DBMS > Apache IoTDB vs. openGauss

System Properties Comparison Apache IoTDB vs. openGauss

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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

An enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by Huawei

Primary database model

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.08
Rank	#183	Overall
	#84	Relational DBMS

Website

iotdb.apache.org

gitee.com/opengauss
opengauss.org

Technical documentation

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

docs.opengauss.org/en
gitee.com/opengauss/docs

Developer

Apache Software Foundation

Huawei and openGauss community

Initial release

2018

2019

Current release

1.1.0, April 2023

3.0, March 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

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, C++, Java

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

ANSI SQL 2011

APIs and other access methods

JDBC
Native API

JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C++
Java

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)

horizontal partitioning (by range, list and 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

Source-replica replication

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

User concepts

Access control

yes

Access rights for users, groups and roles according to SQL-standard

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More resources

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