DBMS > Apache IoTDB vs. Cubrid vs. GBase vs. IBM Db2 Event Store vs. SQLite

System Properties Comparison Apache IoTDB vs. Cubrid vs. GBase vs. IBM Db2 Event Store vs. SQLite

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

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

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

Distributed Event Store optimized for Internet of Things use cases

Widely used embeddable, in-process RDBMS

Primary database model

Time Series DBMS

Relational DBMS

Event Store
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	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

iotdb.apache.org

cubrid.com (korean)
cubrid.org (english)

www.gbase.cn

www.ibm.com/products/db2-event-store

www.sqlite.org

Technical documentation

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

cubrid.org/manuals

www.ibm.com/docs/en/db2-event-store

www.sqlite.org/docs.html

Developer

Apache Software Foundation

CUBRID Corporation, CUBRID Foundation

General Data Technology Co., Ltd.

IBM

Dwayne Richard Hipp

Initial release

2018

2008

2004

2017

2000

Current release

1.1.0, April 2023

11.0, January 2021

GBase 8a, GBase 8s, GBase 8c

2.0

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

free developer edition available

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

Java

C, C++, Java

C, Java, Python

C and C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Windows

Linux

Linux, macOS, Windows for the developer addition

server-less

Data scheme

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

yes

Standard with numerous extensions

yes

through the embedded Spark runtime

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
Native API

ADO.NET
JDBC
ODBC
OLE DB

ADO.NET
C API
JDBC
ODBC

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

Java Stored Procedures

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

none

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

yes

Active-active shard replication

none

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

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

No - written data is immutable

yes

via file-system locks

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

fine grained access rights according to SQL-standard

yes

fine grained access rights according to SQL-standard

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