DBMS > Apache IoTDB vs. Oracle NoSQL vs. RavenDB vs. Riak TS

System Properties Comparison Apache IoTDB vs. Oracle NoSQL vs. RavenDB vs. Riak TS

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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

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Open Source Operational and Transactional Enterprise NoSQL Document Database

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

Primary database model

Time Series DBMS

Document store
Key-value store
Relational DBMS

Document store

Time Series DBMS

Secondary database models

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	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

iotdb.apache.org

www.oracle.com/database/nosql/technologies/nosql

ravendb.net

Technical documentation

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

docs.oracle.com/en/database/other-databases/nosql-database/index.html

ravendb.net/docs

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

Developer

Apache Software Foundation

Oracle

Hibernating Rhinos

Open Source, formerly Basho Technologies

Initial release

2018

2011

2010

2015

Current release

1.1.0, April 2023

23.3, December 2023

5.4, July 2022

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

AGPL version 3, commercial license available

Open Source

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

Erlang

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Solaris SPARC/x86

Linux
macOS
Raspberry Pi
Windows

Linux
OS X

Data scheme

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

schema-free

Typing

predefined data types such as float or date

yes

optional

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

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

SQL-like query language (RQL)

yes, limited

APIs and other access methods

JDBC
Native API

RESTful HTTP API

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C#
Go
Java
JavaScript (Node.js)
Python

.Net
C#
F#
Go
Java
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

Server-side scripts

Stored procedures

yes

Erlang

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

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

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

Multi-source replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency
Immediate Consistency

depending on configuration

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Eventual Consistency

Foreign keys

Referential integrity

links between datasets can be stored

Transaction concepts

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

configurable

ACID within a storage node (=shard)

ACID, Cluster-wide transaction available

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

off heap cache

User concepts

Access control

yes

Access rights for users and roles

Authorization levels configured per client per database

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