DBMS > Apache IoTDB vs. HEAVY.AI vs. NuoDB vs. Riak TS vs. TimesTen

System Properties Comparison Apache IoTDB vs. HEAVY.AI vs. NuoDB vs. Riak TS vs. TimesTen

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

NuoDB is a webscale distributed database that supports SQL and ACID transactions

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

In-Memory RDBMS compatible to Oracle

Primary database model

Secondary database models

Spatial DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	0.94
Rank	#197	Overall
	#92	Relational DBMS

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

iotdb.apache.org

github.com/heavyai/heavydb
www.heavy.ai

www.3ds.com/nuodb-distributed-sql-database

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

Technical documentation

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

docs.heavy.ai

doc.nuodb.com

www.tiot.jp/riak-docs/riak/ts/latest

docs.oracle.com/database/timesten-18.1

Developer

Apache Software Foundation

HEAVY.AI, Inc.

Dassault Systèmes

originally NuoDB, Inc.

Open Source, formerly Basho Technologies

Oracle, TimesTen Performance Software, HP

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

Initial release

2018

2016

2013

2015

1998

Current release

1.1.0, April 2023

5.10, January 2022

3.0.0, September 2022

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; enterprise edition available

commercial

limited edition free

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 CUDA

C++

Erlang

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Linux
OS X

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.

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

SQL-like query language

yes

yes, limited

yes

APIs and other access methods

JDBC
Native API

JDBC
ODBC
Thrift
Vega

ADO.NET
JDBC
ODBC

HTTP API
Native Erlang Interface

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

All languages supporting JDBC/ODBC/Thrift
Python

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

Java, SQL

Erlang

PL/SQL

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

Round robin

data is dynamically stored/cached on the nodes where it is read/written

Sharding

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

Multi-source replication

yes

Managed transparently by NuoDB

selectable replication factor

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

links between datasets can be stored

yes

Transaction concepts

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

ACID

tunable commit protocol

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

Durability

Support for making data persistent

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

Temporary table

yes

User concepts

Access control

yes

fine grained access rights according to SQL-standard

Standard SQL roles/ privileges, Administrative Users

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

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