types

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

JDBC
Native API

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

JDBC
ODBC
RESTful HTTP API

gRPC
HTTP REST
WebSocket

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

C++
Java
JavaScript (Node.js)
Python

Java
Scala

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

via events

yes

triggers when inserted values for one or more columns fall within a specified range

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

none

Source-replica replication

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 or Eventual Consistency depending on configuration

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

In-memory capabilities

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

yes

GPU vRAM or System RAM

User concepts

Access control

yes

Users with fine-grained authorization concept on 4 levels

Access rights for users and roles on table level

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