DBMS > Apache IoTDB vs. Cubrid vs. mSQL vs. Snowflake vs. Sphinx

System Properties Comparison Apache IoTDB vs. Cubrid vs. mSQL vs. Snowflake vs. Sphinx

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

mSQL (Mini SQL) is a simple and lightweight RDBMS

Cloud-based data warehousing service for structured and semi-structured data

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Time Series DBMS

Relational DBMS

Search engine

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	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

iotdb.apache.org

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

hughestech.com.au/products/msql

www.snowflake.com

sphinxsearch.com

Technical documentation

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

cubrid.org/manuals

docs.snowflake.net/manuals/index.html

sphinxsearch.com/docs

Developer

Apache Software Foundation

CUBRID Corporation, CUBRID Foundation

Hughes Technologies

Snowflake Computing Inc.

Sphinx Technologies Inc.

Initial release

2018

2008

1994

2014

2001

Current release

1.1.0, April 2023

11.0, January 2021

4.4, October 2021

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

free licenses can be provided

commercial

Open Source

GPL version 2, commercial licence 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

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

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

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

yes

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
Native API

ADO.NET
JDBC
ODBC
OLE DB

JDBC
ODBC

CLI Client
JDBC
ODBC

Proprietary protocol

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

C
C++
Delphi
Java
Perl
PHP
Tcl

JavaScript (Node.js)
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

Java Stored Procedures

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

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

none

yes

Sharding

Partitioning is done manually, search queries against distributed index is supported

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

none

yes

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

none

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

The original contents of fields are not stored in the Sphinx index.

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

Users with fine-grained authorization concept, user roles and pluggable authentication

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.