DBMS > Apache IoTDB vs. GBase vs. Hive

System Properties Comparison Apache IoTDB vs. GBase vs. Hive

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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

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Primary database model

Time Series DBMS

Relational DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Website

iotdb.apache.org

www.gbase.cn

hive.apache.org

Technical documentation

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

cwiki.apache.org/confluence/display/Hive/Home

Developer

Apache Software Foundation

General Data Technology Co., Ltd.

Apache Software Foundation

initially developed by Facebook

Initial release

2018

2004

2012

Current release

1.1.0, April 2023

GBase 8a, GBase 8s, GBase 8c

3.1.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2

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, Java, Python

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

All OS with a Java VM

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

Standard with numerous extensions

SQL-like DML and DDL statements

APIs and other access methods

JDBC
Native API

ADO.NET
C API
JDBC
ODBC

JDBC
ODBC
Thrift

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions

yes

user defined functions and integration of map-reduce

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) and vertical partitioning

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

yes

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Eventual 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

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