DBMS > Apache IoTDB vs. IBM Db2 vs. Kinetica vs. SpaceTime vs. Teradata

System Properties Comparison Apache IoTDB vs. IBM Db2 vs. Kinetica vs. SpaceTime vs. Teradata

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

Common in IBM host environments, 2 different versions for host and Windows/Linux

Fully vectorized database across both GPUs and CPUs

SpaceTime is a spatio-temporal DBMS with a focus on performance.

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Time Series DBMS

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

Spatial DBMS

Relational DBMS

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Spatial DBMS
Time Series DBMS

Relational DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

iotdb.apache.org

www.ibm.com/products/db2

www.kinetica.com

www.mireo.com/spacetime

www.teradata.com

Technical documentation

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

www.ibm.com/docs/en/db2

docs.kinetica.com

docs.teradata.com

Developer

Apache Software Foundation

IBM

Kinetica

Mireo

Teradata

Initial release

2018

1983

host version

2012

2020

1984

Current release

1.1.0, April 2023

12.1, October 2016

7.1, August 2021

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free version is available

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 C++

C, C++

C++

Server operating systems

All OS with a Java VM (>= 1.8)

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux

hosted
Linux

Data scheme

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

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

SQL-like query language

yes

SQL-like DML and DDL statements

A subset of ANSI SQL is implemented

yes

SQL 2016 + extensions

APIs and other access methods

JDBC
Native API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

C++
Java
JavaScript (Node.js)
Python

C#
C++
Python

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

yes

user defined functions

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

only with Windows/Unix/Linux Version

Sharding

Fixed-grid hypercubes

Sharding

Hashing

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

with separate tools (MQ, InfoSphere)

Source-replica replication

Real-time block device replication (DRBD)

Multi-source replication
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

Immediate 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

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

GPU vRAM or System RAM

yes

User concepts

Access control

yes

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

yes

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

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