DBMS > Apache IoTDB vs. gStore vs. IBM Cloudant vs. JaguarDB vs. Riak TS

System Properties Comparison Apache IoTDB vs. gStore vs. IBM Cloudant vs. JaguarDB vs. Riak TS

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 native Graph DBMS to store and maintain very large RDF datasets.

Database as a Service offering based on Apache CouchDB

Performant, highly scalable DBMS for AI and IoT applications

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

Primary database model

Time Series DBMS

Graph DBMS
RDF store

Document store

Key-value store
Vector DBMS

Time Series DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.14
Rank	#342	Overall
	#34	Graph DBMS
	#16	RDF stores

Score	2.75
Rank	#104	Overall
	#19	Document stores

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#15	Vector DBMS

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Website

iotdb.apache.org

en.gstore.cn

www.ibm.com/products/cloudant

www.jaguardb.com

Technical documentation

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

en.gstore.cn/#/enDocs

cloud.ibm.com/docs/Cloudant

www.jaguardb.com/support.html

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

Developer

Apache Software Foundation

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

DataJaguar, Inc.

Open Source, formerly Basho Technologies

Initial release

2018

2016

2010

2015

Current release

1.1.0, April 2023

1.2, November 2023

3.3 July 2023

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD

commercial

Open Source

GPL V3.0

Open Source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Erlang

C++

the server part. Clients available in other languages

Erlang

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

hosted

Linux

Linux
OS X

Data scheme

yes

schema-free and OWL/RDFS-schema support

schema-free

yes

schema-free

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

SQL

Support of SQL

SQL-like query language

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes, limited

APIs and other access methods

JDBC
Native API

HTTP API
SPARQL 1.1

RESTful HTTP/JSON API

JDBC
ODBC

HTTP API
Native Erlang Interface

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C++
Java
JavaScript (Node.js)
PHP
Python

C#
Java
JavaScript
Objective-C
PHP
Ruby

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

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

View functions (Map-Reduce) in JavaScript

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

Multi-source replication
Source-replica replication

Multi-source replication

selectable replication factor

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

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

yes

atomic operations within a document possible

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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

User concepts

Access control

yes

Users, roles and permissions, Role-Based Access Control (RBAC) supported

Access rights for users can be defined per database

rights management via user accounts

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