DBMS > Apache IoTDB vs. BigObject vs. IBM Cloudant vs. PostgreSQL

System Properties Comparison Apache IoTDB vs. BigObject vs. IBM Cloudant vs. PostgreSQL

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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

Analytic DBMS for real-time computations and queries

Database as a Service offering based on Apache CouchDB

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Primary database model

Time Series DBMS

Relational DBMS

a hierachical model (tree) can be imposed

Document store

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Secondary database models

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	2.75
Rank	#104	Overall
	#19	Document stores

Score	636.25
Rank	#4	Overall
	#4	Relational DBMS

Website

iotdb.apache.org

bigobject.io

www.ibm.com/products/cloudant

www.postgresql.org

Technical documentation

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

docs.bigobject.io

cloud.ibm.com/docs/Cloudant

www.postgresql.org/docs

Developer

Apache Software Foundation

BigObject, Inc.

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

PostgreSQL Global Development Group

www.postgresql.org/developer

Initial release

2018

2015

2010

1989

1989: Postgres, 1996: PostgreSQL

Current release

1.1.0, April 2023

16.3, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community edition available

commercial

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

Erlang

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
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.

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

yes

standard with numerous extensions

APIs and other access methods

JDBC
Native API

fluentd
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C#
Java
JavaScript
Objective-C
PHP
Ruby

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

yes

Lua

View functions (Map-Reduce) in JavaScript

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

Sharding

partitioning by range, list and (since PostgreSQL 11) by hash

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

none

Multi-source replication
Source-replica replication

Source-replica replication

other methods possible by using 3rd party extensions

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

none

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

atomic operations within a document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

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