DBMS > Apache IoTDB vs. Cubrid vs. JanusGraph vs. MySQL vs. SAP HANA

System Properties Comparison Apache IoTDB vs. Cubrid vs. JanusGraph vs. MySQL vs. SAP HANA

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

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Widely used open source RDBMS

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	1.04
Rank	#187	Overall
	#87	Relational DBMS

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

iotdb.apache.org

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

janusgraph.org

www.mysql.com

www.sap.com/products/hana.html

Technical documentation

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

Developer

Apache Software Foundation

CUBRID Corporation, CUBRID Foundation

Linux Foundation; originally developed as Titan by Aurelius

Oracle

since 2010, originally MySQL AB, then Sun

SAP

Initial release

2018

2008

2017

1995

2010

Current release

1.1.0, April 2023

11.0, January 2021

0.6.3, February 2023

8.4.0, April 2024

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

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Implementation language

Java

C, C++, Java

Java

C and C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Windows

Linux
OS X
Unix
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Appliance or cloud-service

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

yes

with proprietary extensions

yes

APIs and other access methods

JDBC
Native API

ADO.NET
JDBC
ODBC
OLE DB

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

Clojure
Java
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

Java Stored Procedures

yes

proprietary syntax

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes

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

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

User authentification and security via Rexster Graph Server

Users with fine-grained authorization concept

no user groups or roles

yes

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