DBMS > Apache IoTDB vs. Cubrid vs. IBM Db2 warehouse vs. Splice Machine vs. YDB

System Properties Comparison Apache IoTDB vs. Cubrid vs. IBM Db2 warehouse vs. Splice Machine vs. YDB

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

Cloud-based data warehousing service

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Time Series DBMS

Relational DBMS

Document store
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	1.30
Rank	#164	Overall
	#75	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.35
Rank	#278	Overall
	#43	Document stores
	#127	Relational DBMS

Website

iotdb.apache.org

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

www.ibm.com/products/db2/warehouse

splicemachine.com

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

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

cubrid.org/manuals

splicemachine.com/how-it-works

ydb.tech/en/docs

Developer

Apache Software Foundation

CUBRID Corporation, CUBRID Foundation

IBM

Splice Machine

Yandex

Initial release

2018

2008

2014

2019

Current release

1.1.0, April 2023

11.0, January 2021

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

Apache 2.0; commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C, C++, Java

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Windows

hosted

Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

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.

Import/export of XML data possible

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language (YQL)

APIs and other access methods

JDBC
Native API

ADO.NET
JDBC
ODBC
OLE DB

.NET Client API
JDBC
ODBC
OLE DB

JDBC
Native Spark Datasource
ODBC

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

yes

Java Stored Procedures

PL/SQL, SQL PL

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

Sharding

Shared Nothhing Auto-Sharding, Columnar 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

Source-replica replication

yes

Multi-source replication
Source-replica replication

Active-passive shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Yes, via Full Spark Integration

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

yes, multi-version concurrency control (MVCC)

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

fine grained access rights according to SQL-standard

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

Access rights defined for Yandex Cloud users

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