DBMS > Apache IoTDB vs. Microsoft Azure AI Search vs. Oracle vs. Quasardb

System Properties Comparison Apache IoTDB vs. Microsoft Azure AI Search vs. Oracle vs. Quasardb

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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

Search-as-a-service for web and mobile app development

Widely used RDBMS

Distributed, high-performance timeseries database

Primary database model

Secondary database models

Vector DBMS

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	5.52
Rank	#59	Overall
	#6	Search engines

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Website

iotdb.apache.org

azure.microsoft.com/en-us/services/search

www.oracle.com/database

quasar.ai

Technical documentation

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

learn.microsoft.com/en-us/azure/search

docs.oracle.com/en/database

doc.quasar.ai/master

Developer

Apache Software Foundation

Microsoft

Oracle

quasardb

Initial release

2018

2015

1980

2009

Current release

1.1.0, April 2023

23c, September 2023

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

restricted free version is available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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 and C++

C++

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

BSD
Linux
OS X
Windows

Data scheme

yes

Schemaless in JSON and XML columns

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

Secondary indexes

yes

with tags

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

SQL-like query language

APIs and other access methods

JDBC
Native API

RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C#
Java
JavaScript
Python

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

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

Server-side scripts

Stored procedures

yes

PL/SQL

also stored procedures in Java possible

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

Implicit feature of the cloud service

Sharding, horizontal partitioning

Sharding

consistent hashing

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

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

can be realized in PL/SQL

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

isolation level can be parameterized

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

In-memory capabilities

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

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

yes

Transient mode

User concepts

Access control

yes

using Azure authentication

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

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