DBMS > Apache IoTDB vs. ITTIA vs. Ultipa

System Properties Comparison Apache IoTDB vs. ITTIA vs. Ultipa

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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

Edge Time Series DBMS with Real-Time Stream Processing

High performance Graph DBMS supporting HTAP high availability cluster deployment

Primary database model

Time Series DBMS

Graph DBMS

Secondary database models

Relational DBMS

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	0.36
Rank	#272	Overall
	#22	Time Series DBMS

Score	0.11
Rank	#338	Overall
	#32	Graph DBMS

Website

iotdb.apache.org

www.ittia.com

www.ultipa.com

Technical documentation

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

www.ultipa.com/document

Developer

Apache Software Foundation

ITTIA L.L.C.

Ultipa

Initial release

2018

2007

2019

Current release

1.1.0, April 2023

8.7

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C and C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Windows

Data scheme

yes

Fixed schema

Typing

predefined data types such as float or date

yes

Float/Blob/Integer/String/Unicode/Date/Time/Timestamp/Interval

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

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

JDBC
ODBC
RESTful HTTP API
WebSocket

RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C++
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

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

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

Database file passwords

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