DBMS > Apache IoTDB vs. Netezza vs. OpenTSDB vs. Realm vs. VoltDB

System Properties Comparison Apache IoTDB vs. Netezza vs. OpenTSDB vs. Realm vs. VoltDB

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

Data warehouse and analytics appliance part of IBM PureSystems

Scalable Time Series DBMS based on HBase

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	1.68
Rank	#146	Overall
	#12	Time Series DBMS

Score	7.60
Rank	#52	Overall
	#9	Document stores

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

iotdb.apache.org

www.ibm.com/products/netezza

opentsdb.net

realm.io

www.voltdb.com

Technical documentation

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

opentsdb.net/docs/build/html/index.html

realm.io/docs

docs.voltdb.com

Developer

Apache Software Foundation

IBM

currently maintained by Yahoo and other contributors

Realm, acquired by MongoDB in May 2019

VoltDB Inc.

Initial release

2018

2000

2011

2014

2010

Current release

1.1.0, April 2023

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

LGPL

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Java, C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

included in appliance

Linux
Windows

Android
Backend: server-less
iOS
Windows

Linux
OS X

for development

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

numeric data for metrics, strings for tags

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

only a subset of SQL 99

APIs and other access methods

JDBC
Native API

JDBC
ODBC
OLE DB

HTTP API
Telnet API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C++
Fortran
Java
Lua
Perl
Python
R

Erlang
Go
Java
Python
R
Ruby

.Net
Java

with Android only
Objective-C
React Native
Swift

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

runs within the applications so server-side scripts are unnecessary

Java

Triggers

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

based on HBase

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

Source-replica replication

selectable replication factor

based on HBase

none

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

based on HBase

Immediate Consistency

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

In-memory capabilities

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

yes

In-Memory realm

User concepts

Access control

yes

Users with fine-grained authorization concept

yes

Users and roles with access to stored procedures

More information provided by the system vendor

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More resources

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